Optimizing Firm Performance ||

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Herausgegeben von/edited byRoland Berger School of Strategy and Economics –Academic Network,München

Christian Faden

Optimizing Firm Performance

Alignment of Operational Success Drivers on the Basis of Empirical Data

Christian FadenUniversität HohenheimStuttgart, Germany

Dissertation Universität Hohenheim, 2013

D 100

ISBN 978-3-658-02745-2 ISBN 978-3-658-02746-9 (eBook)DOI 10.1007/978-3-658-02746-9

Th e Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografi e; detailed bibliographic data are available in the Internet at http://dnb.d-nb.de.

Library of Congress Control Number: 2013944913

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V

I would like to express my gratitude to my doctoral supervisor Prof. Dr. Dirk Hachmeister for

his great support during my research. Further I thank my employer, in particular Oliver

Knapp and Thomas Rinn as well as all people who contributed to this study. For their

patience and encouragement I would like to thank my lovely wife and son. I appreciate very

much the numerous advises my brother has given to me. Lastly, I thank my parents for their

everlasting support in all my pursuits which is why I dedicate this thesis to them.

VII

Contents

Contents .............................................................................................................. VII�

Table of figures .................................................................................................... XI�

List of abbreviations ......................................................................................... XIII�

1� Introduction and purpose ............................................................................... 1�

2� Working capital management: a review of performance

measurement and its drivers ........................................................................... 3�

2.1� Introduction ............................................................................................... 3�

2.2� Introduction to the concept of working capital ......................................... 5�

2.2.1� Evaluation of the working capital concept over time ...................... 5�

2.2.2� Static and dynamic measurements ................................................... 9�

2.3� Theoretical basis: Configurational Theory .............................................. 17�

2.4� Data and methodology............................................................................. 20�

2.5� Review of the literature ........................................................................... 25�

2.5.1� Working capital management and firm performance .................... 25�

2.5.2� Identified drivers of working capital performance ........................ 31�

2.5.2.1� Production-related variables ............................................ 31�

2.5.2.2� Company characteristics ................................................. 35�

2.5.2.3� Competitive position ....................................................... 41�

2.5.2.4� Industry factors ................................................................ 44�

2.5.3� Related research topics interfacing with working capital

management ................................................................................... 45�

2.5.3.1� A literature review of supply chain risk .......................... 45�

2.5.3.1.1� Supply chain risk drivers versus risk sources ............. 45�

2.5.3.1.2� Risk drivers and risk source items .............................. 51�

2.5.3.2� A literature review of manufacturing performance ......... 59�

2.5.3.3� A literature review of supply chain performance ............ 68�

VIII

2.6� Discussion ............................................................................................... 73�

2.7� Conclusion ............................................................................................... 76�

3� Driving firm performance based on an integrated operations

approach consisting of manufacturing, supply chain management,

working capital management and supply chain risk steering ................... 78�

3.1� Relevance ................................................................................................ 78�

3.2� Research model and hypotheses .............................................................. 81�

3.2.1� Manufacturing performance........................................................... 81�

3.2.2� Supply chain performance ............................................................. 83�

3.2.3� Working capital management ........................................................ 84�

3.2.4� Supply chain risk............................................................................ 85�

3.3� Methodology ........................................................................................... 87�

3.3.1� Data collection and descriptive statistics ....................................... 87�

3.3.2� Questionnaire design and measurement items ............................... 90�

3.3.2.1� Questionnaire design ....................................................... 90�

3.3.2.2� Measurement items ......................................................... 91�

3.3.3� Construct reliability and validity ................................................... 93�

3.4� Results of the structural equation model ............................................... 102�

3.5� Discussion ............................................................................................. 104�

3.6� Conclusion ............................................................................................. 109

IX

4� Boosting firm performance: working capital management & supply

risk chain steering as drivers ...................................................................... 111�

4.1� Competitive pressure on operations ...................................................... 111�

4.2� What are the operational drivers for your company? ............................ 114�

4.3� Supply chain risk steering – a tool to boost firm performance ............. 120�

4.4� Four consecutive steps to a successful supply chain risk steering ........ 128�

4.5� Managerial implications ........................................................................ 139�

5� Coverage of the analysis .............................................................................. 141�

6� Summary ....................................................................................................... 143�

Appendix 1: Questionnaire of the empirical survey ............................... 145�

Appendix 2: Participants expert interviews............................................. 151�

Bibliography ....................................................................................................... 153�

XI

Table of figures

Table 1: Number of publications over time ........................................................... 22�

Table 2: Publications in top journals over time ..................................................... 23�

Table 3: Number of publications per word stem ................................................... 24�

Table 4: Working capital performance and firm performance .............................. 26�

Table 5: Indicators of working capital performance ............................................. 33�

Table 6: Literature review of supply chain risk management ............................... 53�

Table 7: Literature review of manufacturing performance ................................... 61�

Table 8: Literature review of supply chain performance ...................................... 69�

Table 9: Breakdown of participants by industry sector ......................................... 88�

Table 10: Firm size, respondent's job title and function ........................................ 89�

Table 11: Average NTC per industry cluster......................................................... 92�

Table 12: Rotated component matrix .................................................................... 95�

Table 13: Component transformation matrix ........................................................ 96�

Table 14: Evaluation of reflective constructs ........................................................ 97�

Table 15: Descriptive statistics and variable correlations ................................... 101�

Table 16: Direct effects of the structural equation model ................................... 103�

Table 17: Coefficients of determination .............................................................. 104

Figure 1: Illustration of the cash conversion c#ycle .............................................. 15�

Figure 2: Conceptual model .................................................................................. 80�

Figure 3: Results of the structural equation model .............................................. 103�

Figure 4: Drivers of supply chain risk ................................................................. 113�

Figure 5: Participants experience years, industry cluster and firm size .............. 115�

Figure 6: Participating companies in the study ................................................... 115�

Figure 7: Study results correlations of constructs ............................................... 116�

Figure 8: The changing focus of management attention over time ..................... 120�

Figure 9: The supply chain risk trade-off ............................................................ 121�

Figure 10: Strategic operational options .............................................................. 122�

Figure 11: Average performance for each strategic option ................................. 123�

Figure 12: Strategic working capital management .............................................. 125�

Figure 13: Derivation of supply chain risk policy ............................................... 130�

Figure 14: Three organizational options for supply chain risk steering .............. 131�

XII

Figure 15: Link between supply chain risk steering and company processes ..... 134�

Figure 16: Integrated approach for supply chain risk steering ............................ 134�

Figure 17: Definition of risk sources and drivers ................................................ 136�

Figure 18: Defining the targeted level of supply chain risk – Example .............. 138�

Figure 19: Steering of operations – Example: supplier default risk .................... 138�

XIII

List of abbreviations

AP ……...................... Accounts payables

AR ……..................... Accounts receivables

AVG …….................. Average

AVE …….................. Average variance extracted

BSE ……................... Bovine Spongiform Encephalopathy

C ……........................ Cash

CAPEX …................. Capital expenditure

CCC …….................. Cash conversion cycle

C2C ……................... Cash-to-cash

CEO …….................. Chief executive officer

CFA ……................... Confirmatory factor analysis

CFI …….................... Comparative fit index

CFO …….................. Chief financial officer

CFROI ….................. Cash flow return on investment

COGS .…................... Cost of goods sold

CxO ……................... C-level executive

DAX …….................. A stock index that represents 30 of the largest and

most liquid German companies that trade on the

Frankfurt Exchange

dm ………................. dm-drogerie markt GmbH & Co. KG

EBSCO …................. Elton B. Stephens Company

ECVI ……................. Expected cross-validation index

EFA ……................... Exploratory factor analysis

EVA™ …….................. Economic value added

EU ..……................... European Union

GDP …….................. Gross domestic product

GFI …….................... Goodness of fit index

HTC …….................. High Tech Computer Company

IFI ..……................... Incremental fit index

XIV

INV …….................... Inventories

IR ……….................. Indicator reliability

ISE …….................... Istanbul Stock Exchange

ISO …….................... International Organization for Standardization

KonTraG .................. Gesetz zur Kontrolle und Transparenz im

Unternehmensbereich

KPI …….................... Key performance indicator

NA ……..................... Net accruals

n.a. ……..................... Not available

NCP ……................... Non-centrality parameter

NWC ……................. Net working capital

NLB ……................... Net liquid balance

NTC …….................. Net trade cycle

OEM ..…................... Original equipment manufacturer

OPEX .…................... Operational expenditure

…..……................... Level of significance

PACAP ..................... Pacific-Basin Capital Markets Research Center

PC ...……................... Personal computer

PIMS ..…................... Profit Impact of Market Strategy

RMR ..…................... Root mean square residual

RMSEA .................... Root mean square error of approximation

R² ………................... Coefficient of determination

R&D …….................. Research and development

ROA …….................. Return on assets

ROE …….................. Return on equity

SARS ..…................... Severe Acute Respiratory Syndrome

SE ...……................... Standard error

SME …….................. Small and medium sized enterprises

STB ……................... Short-term borrowings

TQM ……................. Total quality management

UK ……..................... United Kingdom

XV

U.S. ………................ United States

USA ……................... United States of America

USD ……................... United States Dollar

WCCC ….................. Weighted cash conversion cycle

WCM ……................ Working capital management

WCR ……................. Working capital requirement

1

1 Introduction and purpose

Entrepreneurial action should always seek to increase the value of the firm in either the short

or the long term. One could argue forever about the best way to measure corporate success.

No-one, however, questions that innumerable factors of influence drive this success,

irrespective of how they are ultimately measured.1 This interconnected system of relevant

factors seems to be growing even more sophisticated as companies and whole economies

becoming increasingly integrated. Yet despite this rising complexity in manufacturing

companies – the focus of this study – performance of operations are always expected to play a

key role in determining success or failure.2

As a consequence defining targets in an operations context is an ambiguous game. It is not

just about being the most effective, as firms also have to maximize their efficiency. This is

true especially for Western-based companies, given that companies based in emerging

markets today operate as their equals in terms of quality, flexibility and delivery.3 So

operations finds itself having to foster sales by maintaining reasonable levels of quality,

flexibility and delivery while not losing sight of the cost base. To resolve this dilemma, a

detailed understanding of the main interdependencies within operations, but also at the

interfaces to other disciplines, is essential. Managers must be aware of the interrelationships

between a firm's key operational drivers and how they affect corporate performance. The

expectation is that a firm will only be able to maximize its value if management knows

exactly how the operational cogs interlock with each other – and if they have aligned

operations accordingly. Astonishingly, even the pioneers struggle to master this balancing act.

Toyota, the inventor of lean management, has suffered from recalls and quality problems,

while experts even question whether its legendary manufacturing model is at fault.4 Apple, the

dethroned master of controlling the entire supply chain with supposedly maximum

transparency, has struggled recently as e.g. its main supplier Foxconn reported serious

industrial accidents, leading to a bad press.5 In the wake of the tragic earthquake in Japan in

March 2011, Apple's supply chain in general then faced serious problems.6 These examples

1 Hansen/Wernerfelt (1989), p. 400. 2 Cole (2011), p. 33. 3 Hitt/Dacin/Levitas/Arregle/Borza (2000), p. 452. 4 Cole (2011), p. 29. 5 Plambeck/Lee/Yatsko (2012), p. 43. 6 Sternberg (26 May 2011).

C. Faden, Optimizing Firm Performance, Schriften zum europäischen Management,DOI 10.1007/978-3-658-02746-9_1, © Springer Fachmedien Wiesbaden 2014

2

emphasize the considerable relevance of this topic to practitioners, as firms constantly need to

enhance their operational alignment even if they view themselves in a pioneering role.

Provided its relevance academic literature has identified and operationalized constructs that

are expected to drive operations effectiveness and efficiency in theoretical discourses and

empirical studies. As a result, four drivers that directly affect a firm's physical production,

supply or distribution of goods have been identified: manufacturing performance, supply

chain performance, working capital requirements and the inherent supply chain risk. To the

knowledge of the author nobody so far has evaluated the interdependencies between these

operational drivers, analyzed their impact on firm performance and generated suggestions as

to how they might be optimized. This study contributes to the literature by providing an

integrated evaluation of the different operational performance drivers followed by an

empirical analysis which aims to verify postulated hypothesis on existing correlations.

Thereto the objective is to identify and validate key operational drivers of firm performance

and to examine the relationship between the identified operational drivers and their impact on

working capital and firm performance. Subsequently, results will be evaluated for their

practical relevance by giving precise recommendations on how to align operations in order to

maximize firm performance.

The thesis is structured as follows: Chapter two presents a comprehensive review of the

literature. Chapter three integrates the findings from chapter two into a comprehensive model

and presents the results from the empirical study. The implications of statistical results in

terms of the approaches needed to align operations are discussed subsequently in chapter four.

Need for further research is discussed in chapter five followed by a summary of the thesis in

chapter six.

3

2 Working capital management: a review of performance measurement

and its drivers

2.1 Introduction

Researchers in the field of corporate finance have traditionally focused on long-term financial

decisions. Pioneering and widely accepted research has been conducted in areas such as

investment decisions7, firms' capital structure and business valuations.8 At the same time,

however, academic literature has neglected the management of current assets and liabilities, a

concept subsumed under the term working capital management (WCM). There are various

reasons why WCM attracts only peripheral interest. First, working capital decisions occur

frequently in the course of the daily business routine. Second, despite the fact that 37% of all

funds invested by manufacturing corporations in the U.S. were tied up in short-term assets,

the individual impact of working capital decisions is only marginal.9 Third, short-term

financial decisions are typically reversible over time.10 Notwithstanding, the number of

publications and new concepts in this area has lately increased as many managers focus on

working capital as a way to access liquidity.

The purpose of this literature review is to describe working capital concepts, to outline

existing WCM performance measurement concepts and to identify value drivers that have

been identified, analyzed, and tested. One fundamental change has taken place with regard to

performance measurement: WCM is no longer seen as a discipline whose principal aim is to

maintain sufficient liquidity in the event of liquidation. Rather, its purpose is now to underpin

a company's operating cycle.11 Both views and the corresponding measurements are

elaborated herein. The shift toward a focus on the operating cycle has highlighted WCM as a

key success factor for a firm's profitability. This thesis has been substantiated in numerous

empirical studies. Since the positive impact of WCM on firm profitability has been

substantiated in various empirical studies, several researchers have recently investigated

potential drivers of WCM performance that potentially boost firm performance. Based on data

available in the public sectors, a large quantity of drivers, including operational cash flow,

firm size and sales growth, have been identified and tested. Up to now, however, three related

7 Trossmann/Werkmeister (2001), p. 1-235. 8 Nazir/Afza (2009), p. 19. 9 Hill/Sartoris (1988), p. 9. 10 Gentry/Mehta/Bhattacharyya/Cobbaut/Scaringella (1979), p. 28,

Richards/Laughlin (1980), p. 32. 11 Fess (1966), p. 266.


4

operational research areas – manufacturing performance, supply chain performance and

supply chain risk – have, to the knowledge of the author, not yet been included in these

considerations. Nor were these research areas ever integrated to form additional value drivers,

despite the fact that researchers in these areas confirmed a genuine link to WCM. Schonberger

states that one fundamental objective of world class manufacturing performance is to "cut

flow time, flow distance, inventory, and space along the chain of customers".12 However,

research in the field of manufacturing performance and financial performance is almost solely

limited to product cost modeling.13 Regarding supply chain risk, Jüttner notes that "[..] supply

chain control mechanisms like decision rules and policies regarding order quantities, batch

sizes and safety stocks can either amplify or absorb risk effects".14 Scherr (1989) adds that

"one of the major features of this world is uncertainty (risk), and it is this feature that gives

rise to many of the strategies involving working capital accounts".15 These remarks give clear

indication that a genuine link of the research areas manufacturing performance, supply chain

performance, supply chain risk and working capital management prevails – Providing strong

support to further investigate in their correlation.

First, the concept of working capital is being introduced in chapter 2.2. The theoretical basis

selected for further modeling is the Configurational Theory, which is presented and linked to

WCM in section 2.3. Section 2.4 explains how the sample literature was determined and what

eligibility criteria were applied. In section 2.5. the literature on WCM, manufacturing

performance, and supply chain risk is reviewed to provide a comprehensive summary of the

existing literature. Existing literature on the correlation between WCM and both profitability

and the underlying value drivers is shown in sections 2.5.1 to 2.5.2. A review of the related

research areas supply chain risk, manufacturing performance and supply chain performance is

provided in section 2.5.3. A summary and discussion of the results follows. The review ends

with a discussion of the limitations of the methodologies applied and suggestions for potential

future research directions.

12 Schonberger (1990), p. 296. 13 Malik/Sullivan (1995), p. 171. 14 Jüttner (2005), p. 123. 15 Scherr (1989), p. 3.

5

2.2 Introduction to the concept of working capital

2.2.1 Evaluation of the working capital concept over time

The concept of working capital was originally to ensure that obligations could be met in case

the firm went into liquidation. Holding sufficient short-term assets guaranteed that the firm

would be able to satisfy short-term creditors in the event of liquidation. Thus, the main

objective was to control business in a way that short-term assets matched short-term

liabilities.16 In practice, a one-year period was used to distinguish between the short and long

terms.

In the mid-20th century, the focus shifted towards a going-concern view of the firm. By

consequence, the immediate liquidation of the firm was no longer of concern. This strategic

shift in the basic view of the firm had material consequences for the concept of working

capital. Since then, the new paradigm of working capital management has been to maintain

the firm's operating cycle while seeking to maximize its profitability. The operating cycle

consists of the whole sequence of cash flows generated by the physical activities of the firm's

operations.17 To illustrate a metaphorical expression might be helpful: Working capital

management has to keep a certain level of water in the bathtub bearing in mind current and

future water inflows and outflows. The purpose of keeping water in the bathtub is to serve as

a buffer. Since the market is exposed to inefficiencies such as transaction costs, information

costs, scheduling costs, production limitations, etc., firms must maintain a short-term asset

buffer to accommodate existing uncertainties. In a perfect world, operations would be

accurately predictable. Inflows and outflows of raw materials, goods or cash could thus be

anticipated with such precision that buffers in terms of inventory or cash holdings would be

superfluous. However, in a world of machine outages, late payments and order changes,

inventories, cash holdings and receivables are indispensable.18 Given these imponderables, a

firm that is shy of sufficient liquid reserves may need to delay payments, obtain temporary

financing on potentially unfavorable terms or even sell assets.19 To avoid such costly actions

that, if the worst comes to the worst, can have serious repercussions, reserves are maintained

that can be liquidated at sight. As a consequence, the main reasons for maintaining positive

working capital are unexpected events that could affect inflows or outflows of cash, raw

16 Fess (1966), p. 266. 17 Hill/Sartoris (1988), p. 7. 18 Scherr (1989), p. 2-3. 19 Emery (1984), p. 25.

6

materials, or goods.20 Maintaining a buffer of short-term assets to safeguard the operating

cycle is only one side of the coin, however. Since short-term assets are usually the firm's least

profitable assets, the goal is to keep them as low as possible. Low net working capital serves

in this context to boost firm performance and, ultimately, increases shareholder value.21 As

such, working capital management is expected to resolve the trade-off between maintaining

the operating cycle and keeping the firm profitable. The key to successfully mastering this

trade-off is to use advanced planning systems both to predict inflows and outflows and to

assess the quality and robustness of these results. The design of financial and non-financial

supply chains, however, also has a significant impact on the way in which disruptions affect

the operating cycle.

Osisioma (1997) defines proper working capital management as "the regulation, adjustment,

and control of the balance of current assets and current liabilities of a firm such that maturing

obligations are met, and the fixed assets are properly serviced".22 In line with this definition,

desirable quantities of each component of working capital must be maintained for

management purposes. Identifying the best possible capital structure is clearly a challenging

task that is discussed in several academic papers.23 An overview of different state-of-the-art

approaches in working capital management is presented by Smith.24 An empirical study by

Sarantis demonstrates that a significant negative financial risk effect on borrowing and fixed

investment has been shown by modeling of inter-related demand equations in the UK demand

sector.25 Contrary to the theorem introduced by Modigliani and Miller (1958), this provides

evidence that an optimal capital structure does in fact exist.26 In all neoclassical studies of

investment behavior, the influence of financial policy on the investment is ignored.27

However, it is obvious that the assumptions drawn in neoclassical theory do not hold under

any reasonable assumptions about the market.28 It therefore seems perfectly reasonable to

discuss strategies to optimize working capital. Excess working capital does not earn the cost

of capital. As a consequence, determining the optimal amount of WC maximizes shareholder

value.

20 Gentry/Mehta/Bhattacharyya/Cobbaut/Scaringella (1979), p. 29. 21 Scherr (1989), p. 4 f.; Hachmeister (1997b), p. 826-827. 22 cit. op. Appuhami (2008), p. 10. 23 Ball/Brown (1969), p. 300. 24 Smith (1973), p. 50-55. 25 Sarantis (1980), p. 393. 26 Modigliani/Miller (1958), p. 261-297; Myers (1984). p. 575. 27 Sarantis (1980), p. 393. 28 Borch (1969), p. 1.

7

One mature area of research is the financial planning and the evaluation of the optimal cash

level.29 Insights into the dynamic optimization of cash balances are provided by Eppen and

Fama (1968, 1969).30 Their optimization model focuses on the opportunity cost of cash

balances, penalty costs for negative cash balances and transaction costs. Optimal cash

management policies are likewise analyzed by Kallberg et al. (1982) and Kim et al. (1998),

who focus on uncertainty.31 Emery et al. (1982) model a firm's liquidity as a Wiener process

to determine the likelihood of insolvency and provide a relative measure of liquidity. In their

model, they used the mean and variance of net cash flow per unit of time, the initial liquid

reserve, and the length of the period in units of time.32 A study of the link between the capital

structure of the firm and ruin is provided by Borch (1969).33 In their deliberations, Bierman et

al. (1975) include the contingency of ruin, but enlarge the model by defining earnings as a

function of the level of working capital.34 In the Bierman et al. (1975) model the level of

inventories and credit policies has an effect on sales figures. Contrary to these optimization

models, Knight (1972) advocates applying a simulation approach to summarize major policy

alternatives. In his view, a traditional optimization model is not appropriate, since one basic

assumption of the optimization model does not hold in this case. In practice, working capital

decisions involve multiple objectives with numerous interdependencies, all of which are

subject to uncertainties. However, optimization requires a single objective equation and an

algorithm or systematic mathematical procedure to find a unique solution. Knight (1972)

therefore proposes the simulation approach as a way to determine a satisfactory solution by

running sensitivity and variance analyses.35

Evaluating the above models on how to define an adequate short-term capital structure raises

the question whether certain other major factors of influence must also be taken into account.

Besides factors such as ruin, uncertainty and the cost of holding cash, which we have already

seen, it seems to be obvious that other internal and external key drivers of an optimal working

capital structure also exist. With regard to internal factors, one would assume that a general

correlation between short-term assets and fixed investments exists. The condition of fixed

assets such as production equipment certainly has an impact on required working capital

29 Trossmann (1990), p. 27-36. 30 Eppen/Fama (1968), p. 94; Eppen/Fama (1969), p. 119. 31 Kallberg/White/Ziemba (1982), p. 670-682; Kim/Mauer/Sherman (1998), p. 335. 32 Emery/Cogger (1982), p. 290-303. 33 Borch (1969), p. 1-13. 34 Bierman/Chopra/Thomas (1975), p. 199-200. 35 Knight (1972), p. 33.

8

levels. Firm characteristics such as size, growth rates, the products offered, or industry-

specific criteria are likewise postulated to affect requirements for short-term assets.36 In

addition, external factors such as upstream and downstream supply chain characteristics and

the habits of suppliers and customers can also drive working capital requirements. All in all,

the assumption is that numerous factors actually influence working capital requirements.

Recently, a small number of empirical studies have investigated the potential drivers of

working capital requirements. Literature also provides comprehensive studies of

manufacturing performance, supply chain risk and supply chain performance as separate

research areas. These papers on the drivers of working capital nevertheless fail to link

working capital management to corporate performance. Instead, they focus on identifying

significant drivers of reduced or increased working capital requirements. Given that working

capital management should not only safeguard the operating cycle but also focus on

profitability, this link is worthy of further investigation. Supporting this proposition is that the

dependency between working capital levels and corporate performance has indeed been

tested in numerous papers on the cash conversion cycle – albeit without addressing the issue

of performance drivers.37

An exhaustive review of existing papers that establish a link between working capital levels

and corporate performance, that test working capital drivers in and that explore the related

research areas manufacturing performance, supply chain risk, and supply chain performance

is provided in section 2.5, including a summary of the methodologies applied and the results

achieved. The section that follows begins by outlining existing measures of working capital

management.

36 Horrigan (1965), p. 564-565. 37 Kamath (1989), p. 27; Soenen (1993), p. 55-57; Jose/Lancaster/Stevens (1996), p. 33; Shin/Soenen (1998), p.

39-43; Wang (2002), p. 168; Deloof (2003), p. 585; Eljelly (2004), p. 59; Lazaridis/Tryfonidis (2006), p. 34.

9

2.2.2 Static and dynamic measurements

At the end of the nineteenth century, financial ratios with which to analyze financial

statements were developed in the United States.38 The main purpose was to devise tools that

would enable short-term borrowing capacity to be judged. By the end of the 1920s, financial

ratios had already become a common analytical device for many institutions and analysts.39

Traditional liquidity ratios of relevance to the financial position of the firm include the current

ratio, the quick ratio and net working capital.40 One modified version is the ratio of net

working capital to current liabilities or total assets.41 The current ratio is calculated by

dividing current assets by current liabilities. Subtracting inventory from current assets and

dividing the result by current liabilities defines the quick ratio.42 Net working capital is

defined as the net current assets position or the excess of current assets over current

liabilities.43 The ratio can be calculated by adding marketable securities, accounts receivable,

and inventories and subtracting short-term borrowings, accounts payable, and short-term net

accruals.44 The traditional aim of these ratios is to serve as a liquidity indicator by matching

short-term assets to short-term liabilities. One major deficiency of these traditional liquidity

ratios, however, is "that they incorporate assets that are not readily convertible into cash and

exclude the liquidity provided by potential sources of financing".45 This deficiency is

conditional on whether one is seeking to cover the possibility of liquidation or is focused on

the going-concern value of the firm. It may indeed be impossible for a firm to quickly convert

inventory or receivables to cash at the reported book values. From the going-concern

perspective, a further shortcoming is apparent: Besides wanting to evaluate whether liquid

assets cover short-term liabilities, an external observer also wants to distinguish between

changes in the operating cycle and changes due to the financial strategy of the firm.46

However, ratios such as net working capital do not allow for this distinction, since balance

sheet items are mainly affected by the operating cycle and those affected by financial triggers

are typically reported as aggregates. According to Shulman et al. (1965), these ratios "[..]

disregard the effect of operating cycle changes on corporate liquidity and the impact of capital

38 Horrigan (1965), p. 558. 39 Horrigan (1965), p. 558. 40 Bernstein (1993), p. 491. 41 Emery (1984), p. 26. 42 Kamath (1989), p. 28. 43 Sagan (1955), p. 121. 44 Hawawini/Viallet/Vora (1986), p. 15. 45 Emery (1984), p. 26. 46 Kaiser/Young (2009), p. 70.

10

changes on the operating cycle".47 A company could thus decide to increase cash reserves

based on planned investments and, in so doing, significantly change its reported liquidity

ratios. Yet external observers would not be able to judge whether the increase results from

changes in the operating cycle or from an adjusted financial strategy. Neither can analysts be

sure whether the increase has affected liquid items on the balance sheet, as it could just as

well be attributable to relatively illiquid positions such as receivables or inventory. It follows

that inter-firm or inter-period comparisons are of limited value to the stakeholder, since the

restructuring of asset categories is not transparent.48

To be able to distinguish between the operating cycle and changes in liquid assets Shulman et

al. (1985) refined the net working capital (NWC) ratio by repackaging and redefining the

elements into two separate components, working capital requirements (WCR) and the net

liquid balance (NLB).49 Subject to this alteration, WCR represents the operating cycle

accounts and NWC the liquidity accounts.

47 Shulman/Cox (1985), p. 64. 48 Richards/Laughlin (1980), p. 33. 49 Shulman/Cox (1985), p. 64.

11

The total of WCR plus NLB equals NWC:

��

where WCR is defined as: ��

With:

� Accounts receivable

�� Inventories

� Accounts payable

� Net accruals

and NLB is defined as:

��

With:

� Cash

�� Short-term borrowings

The reclassification of NWC as WCR and the NLB ensures a strict distinction between assets

that are genuinely attributable to the operating cycle and decision variables that must be

assigned to treasury management and strategy. The components of WCR represent all aspects

of the operating cycle: procurement, production, and sales. In other words, WCR is the

amount of money that is tied up in the operating cycle of a company. Most companies have

positive WCR, indicating a conservative approach to working capital management. If WCR is

positive, the excess amount must be funded by either free cash flow or debt. Aggressive

working capital management may lead to negative WCR. In such cases, the operating cycle

serves as a source of funding for other assets. Cash and short-term borrowings as components

of the NLB are seen as financial decision variables with no strict correlation to operations.

Cash levels and liquid securities can be adjusted with no direct impact on the operations of the

firm.50 Since the NLB comprises only of highly liquid assets, it serves as a measure of the

50 Hawawini/Viallet/Vora (1986), p. 15.

12

firm's liquidity. If operational requirements change, the NLB is usually affected. An increase

in accounts receivable or inventory is then accompanied by a decrease in the NLB to bridge

the gap.51 A decrease in the NLB can, however, be avoided if the company is able to

successfully boost accounts payable at the same time. The concepts of WCR and the NLB

have consequently been picked up by numerous authors. Especially in empirical studies,

researchers such as Hill et al. (2010), Appuhami (2008), and Chiou et al. (2006) have used the

classification introduced by Shulman et al. (1985) to measure working capital.52

The concept introduced by Shulman et al. (1985) may remedy the disadvantage of obscuring

operational and financial views of net working capital as KPI. One fundamental drawback

nevertheless remains: WCR and the NLB are static measurements that do not permit estimates

of the future pattern or size of working capital. The power of a liquidity indicator to anticipate

future prospects is key, if one views a firm under the premise of going concern.53

Accordingly, many authors have questioned whether these ratios are indeed suitable tools

with which to evaluate working capital management.54 Emery (1984) gets to the heart of this

criticism, noting that "[…] when these ratios are calculated from standard accounting

statements, they may simply indicate the adequacy of the firm's liquidity reserves for the

immediate past period rather than for some relevant future period".55 As a consequence, "an

examination of conventional, static balance sheet liquidity ratios indicates the inherent

potential for misinterpreting a firm's relative liquidity position."56 Based on these findings, the

flow concept of liquidity began to edge out static concepts. In a seminal paper, Gitman (1974)

presents a concept for calculating the minimum cash balance required by a firm by looking at

the total cash cycle.57 Gitman (1974) defines the total cash cycle as the number of days from

the time the firm pays for its purchases of raw materials to the time the firm collects revenues

from the sale of its finished products. Although Gitman (1974) himself later revised his initial

approach, it was taken up, annotated and used for empirical studies by numerous other

researchers.

51 Shulman/Cox (1985), p. 65. 52 Hill/Kelly/Highfield (2010), p. 784; Appuhami (2008), p. 8; Chiou/Cheng/Wu (2006), p. 149. 53 Jose/Lancaster/Stevens (1996), p. 34; Bernstein (1993), p. 492. 54 Hager (1976), p. 20-21. 55 Emery (1984), p. 27. 56 Richards/Laughlin (1980), p. 36. 57 Gitman (1974), p. 82.

13

The concept of static ratios can be converted into a dynamic flow concept by setting the

relevant balance sheet positions in relation to the corresponding income statement items.58 By

definition, the new measure takes into account that the appraisal of working capital accounts

depends on operational performance in the form of procurement, production, and sales.

Calculating turnover instead of using static ratios empowers analysts to evaluate the working

capital account in relation to the underlying activity. The proportionality of resources used to

performance achieved can thus be analyzed. The turnover in accounts receivable is the ratio of

accounts receivable to sales. This measure indicates how fast a company's receivables are

converted into cash on average. A company that generates high sales and has comparatively

low accounts receivable is no doubt in a better position than a company that runs up relatively

high accounts receivable. Changes in a company's operating policy are reflected in this

measure, as looser credit policy or less effort to collect receivables will raise the accounts

receivable turnover ratio.59 If a company increases sales and at the same time keeps its

accounts receivable constant, the improved situation can be evaluated by calculating the

turnover ratio. However, relying on accounts receivable as a static ratio in the same situation

would leave the improvement in performance invisible. Inventory turnover indicates the

frequency with which companies turn over their raw materials inventory, work in progress,

and finished goods. The ratio is calculated by dividing the total average amount of inventory

by the cost of goods sold. Unlike to the accounts receivable turnover, the cost of goods sold is

the denominator in this case. The reasoning is that profit margins, the cost of sales and

financing costs would falsify the interpretation of inventory turnover. If sales were taken as

the denominator, a company with above-average profit margins or non-representative

financing costs could reduce turnover without improving actual inventory performance. The

most appropriate basis is therefore the costs of goods sold, as this reflects the production

value of semi-finished and finished goods as they enter the warehouse. However, one should

not forget that a main driver of inventories is sales, which reflects the level of demand and

thus implies a positive relation to the required inventory.60 High inventory turnover implies

that a company can get by with little inventory as it maintains production and sales. The fact

that the company's inventory turns over rapidly implies that on average each item in the

warehouse stays in stock only for a short time on average. High turnover ratios keep

58 Jose/Lancaster/Stevens (1996), p. 34. 59 Bernstein (1993), p. 493. 60 Bernstein (1993), p. 493.

14

warehouse efficiency high, as costs per item turned over are relatively low. Changes in

operations, such as an increase in the quantity of defective goods or a reduction in sales

figures, will naturally impact the measure. Likewise, the quality of the turnover ratio

outperforms the static one. If a company manages to increase production leading to an

increase in the cost of goods sold while keeping inventory at the same level, efficiency would

increase. However, this improvement would again not be shown in a static ratio, as the total

inventory volume would remain unchanged. To complete the operating cycle, a firm's

liabilities must be considered when evaluating its liquidity position. Here the relevant

turnover ratio is the ratio of accounts payable to purchases. This ratio depicts the average rate

at which the company pays for purchased services or goods. Longer payment terms improve a

company's liquidity position, as delayed payment effectively translates into financing by the

supplier. The resultant improvement in the financial position can then be used to finance

assets.

The cash conversion cycle (CCC)61 represents in this context an approach that combines the

individual turnover ratios discussed above to form a single measure representing the entire

operating cycle. The CCC reflects "the net time interval between actual cash expenditures on

a firm's purchase of productive resources and the ultimate recovery of cash receipts from

product sales, establishes the period of time required to convert a dollar of cash disbursement

back into a dollar of cash inflow from a firm's regular course of operations".62 Studies on the

subject utilize numerous definitions of the CCC, ranging from general statements to detailed

specifications.63

Notwithstanding, the detailed definition of the CCC is commonly accepted as:64

��

��

��

�

��

�� !��!

�

��"��

�� !��!

61 Other authors used the term "cash-to-cash", "C2C", "cash gap" or "operating cash cycle" as synonyms. Please

see Farris II/Hutchison (2002), Farris II/Hutchison (2003), Farris II/Hutchinson/Hasty (2005), Boer (1999), Churchill/Mullins (2001). For a detailed list of commonly used terms, see Uyar (2009), p. 187.

62 Richards/Laughlin (1980), p. 34. 63 Farris II/Hutchison (2002), p. 289. 64 Keown/Martin/Petty (2011), p. 418.

15

The CCC depicts the average net time interval in days between the moment a firm has to pay

for its purchases and the time the cash receipts are collected (figure 1).65

Figure 1: Illustration of the cash conversion cycle66

The figure for the CCC can be interpreted as the average number of days for which a

company has to finance operating cycle requirements either through debt or equity.67 Most

companies have a positive CCC, indicating the need for financing for the operating cycle. The

CCC may turn negative, however. In this case, accounts receivable are received prior to cash

outflows to settle accounts payable.68 In such a favorable situation, the company relies heavily

on the financing by the supplier.69 A low CCC indicates that a company manages its cash

flows efficiently, as it generates more sales per unit of invested capital.70 As the signs

themselves indicate, an increase in net accounts receivable or the inventory ratio leads to a

longer CCC. This is then accompanied by a higher financial requirement for the operating

cycle, as assets must be financed over a longer period. Conversely, higher accounts payables

turnover indicates greater financial leeway due to the spontaneous granting of credit by

creditors, as long as no penalty is charged for late payment. If an increase in net accounts

receivable or inventory cannot be offset by accounts payable, the firm will require additional

liquidity to finance the increased capital that is now tied up in the operating cycle. Firms that

manage to boost sales figures usually face increased financing requirements, as fixed assets

65 Wang (2002), p. 160. 66 Richards/Laughlin (1980), p. 35. 67 Moss/Stine (1993), p. 25; Churchill/Mullins (2001), p. 137. 68 Hutchison/Farris II/Fleischman (2009), p. 43. 69 Farris II/Hutchison (2003), p. 83. 70 Hutchison/Farris II/Anders (2007), p. 42.

Inventory period

Cash conversion cycle

Accounts receivable period

Accounts payable period

Raw materials

purchased

Payment of

raw materials

Sale of

finished

goods

Cash collection

on sales

Financing

needed

Means of

financing

16

tend to increase during the operating cycle.71 Attempts to optimize the bottom line of the CCC

must always give due consideration to all relevant aspects. Turning one "wheel" will always

affect all the other components of the cycle.72 Efforts to improve CCC efficiency ultimately

lead to reduced costs and, by consequence, to higher profitability.73 Thus, the CCC is a

powerful tool to manage operations.74 A longer CCC requires not only more capital but also

entails the risk that a company may not be as agile in volatile economic times. Necessary

shifts in production, sales, etc. take more time, which can be a serious competitive

disadvantage. As a consequence, the CCC also serves as an indicator for the valuation of a

company: a shorter CCC results in a higher net present value of operating cash flows.75

Ensuring the availability of sufficient capital is a stiff challenge for treasury managers. It is

not only that the overall level of capital requirements must be maintained throughout the

entire operating cycle. In addition, working capital investment flows are, by their very nature,

asynchronous.76 Permanent liquidity management is imperative if a company is to be able to

handle volatile operating cash flows. Indeed, several studies indicate that the day-to-day

management of working capital takes up most of financial managers’ time.77

Gentry et al. (1990) further developed the CCC concept.78 Their starting point was that the

initial concept neglects the fact that the amount of funds tied up in each component of the

CCC is not reflected in the calculation. By calculating weights for each component according

to their relative share, a weighted cash conversion cycle (WCCC) can be determined. The

result reflects both the number of days for which capital is tied up in each component of the

cycle and the amount of funds thus tied. Although the concept is more advanced than its

predecessor, in practice imponderables exist as external analysts struggle to determine the

required data for calculating the proper weights.79

As a consequence, Bernstein (1993) modeled a measure on the CCC termed the net trade

cycle (NTC).80 The difference to the CCC is that sales is used as the denominator for all

terms. Bernstein (1993) argues that his concept improves the uniformity and simplicity of the

71 Soenen (1993), p. 54. 72 Hager (1976), p. 19. 73 Hutchison/Farris II/Fleischman (2009), p. 42. 74 Farris II/Hutchison (2002), p. 297. 75 Farris II/Hutchinson/Hasty (2005), p. 114. 76 Richards/Laughlin (1980), p. 34. 77 Hill/Sartoris (1988), p. 1. 78 Gentry/Vaidyanathan/Lee Hei Wai (1990), p. 90-99. 79 Shin/Soenen (1998), p. 38. 80 Bernstein (1993), p. 510.

17

calculation at the cost of an acceptable lack of stringency. Indeed, some of the financial data

that is needed to calculate the CCC may not be available, in which case the NTC might be a

practical alternative.81 Empirical studies confirm that the CCC and the NTC provide similar

information.82

All in all, CCC provides a valid alternative to earlier static measures of corporate liquidity

management. Many authors emphasize the benefits of this dynamic flow concept, arguing that

it is more practical and can be used to replace or supplement liquidity ratios.83

2.3 Theoretical basis: Configurational Theory

The theoretical basis for this dissertation is the Configurational Theory. Integrated, discussed

and scrutinized by numerous scholars, the Configurational Theory is an offshoot of the

Contingency Theory that dominated research until the 1970s.84 In both theories, the concept

of "fit" in strategic management is the underlying assumption.85 The concept postulates that

the performance of an organization depends on the fit of environment and organizational

design.86 Environment, as one of these two dimensions, has been subdivided into external,

internal and integrated environments in the relevant literature. In line with this classification,

researchers to date have focused on one of these clusters.87 While the concept of fit is the

cornerstone of both the Contingency Theory and the Configurational Theory, each theory

emphasizes different aspects.88 The Contingency Theory demonstrates that "attributes of

environments, technologies, and structures interact to restrict the range of viable

organizational forms".89 Configurational theorists go a step further: Not only should a limited

set of structural concepts such as centralization be considered, but provision should also be

made for abstract situational concepts such as size and technological uncertainty. There is no

question that the two concepts are linked: The Configurational Theory builds on the

Contingency Theory. For this reason, the two concepts are discussed below in the context of

Configurational Theory and working capital management.

81 Shin/Soenen (1998), p. 38. 82 Kamath (1989), p. 26. 83 Kamath (1989), p. 28; Eljelly (2004), p. 50; Hutchison/Farris II/Anders (2007), p. 42. 84 Smith/Shortell/Saxberg (1979), p. 669. 85 Venkatraman/Camillus (1984), p. 513. 86 Shortell (1977), p. 275. 87 Venkatraman/Camillus (1984), p. 516. 88 Vorhies/Morgan (2003), p. 111; Drazin/van de Ven (1985), p. 521. 89 Meyer/Tsui/Hinings (1993), p. 1177.

18

The basic assumption behind contingency models is that the best performance can be

achieved when organizational structures match external contingency factors. In other words,

"contingency models posit that effectiveness is highest where the structure fits the

contingencies. Match causes effectiveness, mismatch causes ineffectiveness."90 Only those

organizations that align their organization with the current environment achieve maximum

output. Strategically significant external variables to be considered include economic

conditions, demographic trends, sociocultural trends political/legal factors and industry

structure variables.91 Given that external factors may change rapidly, managers must

constantly adopt their organizations to the new situation to ensure effectiveness. To guide

practitioners and test the theory empirically, scholars have developed hypothetical best-fit

organizational patterns for different environmental forms.92 However, the use of patterns that

assume linear relationships has also been criticized by researchers.93 In terms of empirical

testing, authors have found evidence that the contingency theory is a powerful approach to

explaining structural change.94

The Contingency Theory "implicitly treats organizations as loosely coupled aggregates whose

separate components may be adjusted or fine-tuned […]".95 Conversely, "configurational

inquiry represents a holistic stance, an assertion that the parts of a social entity take their

meaning from the whole and cannot be understood in isolation".96 Configurational theorists

thus emphasize the alignment of different design parameters in the organization and its

environmental context. As with previous Contingency Theory assumptions, the hypothesis is

that the match between organizational design parameters and context variables will posit

greater effectiveness and efficiency for organizations.97 At the same time, internal

organizational design parameters such as work specifications, reward/incentive systems and

coordination systems must likewise be brought into line. The Configurational Theory was

initially developed by Shortell (1977), who introduced an approach that lists different context

variables and internal design forms. Based on the variables defined, he named four model

relationships and predicted either high or low efficiency and effectiveness.98 However, this

90 Donaldson (1982), p. 67. 91 Hofer (1975), p. 798. 92 Drazin/van de Ven (1985), p. 524-525. 93 Schoonhoven (1981), p. 370. 94 Donaldson (1987), p. 22. 95 Meyer/Tsui/Hinings (1993), p. 1177. 96 Meyer/Tsui/Hinings (1993), p. 1178. 97 Doty/Glick/Huber (1993), p. 1196. 98 Shortell (1977), p. 288.

19

general hypothesis is not applicable to all situations. For this reason, scholars went on to

identify clusters of organization types that perform best in certain business situations in order

to prepare recommended courses of action.99 As a result, certain characteristics of

organizational parameters can be recommended for specific business situations. The empirical

relevance of the theory has been tested by a variety of scholars. The majority of researchers

have found evidence for at least the partial validity of this hypothesis in practice.100

With regard to working capital management, the Configurational Theory claims that available

parameters have to be set according to the contextual variables of the firm, such as the

economic situation, industry structure, supplier variables, and demand behavior. However, it

is not enough merely to align the contextual variables with working capital parameters. To

maximize overall organizational efficiency and effectiveness, working capital parameters

themselves must be aligned with the other relevant organization parameters. Given that, in

accordance with the Configurational Theory, organizational performance depends primarily

on the interplay between the different parameters. This being the case, relevant internal

drivers of working capital performance must be identified and optimized with an integrated

approach to match the requirements of the contextual variables. Only if these conditions are

fulfilled overall firm performance will reach its maximum, as postulated by the

Configurational Theory. In the underlying case correlations for the identified drivers,

manufacturing, and supply chain performance must therefore be tested for correlation. Based

on a firm's contextual variables, such as supply chain risk, an integrated optimization model

must simultaneously be tested in light of identified correlations and the firm's environment.

This implies that a firms’ strategy in terms of e.g. business model, global footprint, and

product portfolio must consider intra firm correlations and contextual variables such as supply

chain set up. Based on the Configurational theory there exist only one distinctive

configuration of manufacturing performance, supply chain performance, working capital

levels, and the firms supply chain risk level that maximizes a firms performance. As such the

basic question emerge how do the different drivers correlate with each other and finally what

is the configuration that maximizes a firms performance. Nonetheless the existing literature

99 Ketchen Jr./Combs/Russell/Shook/Dean/Runge/Lohrke/Naumann/Haptonstahl/Baker/

Beckstein/Handler/Honig/Lamoureux (1997), p. 225; Smith/Shortell/Saxberg (1979), p. 682; Doty/Glick/Huber (1993), p. 1196.

100 Vorhies/Morgan (2003), p. 111.

20

has to the knowledge of the author so far not tested for a model that simultaneously considers

correlations of described variables and their impact on firm performance. There is high

relevance to close this existing gap in the literature via an empirical study which allows for an

evaluation of existing correlations and their individual strengths. Based on the empirically

validated relations a model is developed to provide practical guidelines on how to align

business processes to achieve maximum firm performance.

2.4 Data and methodology

The main purpose of the eligibility criteria applied to the literature review – to ensure

substantive and empirical relevance – was achieved by doing three things.101 First, appropriate

word stems were defined to enable a comprehensive literature search to be conducted. The

word stems defined can be grouped into four categories: working capital management,

manufacturing performance research, supply chain risk studies and supply chain performance

scholars. Defined search criteria for the first category were "working capital", "cash

conversion cycle", and "cash-to-cash" in order to cover static and dynamic concepts. The

word stem "manufacturing performance" was selected for the second research field. "Supply

chain risk" and "supply chain vulnerability" were intended to identify existing research in

category three. Following the same logic, the word stem "supply chain performance" was

selected to cover category four. The electronic library EBSCO (Business Source Premier) was

selected as the database, as it covers more than 1,800 peer-reviewed journals in the field of

economics and is constantly updated. Broad coverage and proven usefulness in other studies

were the main reasons for this selection.102 The time frame chosen for the search was 1970 to

2010. To limit the results, only scholarly peer-reviewed journals were included. When the

searches were launched, Boolean operators were used to search for the word stems in both

titles and abstracts. The exact word stem phrase was searched for using quotation marks. All

in all, the search returned 1,167 hits. The keyword "working capital" yielded most of these

hits (697). "Manufacturing performance" delivered 293 hits, "supply chain risk" 77, "supply

chain performance" 70, "cash conversion cycle" 14, "supply chain vulnerability" 12 and

"cash-to-cash" 4 hits. While these results were spread across a total of 344 journals, the top 30

journals accounted for approximately 50% of all hits. The top five journals were the 101 Ennen/Richter (2010), p. 211-212; Meyer/Dalal/Hermida (2010), p. 131-132;

Randolph (2009), p. 4; Newbert (2007), p. 124-126. 102 Ennen/Richter (2010), p. 211.

21

Accounting Review (75 hits), the International Journal of Operations & Production

Management (45 hits), the Journal of Accountancy (44 hits), the International Journal of

Production Research (43 hits) and the International Journal of Production Economics (38). An

analysis of the publications over time suggests that interest has steadily increased since the

beginning of the nineteenth century. The amount of published articles has indeed increased in

every decade since 1900 (see table 1).

From the 1990s to the first decade of this century, the number of publications actually more

than doubled from 220 to 494. As shown in table 2, analysis of the number of publications in

the top journals over time indicates that research into financial management peaked between

the 1960s and the 1970s. Conversely, research into production and manufacturing was only

just beginning in the 1990s and has not yet peaked. Reference lists of the main papers

identified for the various research dimensions formed the second pillar of the literature

research. This approach prevented the author from overlooking crucial publications due to the

fact that certain keywords were not mentioned in the title or abstract. Reviewing the reference

lists of main papers helped to identify 22 additional papers of relevance to the object of this

research. Third, members of the academic network in related fields were contacted and asked

for publications that had recently been submitted or were in the process of being published in

non-mainstream journals. Discussions with these contacts revealed a further 4 relevant articles

that had not yet been included in the sample. In total, the search for relevant publications

produced a long list of 1,193 papers for review. The word stems defined for the database

search covered a large quantity of papers. This ensured that all potentially relevant papers

appeared on a long list and could be reviewed for genuine relevance. To focus the literature

review more sharply, three steps were taken to narrow down the volume of papers in scope.

The first step was to consolidate the results based on the different sources used and the

elimination of duplicates.

22

Table 1: Number of publications over time

Year Number of publications Percentage of total

1910 1 0

1920 4 0

1930 13 1

1940 15 1

1950 24 2

1960 59 5

1970 103 9

1980 149 13

1990 220 19

2000 494 42

2010 85 7

Total 1.167 100

Second, all papers in the academic research areas working capital management,

manufacturing performance and supply chain performance were screened and those that did

not contain empirical studies were deselected. A search was made for papers that empirically

tested indicators of working capital performance, or that linked working capital performance

to firm performance. Papers that contained the word stem "manufacturing performance" were

screened for empirical studies that tested indicators for manufacturing performance. The same

procedure was repeated for the category supply chain performance. In addition, papers based

on small-scale samples or case studies were excluded. As the number of papers covering

supply chain risk research is rather limited, all papers that specifically defined either risk

drivers or risk source items were selected irrespective of whether or not they conducted

empirical tests.

23

Table 2: Publications in top journals over time

Year

Number of publications Percentage of total

Accounting Review 1920 2 1

1930 5 2

1940 8 3

1950 15 6

1960 25 10

1970 3 1

1980 7 3

1990 4 2

2000 6 2

International Journal of Operations & Production Management

1980 4 2

1990 15 6

2000 23 9

2010 2 1

Journal of Accountancy 1960 5 2

1970 14 6

1980 11 5

1990 9 4

2000 3 1

2010 2 1

International Journal of Production Research 1990 18 7

2000 22 9

2010 3 1

International Journal of Production Economics 1990 12 5

2000 17 7

2010 9 4

Total 244 100

Third, only those papers that contained research into the manufacturing industry were taken

into account. Papers focusing on services such as banking were excluded. Deselection was

conducted as follows: First, substantive but irrelevant papers were excluded based on a

reading of the title and selective reading of the article. Second, all remaining papers were

screened based on a full reading of all abstracts. Third, all papers that passed the first two

steps were read in their entirety to further validate their relevance.

As a result, 92 papers met all the defined criteria (table 3). The section that follows analyzes,

evaluates and cross-references all papers that matched the defined search parameter. In the

24

interests of clarity, the review of the selected papers was structured in line with the object of

investigation. In the initial scope of investigation, all papers were collected that seek to

analyze correlations between working capital management and firm performance. The

independent variable in the correlation analysis can be either static or dynamic. In the second

category, all papers were summarized that investigate drivers of working capital performance.

As in the previous category, the dependent variable too can be static or dynamic. In the third

to fifth categories, all papers were clustered that contain research into either manufacturing

performance, supply chain risk or supply chain performance.

Table 3: Number of publications per word stem

Search word stem Number of publications Percentage of total

Manufacturing performance 35 38

Working capital 15 17

Supply chain risk 15 17

Cash conversion cycle 13 14

Supply chain performance 13 13

Supply chain vulnerability 1 1

Total 92 100

Accordingly, section 2.4 is structured as follows: Existing research into the correlation

between working capital performance and firm performance is presented first (section 2.4.1).

All studies covering indicators for the performance of working capital are outlined next

(section 2.4.2). Lastly, the related research fields manufacturing performance, supply chain

risk and supply chain performance are processed in the third section (section 2.4.3).

25

2.5 Review of the literature

2.5.1 Working capital management and firm performance

To the author's knowledge, a total of thirteen papers investigated the relation between

working capital management and firm performance (table 4).

The first paper was published in 1989 and written by Ravindra Kamath (1989).103 However,

most of the papers were published in the first decade of this century, with the two most recent

ones appearing in 2009. Although these papers all adopt a fairly uniform approach, their

geographical focus varies significantly. Japan, Taiwan, Belgium, Saudi Arabia, Greece,

Mauritius, Pakistan and Turkey were all selected as sample countries, alongside the U.S. The

sample size differs as a result: The lowest sample size covered 29 companies in Saudi Arabia,

the highest 2,718 in the United States. The average sample size was 825 companies. The

database was chosen as a function of the focus country in each case. Investigations in the

United States used the Compustat database. Other investigations used local databases, such as

the PACAP database for the financial data of Japanese companies. It is worth noting that all

the authors based their investigations on data available in the public domain, since the purpose

of all papers was to examine correlations between working capital measures and firm

performance KPIs. None of the authors used empirical surveys to collect the requisite data.

However, both the independent and dependent variables used and the statistical approaches

adopted to establish the described correlation are not uniform. The same holds for the results,

for which neither the methodologies used to establish positive or negative relationships nor

the quality of the models applied were as rigorous as they could have been. On the whole, the

papers nevertheless conclude that successful working capital management has a positive

impact on firm performance – a correlation that has been established in numerous papers by

statistical methods.

The first paper in this research area was published by Ravindra Kamath (1989).104 The

objective of Kamath's academic work was to compare and contrast traditional static liquidity

ratios with dynamic concepts such as the CCC. Moreover, she also investigated the

relationship between these liquidity measures and firm profitability.

103 Kamath (1989), p. 24-28. 104 Kamath (1989), p. 24-28.

26

Table 4: Working capital performance and firm performance a

a - significant negative correlation; + significant positive correlation; ? no significant correlation persistent

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27

For her statistical analysis, Kamath used a sample of 99 firms over a time frame from 1970 to

1984. Regarding the assumed superiority of dynamic measures over static ones, she argues

that the current ratio, quick ratio and CCC should be used concurrently, because considering

only one measure could lead to misleading clues. Comparing the CCC and NTC on the basis

of her data suggests that these KPIs provide the same information. The traditional liquidity

measures current ratio and quick ratio were not found to exhibit the assumed negative

correlation with firm performance, which is calculated as ��

��

. However, dynamic

ratios such as CCC and NTC did reveal the expected positive correlation in most cases. Over

the 15 sample years, CCC and firm performance exhibited a significant negative correlation in

five years, while NTC and firm performance did so in nine years, with 95 and 99 percent

significance levels respectively. For his investigation four years later, Luc A. Soenen (1993)

used the net trade cycle as the endogenous variable and the total return on assets, measured as

��

��

, as the exogenous variable.105 For the purposes of his analysis, he

classified all companies in the timeframe from 1970 to 1989 into four quadrants, depending

on whether the observed net trade cycle and total return on assets was above or below the

median value for that industry. In line with his hypothesis – the shorter the cash cycle, the

higher the profitability – Soenen anticipated a high concentration in the quadrants that

indicated a short cash cycle and a high total return on assets, and vice versa. Considering all

20 industries simultaneously (as only 55.4% of all observations matched up with his

hypothesis), he concluded that, although the cash cycle does have some influence on the total

return on assets, the correlation is not very strong. However, when analyzing the postulated

correlation by industry, he found significant evidence for the negative correlation in 18 out of

the 20 industries. In summary, Soenen concluded that a strong negative relationship exists

between the cash cycle and the total return on assets depending on the type of industry.

Following the same basic approach, Soenen (1993) published a further paper in collaboration

with Hyun-Han Shin five years later.106 Their database contained 58,985 firm years in the

period from 1975 to 1994. The results confirmed the authors' previous conclusions: The

statistics show a significant negative relationship between NTC and firm performance,

105 Soenen (1993), p. 53-58. 106 Shin/Soenen (1998), p. 37-45.

28

represented as �� !��

��

. Similarly, the correlation between the current ratio and firm

performance is significantly negative. The authors therefore conclude that reducing the net

trade cycle to a reasonable minimum increases shareholder value – a sound reason for

financial executives to focus on this topic.

Departing from NTC as the measure of working capital used by Soenen and Shin, a large

number of researchers in subsequent years investigated the relationship between CCC and

firm performance. To the author's knowledge, a total of seven papers have been published on

this subject. Manuel L. Jose, Carol Lancaster and Jerry L. Stevens (1996) started the ball

rolling by publishing their article "Corporate Returns and Cash Conversion Cycles".107 Their

main focus was to support the hypothesis that successful management of a firm's operating

cycle triggers superior firm performance. Arguing that in the past management focused

mainly on investment and financing decisions, the authors wanted to draw attention to the

day-to-day management of short-term assets and liabilities. In line with other empirical

research in the US, they used the Compustat database with a sample size of 2,718 companies.

The exogenous variables used in the model were the return on assets and the return on equity.

For both relationships – between CCC and ROA and between CCC and ROE – the statistics

show a significant negative correlation for all industries except financial services. R² is .0808

for the first regression and 0.1155 for the second. Introducing a control for size difference

increased the quality of the model: The negative relation is significant for all industries and

the R² measure rises to .3044 and .3145 respectively. These statistical findings are consistent

with previous publications. After this investigation in the US, other researchers applied this

approach to other countries too. The first person to do so was Yung-Jang Wang (2002), who,

in line with the approach adopted by Jose et al. (1996), investigated whether the latter's

conclusion could also be applied to the Taiwanese and Japanese markets.108 Based on their

sample of 1,934 companies, the statistical results again indicate a negative correlation

between both ROA and ROE and firm performance in the overall sample. However, the

expected relationship is not significant in all cases on an industry level. In the Japan sample,

the correlation is not significant for both firm performance measures in the petrochemicals,

electronics and transportation industries. For the Taiwan sample, the correlation is not

significant at the .90 level in the electronics, transportation and services industries.

Furthermore, R² in their regression model is significantly lower, at .0353 for Taiwan and 107 Jose/Lancaster/Stevens (1996), p. 33-46. 108 Wang (2002), p.159-169.

29

.0068 for Japan. In particular, the very low R² value for analysis on the Japanese market is

regarded very critically, suggesting that the explanatory power of the model is rather limited.

The first investigation in the European market was conducted by Marc Deloof (2003).109 He

analyzed a sample of 1,637 Belgian firms using almost the same variables. With regard to

firm profitability, he slightly modified the ROA and ROS ratios used hitherto, instead using

the KPI �" #��$%�&"�

��$ ��'��

. Similarly, he investigated a negative correlation with the

CCC. Like previous papers, he investigated not only the correlation between the aggregate

CCC and firm performance, but also each component in isolation. According to his

calculations, the number of accounts receivable, inventory and accounts payable days

correlate negatively to firm performance too. It is interesting that accounts payable correlate

negatively despite the fact that payables are presumed to reduce the cash gap. Deloof (2003)

argues that this finding, which appears contradictory at first glance, is the result of a

shortcoming in Pearson correlations, which do not allow causes to be distinguished from

consequences. A negative correlation is thus consistent with the view that highly profitable

firms usually afford their suppliers shorter payment periods, as they have the financial

resources to do so. According to Deloof (2003), profitability affects accounts payable days,

not vice versa.

In 2006, Lazaridis et al. (2006) investigated the postulated correlation for companies listed on

the Athens Stock Exchange.110 Here again, a negative correlation was established. The model

resulted in an adjusted R² of .238. The comparably high value of R² can be explained by the

fact that Lazaridis et al. (2006) included in their regression model additional variables such as

a fixed financial assets ratio, a financial debt ratio and a natural logarithm of sales indicating

the size of the firm. These additional variables boost R², whose value is higher than in

previous studies. Deloof and Lazaridis et al. (2006) both observed a negative correlation

between accounts payable and firm profitability, arguing in the same direction. In conclusion,

Lazaridis et al. (2006) advocate greater attention to working capital management and the

optimized handling of the various components of the CCC.

Contrary to the research conclusions presented above, Padachi et al. (2006) published a

positive correlation between CCC and ROA using a fixed asset model.111 Several specifics of

this case must nevertheless be considered when analyzing this result. First, a very small

109 Deloof (2003), p. 573-587. 110 Lazaridis/Tryfonidis (2006), p. 26-35. 111 Padachi (2006), p. 45-58.

30

sample of only 58 companies serves as basis for the statistics used. Second, a market with

unique conditions was chosen: Mauritius. Accordingly, Padachi et al. (2006) explain the

contradictory results mainly due to the small firm sizes. They assume that smaller firms

maintain a lower fixed asset base and rely mostly on current assets to increase profits. Also,

when a pooled OLS regression was used, the correlation turned negative. Notwithstanding,

the authors emphasize that there is a pressing need for further investigation, especially among

SMEs.

Similar correlations between CCC and firm profitability have been analyzed for Pakistan and

Turkey as well. Raheman et al. (2007) and Uyar (2009) respectively based their statistical

analysis on data for companies listed on the Karachi and Istanbul Stock Exchanges.112 The

results confirmed the negative correlation.

A different measure to analyze working capital performance was used by Eljelly (2004).113 He

focused on the links between both the current ratio and the cash gap and firm performance.

Firm performance is defined in his study as �� !��$(��

��

. Based on his

analysis of 29 Saudi Arabian companies, Eljelly was able to verify the negative correlation

between the current ratio and firm performance. However, the correlation between the cash

gap and firm performance was not significant. The explanatory power of the model has an R²

value of 0.163.

A unique approach has taken by Filbeck et al. (2007).114 Their research focused on whether

efficient working capital management has a positive impact on annual stock market closing

price returns and dividends. A database covering approximately 1,000 companies served as

the basis for their statistical analysis. According to these authors' results, a positive correlation

exists between efficient working capital management and the return to shareholders in terms

of annual stock market closing prices and dividends.

The most recent paper to focus on the effect of working capital management on firm

performance was published in 2009.115 Nazir et al. (2009) investigated the effect of variables

such as the asset structure or size of a company on firm performance, measured as the ROA.

In particular, the authors reviewed a data set containing 204 companies listed on the Karachi

Stock Exchange for six variables: current assets as a share of total assets, current liabilities as

112 Raheman/Nasr (2007), p. 279-300; Uyar (2009), p. 186-193. 113 Eljelly (2004), p. 48-61. 114 Filbeck/Krueger/Preece (2007), p. 18. 115 Nazir/Afza (2009), p. 19-30.

31

a share of total assets, size of firms, sales growth, the GDP growth rate and financial leverage.

They conclude that the variables current assets as a share of total assets, firm size and the

GDP growth rate correlate positively with the ROA. All the other variables correlate

negatively. Nazir et al. (2009) see evidence that the degree of aggressiveness of an investment

is negatively correlated to firm performance. Profitability increases as the ratio of current

assets to total assets increases. The same applies for the liabilities ratio: The higher the ratio of

current liabilities to total assets, the more aggressive the financing policy. According to the

results of the study, then, an aggressive financing policy yields a negative return on assets.

To summarize: Academic research confirms the initial hypothesis that successful working

capital management leads to increased profitability. Especially with regard to the correlations

based on the dynamic measures CCC and ROA, numerous papers statistically verify a

negative correlation. This finding gains even greater importance from the fact that the

investigations cover a large variety of industries and marketplaces. Given that the positive

impact of successful working capital management on firm performance thus appears to be

validated, the question of what specific factors affect working capital performance must now

be addressed. If firm performance is to be boosted by improving working capital

management, it is imperative to identify the precise drivers of working capital. The section

that follows presents a comprehensive overview of existing literature on this topic.

2.5.2 Identified drivers of working capital performance

2.5.2.1 Production-related variables

A summary of all papers that investigate drivers of working capital performance is presented

in table 5. Surprisingly, only one paper that matched the defined search criteria investigated

production-related drivers of working capital performance. The paper by Kenneth P. Nunn Jr.

(1981)116 – the first to analyze drivers of working capital performance in general – tested

several production characteristics for their significance. Based on a sample of 1,700

companies derived from a special database called the "PIMS database", Nunn published his

key findings in 1981, proving seven production-related drivers to have a significant influence

on working capital, which he defined as �)��*��+ ,#��+��-�

.��

. Nunn was able to

116 Nunn (1981), p. 207-219.

32

substantiate a positive correlation for the factors "% of small batch size production", "% order

backlog", "capital intensity" and "relative product line breadth".

The main explanation for these observations is to be found in the interplay of inventory and

production. Small batches, substantial order backlogs, capital intensity and product line

breadth all tend to increase inventory levels, which in turn increases working capital levels.

Small batches require a relatively longer work-in-process cycle that drives inventory

requirements. The same applies for order backlogs: Production at the limit of capacity leads to

increased work-in-process cycles as lead times grow longer. This also determines raw

material levels. Companies with capital-intensive production sites tend to maintain production

levels even during slack periods in order to cover the cost of capital. The effects of relative

product line breadth are obvious: The more product variants exist, the more raw materials and

finished goods inventory has to be maintained. Four drivers that negatively impact working

capital levels were identified: "% continuous process production", "% capacity utilization"

and "make-to-order products". Here again, the main explanations are to be found in the

relation to inventory. As a continuous process, production requires a short work-in-process

cycle that lowers inventory needs.

33

Table 5: Indicators of working capital performance a

# Year Author Area Sample sSource Endogenous variable(s) Exogenous variable(s) Correlation R²

1 1981 Nunn USA 1,700 PIMS data base • % Small batch production• % Continuous process production• % Capacity utilization• % Order backlog• Capital intensity• Make-to-order products• Relative product line breath• Media Advertising/Sales• Sales Force Expense/Sales• % Gross Margin - Channels• % Sales to Components• Accounting Method• Relative market share• Market share instability• Relative image• Relative price• Industry exports• Industry imports• Industry concentration

WC (accounts receivables + Inventory) / Sales

• +• -• -• +• +• -• +• -• +• +• -• +• -• +• -• +• +• +• +

.447

2 1986 Hawawini USA 1,181 Compustat • Industry WCR / Sales • Significant effect persistant

n.a.

3 1993 Fazzari, Petersen

USA 382 n.a. • Cash Flow/Fixed Capital • ∆ Working Capital/Fixed Capital

• + .280

4 1993 Moss USA n.a. Compustat • Total net Sales• Total assets• Cash Flow

• CCC • -• -• -

n.a.

5 1998 Kim, Mauer, Sherman

USA 915 Compustat • Size• Market value to book value• Variance of Cash Flow• Difference ROA to short term treasury bills• Cash Cycle• Variance of cash cycle• Leverage• Return on Sales

• Cash Holding (Cash + marketable securities to total assets)

• ?• +• ?• -• -• ?• -• -

.580

6 1999 Ricci USA 89 Mail survey • Granting credit• Obtaining Information• Setting Credit Limits• Monitoring Receivables• Reporting to Management

• Existence of Past Due Accounts

• ?• ?• -• -• -

n.a.

7 1999 Opler USA n.a. Compustat • Market-to-book ratio• Real size• Cash flow/assets• Net working capital/assets• Capital expenditures/assets• Total leverage• Industry sigma• R&D/sales

• +• -• +• -• +• -• +• +

.223

8 1999 Lancaster, Stevens

USA 417 Compustat • Income before Extraordinary Items/Working capital from Operations

• Current Ratio

• Quick Ratio

• CCC

• effect exists

• effect exists

• No effect

n.a.

9 2005 Filbeck, Krueger

USA 1,000 Research Inside®

• Industry influence

• Time influence

• Cash Conversion Consistency• Days of Working Capital• Days Sales Outstanding• Inventory Turnover• Days Payables Outstanding• Cash Conversion Consistency• Days of Working Capital• Days Sales Outstanding• Inventory Turnover• Days Payables Outstanding

• +• +• +• +• +• +• +• +• +• +

n.a.

34

Table 5: Indicators of working capital performance (continued)

a - significant negative correlation; + significant positive correlation; ? no significant correlation persistent

Companies facing high capacity utilization are able to realize economies of scale: While

work-in-process inventory can be kept fairly constant, sales increases in proportion to

capacity. A large proportion of make-to-order products ensures that the finished goods

inventory remains within reasonable limits, as such products can normally be shipped to

# Year Author Area Sample sSource Endogenous variable(s) Exogenous variable(s) Correlation R²

10 2006 Chiou, Cheng Taiwan 533 TEJ database • ∆ Business Cycle• ∆ Dept ratio• ∆ Operational Cash Flow/total assets• ∆ Sales growth• Age• ∆ Return on assets• ∆ Firm size• ∆ Business Cycle• ∆ Dept ratio• ∆ Operational Cash Flow• ∆ Sales growth• Age• ∆ Return on assets• ∆ Firm size

• Δ WCR/Total assets

• ∆ NLB/Total assets

• -• -• -• ?• +• -• +• -• -• +• +• -• -• -

.050

.054

11 2007 Garcia-Teruel, Martinez-Solano

Spain 11,533 n.a. • Financial solvency• Average maturity of assets• ∆ one year and six to ten year bonds• Depreciation/Total assets• log firms asset value• Total debt/shareholder equity

• Short term debt • +• -• +• -• -• -

.069

12 2008 Appuhami Thailand

416 Financial statements

• CAPEX• OPEX• Investment Expenditure• Operating cash flow• Dept ratio• Market to book ratio• Sales growth• CAPEX• OPEX• Investment Expenditure• Operating cash flow• Dept ratio• Market to book ratio• Sales growth

• NLB

• WCR

• +• +• -• +• -• ?• ?• -• +• +• -• +• ?• ?

.404

.304

13 2008 Garcia-Teruel, Martinez-Solano

Spain 860 SABE-database • Growth• Size• Short term bank debt• Probability of financial distress• Leverage• Debt Maturity• Cash Flow• Investment in liquid assets• Opportunity cost of cash holdings

• Cash Holding (Cash + marketable securities to total assets)

• +• -• -• ?• +• -• +• -?

n.a.

14 2010 Hill, Kelly, Highfield

USA 3,343 Compustat • ∆ sales growth• Gross profit margin• Sales volatility• Operating Cash Flow• Market-to-book ratio• Firm size• Market power• Financial distress

• WCR/Sales • -• ?• -• +• -• +• ?• -

.140

15 2010 Banos-Caballero, Garcia-Teruel, Martinez-Solano

Spain 4,076 SABE-database • Cash Flow• Debt/total assets• (Sales1-Sales0)/Sales0• log assets• Age• Tangible fixed assets/total assets• ROA

• CCC • -• -• ?• +• +• -• +

n.a.

35

customers immediately after completion. Although many of the drivers presented correlate

with working capital levels as anticipated, Nunn (1981) was able to substantiate these

correlations with statistical data. To the amazement of the present author, literature in this

research area is rather scarce. In their investigations to date, almost all academics have leaned

toward firm characteristics based on external KPIs as drivers of working capital requirements.

It is assumed that this is not due to the belief that research into production-related drivers is

irrelevant. The reason is rather to be found in the fact that little data on production-related

variables is readily available. Such data usually has to be collected in time-consuming

empirical surveys. On the other hand, firm characteristics such as the capital structure can

easily be retrieved from public databases.

2.5.2.2 Company characteristics

The different drivers with regard to company characteristics are separated into three groups:

performance indicators, capital structure-related drivers and others. An examination of the

relevant studies clearly shows that a multiplicity of endogenous and exogenous variables is

used. Some drivers of working capital management recur in different published papers. Over

time, however, new indicators have steadily been introduced. The same applies for measures

of working capital management: Not all studies use standard measures such as CCC, WCR or

the NLB. Such variations hamper the strict interpretation of published results. Published

articles cover every decade from the 1980s to the present. In terms of frequency, it is

conspicuous that most papers were published over the past decade, suggesting that the topic

has lately gained in significance. The geographic coverage of the studies is rather limited. By

far the majority of studies use data gleaned from U.S. companies. More recently, studies of

Spain, Thailand and the Taiwanese market have rounded off the existing picture. In-depth

studies of SMEs have been conducted for the Spanish market in particular. The goodness

achieved by the models applied varies as a function of the exogenous variables they use.

Given that values for R² range from .050 to .580, it would seem to be difficult to derive a clear

line from the figures presented.

Numerous authors have investigated correlations between two different performance

measures – profitability and of the ability to generate cash flow – and working capital

management. With regard to profitability, existing literature tends to confirm the finding of

the previous chapter: that successful working capital management drives performance and

36

vice versa. This finding is a consequence of the fact that regressions do not allow us to

distinguish between cause and effect. Switching dependent and independent variables leads to

the same result. A negative correlation between ROS and cash holdings is, for example,

presented in Kim et al. (1998)117, while negative correlations between ROA and WCR as well

as NLB are outlined by Chiou et al. (2006).118 For the correlation between performance and

working capital KPIs, see also Lancaster et al. (1998).119

The relationship of cash flow to working capital management has been investigated in many

studies. Different KPIs have been used: cash flow, operational cash flow, changes and

variances in cash flow and cash flow in relation to assets.120 Regarding the relationship

between operating cash flow and WCR, both Chiou et al. (2006) and Appuhami (2008)

confirm a significant negative correlation, indicating that a company's working capital

management becomes more efficient as cash flow rises:121 "Greater cash flow spawned by

operations activity implies better working capital management".122 This finding is in line with

the results of Moss (1993) and Baños-Caballero (2010), who identify a negative correlation

between cash flow and CCC.123 The conclusions are the same: Superior working capital

management results in higher cash flow, and vice versa. For the sake of completeness, it is

important to mention that Hill et al. (2010) and Fazzari et al. (1993) arrive at different

conclusions.124 According to their research, positive operating cash flow enables companies to

adopt a more conservative working capital strategy with a positive correlation. Statistical

analysis of the data for 3,343 companies in the US confirms their hypothesis. Existing

literature adopts a strict stance with regard to the correlation between cash flow and cash

holdings. Among others, Opler et al. (1999) and García-Teruel (2008) determine a positive

correlation in line with the initial hypothesis that financially strong companies hold relatively

more cash reserves.

117 Kim/Mauer/Sherman (1998), p. 354. 118 Chiou/Cheng/Wu (2006), p. 154. 119 Lancaster/Stevens/Jennings (1998), p. 37-46. 120 Fazzari/Petersen (1993), p. 335; Moss/Stine (1993), p. 27; Kim/Mauer/Sherman (1998), p. 354;

Opler/Pinkowitz/Stulz/Williamson (1999), p. 19; Chiou/Cheng/Wu (2006), p. 151; Appuhami (2008), p. 13; García-Teruel/Martínez-Solano (2008); p. 132; Hill/Kelly/Highfield (2010), p. 786; Baños-Caballero/García-Teruel/Martínez Solano (2010), p. 514.

121 Chiou/Cheng/Wu (2006), p. 154, Appuhami (2008), p. 16. 122 Appuhami (2008), p. 150. 123 Moss/Stine (1993), p. 33; Baños-Caballero/García-Teruel/Martínez Solano (2010), p. 523. 124 Hill/Kelly/Highfield (2010), p. 786; Fazzari/Petersen (1993), p. 336.

37

The second group of indicators for working capital management focuses on the capital

structure of the company. Again, numerous authors have contributed to this research field.

One frequently used indicator is the amount of total assets as a proxy of firm size.125 The

relationship between firm size and WCR has been investigated by Chiou et al. (2006) and Hill

et al. (2010), leading to the same conclusion.126 According to their findings, the more assets a

company has, the higher the WCR. This is because size is seen as a proxy for capital market

access. Smaller firms face greater challenges in seeking to finance a positive WCR. For this

reason, they more closely monitor operating working capital strategies. Following the

arguments for WCR, larger firms do not need to hold as much cash, as they have better access

to the capital market. Accordingly, larger firms are able to use the short-term debt market

according as and when the need arises. The relationship between size and cash holdings

follows this line of argumentation. Larger firms are not forced to hold significant liquidity, as

they have much greater flexibility in responding to demand for short-term liquidity.127

Smaller firms may experience severe difficulties if cash buffers are unable to satisfy

unexpected demand. Correlating total assets to CCC, Moss (1993) observes evidence that

larger firms are able to achieve lower CCC.128 Especially with regard to inventory and

accounts receivable, the study suggests that smaller firms have room to improve. In the study

conducted by Baños-Caballero et al. (2010), this variable reveals no significance – one

possible explanation being the study's focus on SMEs. Besides using total assets themselves

as a variable, several authors also use total asset ratios. Opler et al. (1999) analyze the

correlation between capital expenditure and total assets including cash holdings, revealing a

positive correlation.129 Their reasoning is that firms that are able to invest relatively more

usually also enjoy greater liquidity. Another study reveals that the ratio of depreciation to total

assets correlates negatively to short-term debt.130 Firms with less depreciation expenses

usually have fewer tangible assets but greater growth options. It is therefore expected that

these firms will use more short-term debt. Correlating the ratio of tangible fixed assets to total

assets with CCC produces a negative sign. The authors develop the hypothesis that fixed

125 Moss/Stine (1993), p. 29; Kim/Mauer/Sherman (1998), p. 354; Opler/Pinkowitz/Stulz/Williamson (1999),

p. 25; Chiou/Cheng/Wu (2006), p. 154; García-Teruel/Martínez-Solano (2008), p. 134; Hill/Kelly/Highfield (2010), p. 795.

126 Hill/Kelly/Highfield (2010), p. 795, Chiou/Cheng/Wu (2006), p. 154. 127 Opler/Pinkowitz/Stulz/Williamson (1999), p. 25; García-Teruel/

Martínez-Solano (2008), p. 134. 128 Moss/Stine (1993), p. 30. 129 Opler/Pinkowitz/Stulz/Williamson (1999), p. 25. 130 García-Teruel/Martínez-Solano (2007), p. 592.

38

assets compete for funds with levels of working capital when a company is operating under

financial constraints.131 García-Teruel et al. (2007) include an additional question in their

considerations. According to their findings, the average use of short-term debt is greater when

the average maturity of assets is shorter.132 The authors argue that this is consistent with the

usual practice of adapting asset liquidity to the time it takes to settle liabilities.

Another widely covered field is way in which leverage and working capital management

interact. Given that working capital positions are relatively liquid, they are a major focus

when financial distress situations occur. The existence of a correlation in the way financially

distressed companies tend to squeeze out working capital positions has been postulated,

whereas – according to the same hypothesis – other firms can afford to finance sufficient

levels. This initial hypothesis has been confirmed by many studies.133 The main line of

argumentation is that companies short of funds tend to raise capital internally before issuing

new stocks or borrowing money from outside, as this path is usually barred.134 It follows that

a rising debt ratio forces companies to pay more attention to working capital management.

Another explanation heading in the same direction is that indebted firms, whose financial

risks are greatest, try to control risk by lengthening the average maturity of their debt.135 For

this reason, highly leveraged firms try to increase maturity in order to avoid liquidation.

Interestingly, the study conducted by García-Teruel et al. (2007) comes to different results.

While their data shows a positive correlation, they themselves qualify their statement. On the

one hand, it is argued that, in light of their limited access to the capital markets, small firms

might prefer to maintain high cash levels rather than using cash to reduce their debt. On the

other hand, the significance of both this variable and the coefficients is low. The authors

therefore opt to give limited support to these findings.

Besides performance and capital structure indicators, scholars have also tested the empirical

relevance of several other items that reflect company characteristics with regard to working

capital performance. Nunn (1981), for instance, focuses on sales-related variables.136

Specifically, he investigates whether the ratios media advertising to sales, sales force 131 Baños-Caballero/García-Teruel/Martínez Solano (2010), p. 524. 132 García-Teruel/Martínez-Solano (2007), p. 592. 133 Kim/Mauer/Sherman (1998), p. 354; Opler/Pinkowitz/Stulz/Williamson (1999), p. 25;

Chiou/Cheng/Wu (2006), p. 154; García-Teruel/Martínez-Solano (2007), p. 591; Appuhami (2008), p. 16; Hill/Kelly/Highfield (2010), p. 795; Baños-Caballero/García-Teruel/Martínez Solano (2010), p. 523.

134 Chiou/Cheng/Wu (2006), p. 150. 135 García-Teruel/Martínez-Solano (2007), p. 593. 136 Nunn (1981), p. 211.

39

expenses to sales, the gross profit margin as a percentage and sales as a percentage of

components have a significant impact on working capital measures. The exogenous measure

used in his model is the ratio working capital divided by sales. According to Nunn's

calculation, the first and last variables presented have a significant negative impact and the

other two are revealed to have a significantly positive correlation. The significance level for

all variables is .999. According to Nunn (1981), media advertisement leads to a competitive

advantage in the market concerned, resulting in working capital-related economies. In

particular, it is expected that the use of credit and fast delivery as selling tools can be reduced.

The positive correlation between sales force expenses and working capital levels can be

explained as follows: The more powerful the sales force is, the more pressure will be exerted

on the finance and production functions to provide more generous credit terms and faster

shipment. The same applies for the third variable. A larger gross profit margin creates an

incentive to ship quickly and lowers barriers to the granting of credit. The variable percentage

of sales to subsidiaries correlates negatively to working capital, as finished products are

usually shipped on completion and accounts receivable are paid on time in line with company

guidelines. To the knowledge of the author, no empirical research other than that of Nunn has

yet investigated similar sales-related variables. Given that significant correlations in line with

initial hypotheses have been identified, this is astonishing. Another interesting variable used

to explain working capital success is the ratio R&D spend divided by sales. Opler et al. (1999)

analyze this effect and discover that the relative R&D spend has a significant positive impact

on cash holdings.137 They argue that this effect results from existing information asymmetries

that are expected to be higher at firms whose R&D spend is higher. According to the authors,

R&D expenses are a form of investment in which information asymmetries are of importance.

Another unique research question is tackled by Filbeck et al. (2005),138 who investigate

whether key working capital performance indicators change over time. According to their

sample of 1,000 companies from 1996 to 2000, the standard deviation of key performance

measures is comparatively low. For instance, average days working capital for the whole

timeframe were 51.8, with a standard deviation of 4.7 days. However, the authors do find

significant changes in working capital measures over time. These changes are explained

mainly by macroeconomic factors such as interest rates, the rate of innovation and

competition. As a factor of influence on working capital, age is analyzed in studies by Chiou

137 Opler/Pinkowitz/Stulz/Williamson (1999), p. 11. 138 Filbeck/Krueger (2005), p. 13

40

et al. (2006) and Baños-Caballero et al. (2010).139 It is argued that companies tend to achieve

higher growth rates in their early years. As management loosens the reins over time, working

capital management becomes less efficient. Another explanation is that older firms have better

access to external capital sources. Since they are able to obtain liquidity from the market,

these companies maintain CCC for longer because of the lower cost of financing.

A study conducted by Appuhami (2008) traces the impact of capital expenditure (CAPEX)

and operating expenditure (OPEX) on the net liquid balance and the working capital

requirement.140 Based on the results, CAPEX has a significant positive impact on NLB with a

coefficient of .531. The same applies for OPEX: The data shows a significant positive impact.

These findings are in line with the assumption, formulated earlier, that future growth

opportunities require a higher current cash balance and more short-term investment. Working

capital requirements exhibit a significant negative correlation to CAPEX, indicating that

companies tend to manage working capital more efficiently when investment is high.

Conversely, working capital requirements correlate positively to OPEX, indicating that

companies seem to hold more current assets when they have commitments to pay interest.

One final company characteristic that has attracted considerable attention from researchers, as

has its impact on working capital, is sales and sales growth. The first study that included sales

as a variable was conducted by Moss (1993).141 Like other scholars, Moss (1993) used sales

as an indicator of firm size. According to his results, there is a significant impact in that firms

with higher sales – i.e. larger firms – have shorter CCC cycles. Based on this outcome, Moss

concluded that smaller firms probably have the potential to shorten the CCC by applying

strategies to reduce inventories or receivables or both. Several subsequent studies also

investigated sales as a driver of working capital, but found no significant correlation.142 More

interestingly, García-Teruel et al. (2008) described a significant positive correlation between

sales growth and cash holdings in companies. According to their line of argument, firms with

growth opportunities should retain higher liquid reserves in order to be able to exploit these

opportunities.143 Another study by Hill et al. (2010) analyzed the correlation between sales

139 Chiou/Cheng/Wu (2006), p. 154;

Baños-Caballero/García-Teruel/Martínez Solano (2010), p. 522 140 Appuhami (2008), p. 13 141 Moss/Stine (1993), p. 33 142 E.g. Chiou/Cheng/Wu (2006), p. 154; Appuhami (2008), p. 16-18; Baños-Caballero/García-Teruel/Martínez

Solano (2010), p. 523. 143 García-Teruel/Martínez-Solano (2008), p. 141.

41

growth and WCR.144 Based on the data used, a significant negative correlation exists. The

main reason cited by Hill et al. (2010) is that companies tighten their credit policy as they

achieve planned levels of sales growth. In addition, the authors argue that prior sales growth

provides net financing. The same study also examined an additional correlation: the question

whether sales volatility has an impact on working capital management. A significant negative

correlation between sales volatility and working capital requirements was identified,

suggesting that managers tend to manage working capital more aggressively when their sales

figures become more volatile. According to these findings, companies with highly volatile

sales usually reduce working capital to the minimum needed to ensure liquidity.

2.5.2.3 Competitive position

Besides company-specific variables, many researchers have drawn attention to external

variables as well. The aim is to analyze whether external factors that companies are scarcely

able to influence might also impact working capital decisions. The most obvious variable is

the question of whether the economy's business cycle has an impact on working capital levels.

According to academic research conducted by Chiou et al. (2006), companies tend to increase

NLB and WCR when the business cycle is in recession.145 The reason for the positive

correlation to NLB is that, during periods of economic recession, cash supply is

comparatively tight. Companies therefore have to keep more current assets available in order

to maintain the operating cycle. The same applies for the correlation to WCR: In a recession,

companies face higher levels of WCR. When a company faces difficult economic conditions,

growth often falls short of planned targets. Collecting accounts receivable tends to take

longer, while inventory levels rise due to reduced sales figures. All in all, these assumed

consequences result in higher levels of NLB and WCR.

A further competitive variable in respect to working capital management is the company's

market share. The underlying question is whether companies that enjoy a relatively powerful

market position in respect of their competitors, suppliers and customers pursue different

working capital policies to companies that face stiff competition. Nunn came to the

conclusion that a strong competitive position in its market enables companies to reduce

working capital levels.146 Basically, powerful companies manage to maintain lower levels of

working capital due to advantages in buying and selling. On the upstream side, dominant 144 Hill/Kelly/Highfield (2010), p. 794-796. 145 Chiou/Cheng/Wu (2006), p. 152f. 146 Nunn (1981), p. 212.

42

companies can leverage their larger scale of purchases to drive down procurement costs. In

addition, these companies are most likely to be able to dictate suppliers' delivery schedules,

due dates, credit terms, etc. to their own advantage. On the downstream side, a powerful

market position reduces the need for marketing tools such as credit and fast delivery, as

competitors are hardly in a position to jeopardize their sales. This argumentation is in line

with the results arrived at by Hill et al. (2010),147 though it should be noted that the tested

correlation is not statistically significant. The authors nevertheless stated that, in line with

their initial hypotheses, a negative correlation is indeed expected. Nunn then expanded on his

deliberations by investigating the effects of unstable market shares.148 His results were in line

with the correlations identified in the first run. The more instable a company's market

position, the more working capital it requires. In situations where customer loyalty is

uncertain, managers may therefore step up the use of sales tools such as credit and fast

delivery.

Another widespread variable used in studies is the market-to-book ratio. The results for this

variable are not clear-cut, as some indicate positive correlation while others find no indication

of any significant correlations. The first researchers to explore this issue were Kim et al.

(1998), who investigated putative correlations between market-to-book values and cash

holdings. According to their study, a significant positive correlation exists, indicating that the

higher the ratio, the higher the cash levels maintained.149 It is argued that a higher market-to-

book value is determined by growth opportunities. However, companies whose value is

determined to a large extent by growth opportunities usually face more serious information

asymmetries. Such information asymmetries ultimately lead to information-induced financing

constraints that force these companies to hold more liquid reserves. The results documented

by Kim et al. (1998) are in line with research published by Opler (1999).150 Appuhami (2008)

was unable to establish a significant correlation, even though correlations to NLB were, as in

the two previous studies, positive.151 Hill et al. (2010) investigated the correlation between the

market-to-book ratio and WCR/sales. The authors identified a significant positive correlation,

presenting a correlation coefficient of -.128.152 In line with the arguments put forward by Kim

et al. (1998), the higher market-to-book ratio in their results seems to indicate the existence of

147 Hill/Kelly/Highfield (2010), p. 797. 148 Nunn (1981), p. 212f. 149 Kim/Mauer/Sherman (1998), p. 352-355. 150 Opler/Pinkowitz/Stulz/Williamson (1999), p. 25. 151 Appuhami (2008), p. 16. 152 Hill/Kelly/Highfield (2010), p. 797.

43

increased information asymmetries. The degree of information asymmetries can be taken as a

proxy for the cost of external financing. Based on this line of argument, firms with higher

market-to-book ratios face higher financing costs and will seek to reduce WCR. Another of

the arguments provided points in the same direction: Companies with substantial growth

opportunities aim to reduce WCR to free up the liquidity needed to fund future growth. It

follows that both the negative correlation between the market-to-book ratio and cash holdings

described by Kim et al. (1998) and Opler (1999) and the positive correlation between the

market-to-book ratio and WCR described by Hill et al. (2010) are consistent and support each

other.

By analogy to the variables outlined above, researchers have also tested the impact of external

assessments of companies' financial strength on working capital. Kim et al. (1998) studied

potential effects of the interest spread between the return on assets and the return on short-

term treasury bills.153 The spread can be interpreted as the opportunity cost of cash holdings:

The higher the return on physical assets, the lower the opportunity cost for cash holdings. The

results of Kim et al.'s indicate a significant negative correlation. This outcome is consistent

with the prediction that the higher the return on physical assets compared to liquid assets, the

lower will be the investment in liquid assets. García-Teruel et al. (2008) likewise tested

whether there is a correlation between the interest spread and working capital decisions.154

They were unable to establish any significant results.

With regard to external views of companies and their products, moreover, Nunn (1981) also

tested the impact of the customers' perception of a company's image and the image of its

products on working capital levels. Nunn anticipated a negative correlation between a

company's relative image and working capital levels. According to his assumption, the better

the brand image of a company, the greater the degree of loyalty and the more favorably

products would be perceived relative to those of rivals. These attributes would guarantee sales

while reducing the need for extensive accounts receivable and inventories. In the course of his

study, Nunn (1981) was able to confirm his assumption, presenting a correlation coefficient

of -.114 at the .999 significance level. Along the same lines, he analyzed the impact of

relative price levels compared to a company's competitors. Nunn (1981) predicted a positive

impact, postulating that the higher the selling price compared to a company's competitors, the

greater its relative sales profitability would be. Consequently, higher relative profitability

153 Kim/Mauer/Sherman (1998), p. 352-355. 154 García-Teruel/Martínez-Solano (2008), p. 145-146.

44

would increase the incentive to use marketing tools such as credit granting to attract new

customers or retain the loyalty of existing customers. Nunn (1981) backed up the hypotheses

in his study by showing a positive correlation of .079 at the .995 significance level.

2.5.2.4 Industry factors

Not surprisingly, many academic researchers expected industry affiliations to have a

significant effect on working capital management. Since different industries require different

levels of capital intensity depending on their business models, one would logically expect to

find different working capital requirements. The first scholar who investigated these

relationships was Nunn (1981),155 who argued that companies that export a large proportion

of their products have to maintain longer pipelines for raw materials and work-in-progress

inventories. In addition, these companies may also need to operate part of their production in

the foreign countries they serve. Besides pressure on inventory levels, it is expected that the

collection period too may be longer as both distance and payment procedures in the host

country can drive complexity. Regarding the share of imports, he argues along similar lines:

The more a company is dependent on foreign raw materials and primary products, the longer

the supply pipelines and therefore the greater the transit inventories. Given that a large share

of imports implies exposure to the risk of supply chain interruptions, companies are expected

to increase buffer inventories too. The data used in Nunn's study supported his assumptions:

Both exports and the share of imports correlate positively to working capital/sales.

Another aspect is industry concentration. Nunn (1981) argues that heavy industry

concentration supports a greater spread of price to marginal costs than under normal market

conditions.156 A relatively higher spread of price to marginal cost, however, makes companies

more sensitive to disruptions and dissatisfied customers. In other words, the higher the spread,

the more beneficial it is to a company to carry higher inventory levels. The same applies to

the granting of credit: The higher the spread, the more beneficial is it to a company to satisfy

customers by using this kind of marketing tool, for example. By consequence, Nunn (1981)

was able to confirm his assumptions by presenting statistics that affirm a significant positive

correlation.

155 Nunn (1981), p. 212-213. 156 Nunn (1981), p. 213.

45

Hawawini et al. (1986) adopted a more generous approach in investigating industry effects on

working capital.157 Based on Compustat data for 1,181 companies in the period from 1960 to

1979, they analyzed whether the variability of WCR-to-sales ratios is different within

industries to the variability across industries. According to their results, a significant industry

effect was persistently visible in all of the nineteen years examined. They then extended their

research by identifying industries that have more similar working capital requirements and

others that differ very considerably. These findings were confirmed in a study conducted by

Filbeck et al. (2005).158 Analyzing the data for approximately 1,000 companies in the period

from 1996 to 2000 confirmed that working capital measures such as cash conversion

consistency, days working capital and days sales outstanding vary significantly between

industries. By way of example, the petroleum industry managed average days working capital

of 6 days throughout the entire period, whereas the scientific equipment industry averaged 25

days.

2.5.3 Related research topics interfacing with working capital management

2.5.3.1 A literature review of supply chain risk

2.5.3.1.1 Supply chain risk drivers versus risk sources

On a very general level, risk can be defined as "the probability of variance in an expected

outcome" or "the variance of the future return around its expected value".159 In other words,

risk is the product of the two separate but interrelated elements uncertainty and impact.160

Alongside copious research derived from risk and risk management, one emerging discipline

has been to analyze the aspects of risk associated with supply chains.161 In line with these

general definitions of risk, Jüttner et al. (2003) defined supply chain risk as "variation in the

distribution of possible outcomes, their likelihoods, and their subjective value".162 According

to this definition, there is an "upside" and a "downside" risk as the ex-post situation could be

above or below the expected value ex ante. This definition is not uncontroversial: Wagner et

al. (2006) argue that, for the purposes of supply chain management research, a notion of risk

157 Hawawini/Viallet/Vora (1986), p. 18-23. 158 Filbeck/Krueger (2005), p. 14. 159 Spekman/Davis (2004), p. 416; Ball/Brown (1969), p. 301. 160 Zsidisin/Ragatz/Melnyk (2005), p. 48. 161 E.g. March/Zur Shapira (1987), p. 1404-1418; Lhabitant/Tinguely (2001), p. 343-363. 162 Jüttner/Peck/Christopher (2003), p. 200.

46

as being purely negative corresponds best to supply chain reality. To this end, they define

supply chain risk as "the combination of (1) an unintended, anomalous triggering event that

materializes somewhere in the supply chain or its environment, and (2) as a consequential

situation which significantly threatens normal business operations of the firm in the supply

chain". Following, supply chain risk is the exposure to a breakdown of flows between the

different components of the supply chain.163 This position has been supported by several

studies confirming that, in human perception, the downside potential of risk is overrated.164

For this reason, it seems appropriate not to consider "happy disasters": situations in which

managers intentionally "gamble" on risk in a supply chain context, as outlined by Wagner et

al. (2006).165

Supply chain risk research has spawned different nomenclatures. For this reason, it is

appropriate first to specify, structure, and link existing terms relating to supply chain risk

management. The first terms that form a pair are supply risk drivers and supply chain

vulnerability. These terms describe the degree to which a supply chain is exposed to

disruptions. Supply chain vulnerability is the total exposure of the company's supply chain to

undesirable conditions. If this total exposure is broken down into its components, one speaks

of supply chain risk drivers. Svensson (2002) defined supply chain vulnerability as "a

condition that affects a firm's goal accomplishment dependent upon the occurrence of

negative consequences of disturbance […]".166 From a supply chain perspective, these

negative consequences potentially prevent effective supplies to the end customer market.167

One important factor regarding to supply chain vulnerability seems to be the company's

perceived trust and reliability. According to Svensson (2002), trust is important in lean

business relationships, as lean models tend to increase supply chain vulnerability.168 Existing

literature determines supply risk drivers to be variables such as global sourcing, supplier

concentration, and increased outsourcing.169 Supply chain risk drivers as such, and supply

chain vulnerability as a whole, do not lead to negative consequences. Rather, negative

consequences require variables "that cannot be predicted with certainty and from which

163 Lavastre/Gunasekaran/Spalanzani (2011), p. 829. 164 March/Zur Shapira (1987), p. 1407. 165 Wagner/Bode (2006), p. 303. 166 Svensson (2002), p. 112. 167 Jüttner/Peck/Christopher (2003), p. 200-201. 168 Svensson (2004), p. 469. 169 Chopra/Sodhi (2004), p. 54.

47

disruptions might emerge."170 In other words, supply chain exposure leads only to a negative

impact once a trigger or risk source is apparent. In existing literature, these triggers of

negative consequences have been called supply risk sources or supply disruptions. According

to the definition proposed by Jüttner et al. (2003), supply risk sources are environmental,

organizational or supply chain-related variables whose characteristics cannot be predicted

with certainty in advance.171 These variables nevertheless impact supply chain outcome

variables. Norrman et al. (2004) define supply chain risk sources as the "devastating ripple

effects that disasters or even minor business disruptions can have in a supply chain", in line

with the classification formulated by Jüttner et al. (2003).172 Studies list potential supply risk

sources such as environmental risk sources (e.g. sociopolitical actions and force majeure) and

organizational risk sources (e.g. strikes and machine failures).173 As is obvious from the list,

the nature and impact of supply risk sources varies significantly. The impact and probability

of an earthquake will most likely differ greatly from the characteristics of a supplier default.

A further consideration is that supply chain risk sources can be endogenous (such as machine

downtime) or exogenous (such as a political instability). As a consequence, various

taxonomies have been established though they generally contain many inconsistencies. The

demarcation of different categories likewise varies, as does the assignment of single risk

sources to super ordinate classes.174 One of the first researchers to investigate and classify

potential supply chain sources was Miller (1992). Miller (1992) defined "input supply

uncertainties" and "production uncertainties" as one aspect of integrated risk management.175

Based on a perspective current at that time, however, his understanding reflected only a small

subset of potential supply chain disruptions. Miller focused on raw material shortages, quality

changes, spare parts restrictions, machine failures and other random production factors.

According to his risk definition, "input supply uncertainties" and "production uncertainties"

belong to "operative uncertainties" that are part of overall "firm uncertainties". Besides "firm

uncertainties", he also identified "general environmental uncertainties", "industry

uncertainties" and "financial risk uncertainties" as risk drivers to the company. This taxonomy

was later broadened by Jüttner et al. (2003), who defined three main pillars: "environmental 170 Jüttner (2005), p. 122. 171 Jüttner/Peck/Christopher (2003), p. 200. 172 Norrman/Jansson (2004), p. 435. 173 Jüttner/Peck/Christopher (2003), p. 201; Norrman/Jansson (2004), p. 435;

Wagner/Bode (2006), p. 304-305. 174 E.g. Jüttner/Peck/Christopher (2003), p. 201; Wagner/Bode (2006), p. 304-305;

Chopra/Sodhi (2004), p. 54. 175 Miller (1992), p. 18.

48

risk sources", "organizational risk sources" and "network related risk sources".176 For other

taxonomies, please see Wagner et al. (2006), Manuj et al. (2008) and Svensson (2000).177

Some papers, however, preferred to analyze potential risks to supply chains in general rather

than discussing these different constructs in particular.178 One special focus of supply chain

risk research was initiated by Zsidisin et al. (2000), who published a paper about purchasing

risk assessment and management-named supply risk.179 These authors were the first to define

supply risk as "the transpiration of significant and/or disappointing failures with in-bound

goods and services".180 As outlined in the definition, Zsidisin et al. (2000) focused their work

on one part of the value chain: the inbound supply chain. This focus reflects the fact that

sourcing is becoming a competitive advantage, as the scope of outsourcing is on the rise in

many companies, such that product supply lines span the entire global marketplace.181 The

foundation for research into supply risk was laid even earlier, however. In 1983, Kraljic

(1983) published a highly regarded paper on the need to integrate purchasing in supply

management.182 These papers moved numerous authors to conduct further investigation into

potential disruptions along the whole value chain from suppliers, through the internal

transformation process to final distribution to customers. Others, such as Giunipero et al.

(2004) and Zidisin et al. (2003), followed Zsidisin et al. (2000) in studying the specialized

field of supply risk.183

The negative impact of the occurrence of a supply risk is called risk consequence.184 The

consequences of a supply risk focus on the supply chain outcome in terms of cost, quality,

quantity and time, for example.185 Christopher et al. (2004) named several potential supply

risk consequences, such as inventory costs due to obsolescence, markdowns, stock-outs and

retail markdowns.186 To avoid these negative impacts of supply risks, companies need to

develop mitigation strategies. The underlying supply chain risk drivers serve as the levers that

can be used to steer supply chain risk. Mitigating strategies are those actions that

organizations deliberately undertake to mitigate the uncertainties identified from potential

176 Jüttner/Peck/Christopher (2003), p. 201-202. 177 Wagner/Bode (2006), p. 304-305; Manuj/Mentzer (2008), p. 201; Svensson (2000), p. 739. 178 Spekman/Davis (2004), p. 419-420. 179 Zsidisin/Panelli/Upton (2000), p. 187-197. 180 Zsidisin/Panelli/Upton (2000), p. 187. 181 Christopher/Mena/Khan/Yurt (2011), p. 67. 182 Kraljic (1983), p. 109-117. 183 Giunipero/Eltantawy (2004), p. 698-713; Zsidisin/Ellram (2003), p. 15-27. 184 Jüttner/Peck/Christopher (2003), p. 200. 185 Bogataj/Bogataj (2007), p. 291. 186 Christopher/Lee (2004), p. 388.

49

supply disruptions.187 Potential mitigating strategies have been outlined by Chopra et al.,

Christopher et al. (2004) and Jüttner et al. (2003).188 Companies must adopt a holistic

approach to managing supply chain risk. They must cover the identification of supply chain

risk sources, their consequences and the underlying supply risk drivers in order to develop

mitigation strategies – supply chain risk management, in other words.189 Jüttner et al. (2003)

defined supply chain risk management as follows: "The identification and management of

risks for the supply chain, through a coordinated approach amongst supply chain members, to

reduce supply chain vulnerability as a whole".190

Most authors in the field of supply chain risk management claim that today's business

conditions and requirements necessitate a larger number of supply chain risk sources and

drivers, as well as interrelations and correlations. Christopher et al. (2004), for example, list

greater uncertainties in supply and demand, the globalization of the market, ever shorter

product and technology lifecycles and the increased use of manufacturing, distribution and

logistics partners as trends that are driving greater risk exposure in today's supply chains.191

Others, such as Svensson (2001) and Lee (2004, 2010), also emphasize the trend toward ever

leaner supply chains based on practices such as just-in-time delivery.192 Indeed, there is hardly

a company nowadays that does not supply its wares to Far Eastern or triad markets, sell to

global customers or make use of integrated value chains. Besides these business trends,

another trend too is apparent: The number of natural disasters, terrorist attacks, instances of

political instability and man-made disasters has likewise increased perceptibly of late.193 The

growing frequency of such incidents has also driven monetary losses as a consequence of

these disasters.194 Recent reports indicate that total monetary losses in 2011 reached the

second highest total of USD 108 billion.195 One major cause of greater storm damage and

flooding, as in the wake of Hurricane Katrina, for example, is undoubtedly the process known

as global warming. Accordingly, experts predict increasing threats to global supply chains

going forward. Following on from 9/11, the Madrid bombings and the attacks in London,

187 Miller (1992), p. 322. 188 Chopra/Sodhi (2004), p. 55; Christopher/Lee (2004), p. 10; Jüttner/Peck/

Christopher (2003), p. 19. 189 Berg/Knudsen/Norrman (2008), p. 288-310, Ritchie/Brindley (2007), p.1398-1410. 190 Jüttner/Peck/Christopher (2003), p. 201. 191 Christopher/Lee (2004), p. 388. 192 Svensson (2001); p. 208; Lee (2004), p. 102; Lee (2010), p.64. 193 Kleindorfer/Saad (2005), p. 53. 194 Stecke/Kumar (2009), p. 1. 195 Handelsblatt, 15th December 2011.

50

terrorist activities too have attracted greater attention in recent years.196 Decision makers have

recognized that such disruptions have the potential to severely harm profits or even threaten

the existence of entire companies.197 One recent example was the Japanese earthquake, which

caused many supply chains in the automotive and electronic industries to collapse. All in all,

it is assumed that these trends will add to the vulnerability of today's supply chains.198

Strategic maneuvers by decision makers are required to develop mitigation strategies and

properly manage supply chain risk levels. In light of the above, companies have no choice but

to adapt and "weatherproof" their supply chains in the future.199

Literature on supply chain risk has been attracting considerable attention since the early years

of this century.200 However, there is still a lack of work in this research field.201 In particular,

empirical surveys that test supply risk constructs and correlations between constructs are

conspicuous by their absence.202 A few case studies covering the topic have been conducted,

such as those by Norrman (2004) and Peck (2005).203 The number of large-scale studies is

very limited, however. To the knowledge of the author, only a handful of studies make use of

large-scale surveys. One of the first was Zsidisin et al. (2003), who gathered data from a mail

survey addressed to 1,000 randomly selected purchasing professionals. In total, 261 usable

mailings were returned, equivalent to a response rate of 28 percent.204 In 2005, Jüttner (2005)

conducted an empirical survey in cooperation with the Chartered Institute for Logistics and

Transport and was supplied with the views of 137 logistic managers.205 The latest large

empirical study was conducted by Wagner et al. (2006). All in all, 760 usable responses were

generated by top executives in logistics and supply chain management positions.206 All

authors who have worked empirically on this topic have bemoaned the lack of a substantiated

empirical database on supply chain risk and encouraged for further contributions by the

scientific community.207

196 Martha/Subbakrishna (2002), p. 19. 197 Hendricks/Singhal (2005), p. 35. 198 Thun/Hoenig (2011), p. 247. 199 Martha/Subbakrishna (2002), p. 19. 200 Zsidisin/Panelli/Upton (2000), p. 187; Jüttner/Peck/Christopher (2003), p. 197; Chopra/Sodhi (2004), p. 53. 201 Thun/Hoenig (2011), p. 243. 202 Tang/Nurmaya Musa (2011), p. 25. 203 Norrman/Jansson (2004), p. 440; Peck (2005), p. 211. 204 Zsidisin/Ellram (2003), p. 20. 205 Jüttner (2005), p. 124. 206 Wagner/Bode (2006), p. 306. 207 Jüttner (2005), p. 139; Tang/Nurmaya Musa (2011), p. 31-33.

51

2.5.3.1.2 Risk drivers and risk source items

Besides large-scale empirical studies, numerous researchers have also listed and classified

supply chain risk drivers and supply chain risk sources (see table 6). This section therefore

documents and clusters the identified supply chain risk drivers and supply chain risk source

items. All in all, 16 papers more or less cover research into supply chain risk management in

terms of the way they list the sources and drivers of supply chain risk. Research into these

issues peaked from 2003-2005, the period in which most of the papers were published. As we

saw in the previous section, the first paper published dates back to 1992 and was published by

Kent D. Miller (1992). This section first outlines all the supply chain risk drivers identified in

existing literature, followed by a review of literature on supply chain risk sources.

The first scientific work that listed supply chain risk drivers in the context of supply chain risk

management was conducted by Jüttner et al. (2003). All in all, these authors list five supply

chain risk drivers, claiming that these drivers had emerged over the past decade and therefore

had thus increased companies' supply chain risk levels. The risk drivers named were "a focus

on efficiency rather than effectiveness", "the globalization of supply chains", "focused

factories and centralized distribution", "the trend to outsourcing" and "reduction of the

supplier base".208 These risk drivers affect the complexity of network structures (in the

context of globalization, for example) or lead to more integrated supply chains. Most of the

identified supply chain risk drivers have been confirmed by other researchers. Norrman et al.

(2004) identified "globalization of supply chains" as a risk driver, for example, as did Jüttner

(2005), Peck (2005), Wagner et al. (2006).209 Similarly, lean supply chains, centralized

distribution, outsourcing and the reduced supplier base have all been acknowledged as risk

drivers by at least one of the authors named above, which also confirms the initial hypothesis

of Jüttner et al. (2003). Still more authors have extended the list of supply chain risk drivers

with regard to a more integrated supply chain. Norrman et al. (2004) identified "more

intertwined and integrated processes" as a risk driver in today's supply chains.210 Additionally,

Norrman et al. (2004) identified "reduced buffers, e.g. inventory and lead time" as a risk

driver, a finding that was confirmed by Jüttner (2005) in a paper published later.211 In terms of

network-related risk drivers, the initial list put forward by Jüttner et al. (2003) has been

208 Jüttner/Peck/Christopher (2003), p. 205. 209 Norrman/Jansson (2004), p. 434; Jüttner (2005), p. 134; Peck (2005), p. 214-216; Wagner/Bode (2006),

p. 305-306. 210 Norrman/Jansson (2004), p. 434. 211 Norrman/Jansson (2004), p. 434; Jüttner (2005), p. 134.

52

expanded as follows: Peck (2005) cited "IT upgrades" and "internal network redesigns" as

additional drivers, both of which have since been confirmed by others such as Chopra et al.

(2004).212 Other drivers in this category named by at least one author are: centralized

production, industry consolidation, ongoing regulatory changes, single sourcing, the financial

strength of customers, high capacity utilization of the supply source, inflexibility of the

supply source, system integration or extensive systems networking, e-commerce and vertical

integration of the supply chain.213 In addition to these network-related drivers and drivers

toward an integrated supply chain, however, researchers have also come up with further

drivers to complete the picture. In recent literature, another aspect has received growing

attention: the importance of demand-related risk drivers.

212 Peck (2005), p. 215; Chopra/Sodhi (2004), p. 54. 213 Jüttner (2005), p. 134; Peck (2005), p. 215; Wagner/Bode (2006), p. 305; Chopra/

Sodhi (2004), p. 54

53

Table 6: Literature review of supply chain risk management

# Year AuthorObject of investigation Drivers level 1 Drivers level 2

1 1992 Miller Risk sources • Labor uncertainties

• Input supply uncertainties

• Production uncertainties

• Product liability• Emission of pollutants• Problems with collectibles

• Labor unrest• Employee safety• Raw materials shortages• Quality changes• Spare parts restrictions• Machine failure• Other random production facotors

2 2000 Zsidisin, Panelli, Upton

Risk sources • Business risk• Supplier capacity risk• Quality• Production technological changes• Disasters

Risk drivers • Focus on efficiency rather than effectiveness• Globalisation of Supply Chains• Focused factories and centralized distribution• Trend to outsourcing• Reduction of supplier base

Risk sources • Environmental risk sources

• Organizational risk sources

• Network-related risk sources

• Result of accidents (e.g. fire)• Socio-political actions (e.g. fuel protests or terrorist attacks)• Acts of God (e.g. extreme weather or earthquakes)• Labour (e.g. strikes)• Production uncertainties (e.g.machine failure)• IT-system uncertainties• Lack of ownership• Chaos• Inertia

4 2003 Zsidisin Risk sources • Individual supplier failures

• Market charakteristics

• New product development problems• Delivery failures• Relationship issues• Supplier obligations to other customers• Quality problems• Price/cost increases• Inability to meet quantity demand• Technologically behind• Discontinuity of supply• Sole source/limited qualified sources• Market shortages• Commodity price increases• Geographic concentration of suppliers• Supplier patents

5 2003 Zsidisin, Ellram Risk sources • Inability to handle volume demand changes• Failures to make delivery requirements• Cannot provide competitive pricing• Technologically behind competitors• Inability to meet quality requirements

3 2003 Jüttner, Peck, Christopher

54

Table 6: Literature review of supply chain risk management (continued)


6 2004 Chopra, Sodhi Risk drivers • Disruptions

• Delays

• Systems

• Forecast

• Intelectual property

• Procurement

• Receivables

• Inventory

• Capacity

• Natural disaster• Labor dispute• Supplier bankruptcy• War and terrorism• Dependency on a single source of supply• High capacity utilization at supply source• Inflexibility of supply source• Poor quality or yield at supply source• Excessive handlings due to border crossings etc.• Information infrastructure breakdown• System integration or extensive systems networking• E-commerce• Inaccurate forecasts due to long lead times, seasonality, product variaty, short life cycles, small customer base• "Bullwhip effect" or information distortion due to sales promotions, incentives, lack of supply-chain visibility and exaggeration of demand in times of product shortage• Vertical integration of supply chain• Global outsourcing and markets• Exchange rate risk• Percentage of a key component or raw material produced from a single source• Industrywide capacity utilization• Long-term versus short-term contracts• Number of customers• Financial strength of customers• Rate of product obsolescence• Inventory holding cost• Product value• Demand and Supply uncertainty• Cost of capacity• Capacity flexibility

7 2004 Christopher, Lee

Supply Chain Confidence

• Demand volatility• Product and technology life-cycles• Outsourcing• Reduce supplier base• Globalisation of the markets• Increased use of manufacturing, distribution and logistics partners

8 2004 Giunipero, EltantRisk creators • Material availability• Long distances• Insufficient capacity• Demand fluctuations• Technoligical changes• Financial instability• Labor instability• Management turnover

Risk drivers • Increased use of outsourcing of manufacturing/R&D• Globalization of supply chains• Reduction of supplier base• More intertwined and integrated processes• Reduced buffers, e.g. inventory and lead time• Increased demand for on-time deliveries• Shorter lead times• Shorter product life cycles

Risk sources • Hurricanes• Diseases• Fires• Rapidly weakening demand• Inaccurate supply planning• Supply chain capacity risks

10 2004 Spekman, Davis

Risk sources • Supplier capacity risk• Quality• Changes in product design and production processes• An inability to reduce costs• Unanticipated delays and supply disruptions

11 2005 Jüttner Risk drivers • Globalisation of supply chains• Reduction of inventory holding• Centralised distribution• Reduction of the supplier base• Outsourcing• Centralised production

9 2004 Norrman, Jansson

55

Table 6: Literature review of supply chain risk management (continued)


12 2005 Peck Risk drivers • Demands for shorter lead-times• Outsourcing• Increasing use of global sourcing and supply• Irregular demand patterns• Measures introduced to reduce costs• Changes and upgrades to product specifications• Customer determined network reconfigurations• Continous improvement initiatives• Internal network redesigns• IT upgrades• Changing process technology• Supplier rationalisations• Industry consolidations• Ongoing regulatory changes

13 2005 Zsidisin, Ragatz et al

Risk sources • Inbound Supply Processes • Supplier network• Cash Management• Order Fulfillment• People & Knowledge• Business Planning• Customer Solutions

Risk drivers • Customer dependence• Supplier dependence• Supplier concentration• Single sourcing• Global sourcing

Risk sources • Demand side risks

• Supply side risks

• Catastropic risks

• Unanticipated or very volatile demand• Insufficient or distorted information from your customer • Poor logistics performance of suppliers• Supplier quality problems• Sudden demise of a supplier (e.g. due to bankruptcy)• Poor logistics performance of logistics service providers• Capacity fluctuations or shortages on the supply markets• Political instability, war, civil unrest, socio-political crises• International terror attacks • Diseases or epidemics• Natural disasters

15 2008 Manuj, Mentzer Global Supply Chain risks

• Supply

• Demand

• Operational risks

• Supplier Opportunism• Inbound product quality• Transit time variability• Risks affecting suppliers• Demand variability• Forecast error• Competitor moves• Risks affecting customers• Inventory ownership• Asset and tools ownership• Product quality and safety

16 2008 Wagner, Bode Risk sources • Demand side risks

• Supply side risks

• Regulatory, legal and bureaucratic risks

• Infrastructural risks

• Catastrophic risks

• Unanticipated or very volatile demand• Insufficient or distorted information [...]• Poor logistics performance of suppliers• Supplier quality problems• Sudden default of a supplier• Poor logistics performance [...]• Capacity fluctuations or shortages [...]• Changes in the political environment [...]• Administrative barriers [...]• Downtime or loss of own production capacity [...]• Perturbation or breakdown of internal IT [...]• Loss of own production capacity [...]• Perturbation or breakdown of external IT [...]• Political instability, war, civil unrest, socio-political crises• Diseases or epidemics• Natural disasters• International terror attacks

14 2006 Wagner, Bode

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Norrman et al. (2004) identified "increased demand for on-time deliveries", "shorter lead

times" and "shorter product life cycles" as risk drivers with growing importance.214 This

aspect has been taken up by several other authors as well.215 Items such as "irregular demand

patterns", "customer dependence" and "financial strength of customers" have been presented

too.216

The aspect of supply chain risk research that has even received more attention is the supply

chain risk sources construct. Unlike the drivers of supply chain risk, its sources can be

quantified in terms of probability and impact. Indeed, this aspect has undoubtedly captured

attention in part due to the quantification of strategic and operating risks in many risk reports.

Here again, researchers have come up with numerous potential risk sources. Following the

classification used by Wagner et al.. (2008), five main categories of supply chain risk sources

exist: "demand side risks", "supply side risks", "regulatory, legal and bureaucratic risks",

"infrastructural risks" and "catastrophic risks".217 This classification seems to the author to be

reasonable, as all the risk sources identified by researchers up to now can be assigned to one

or other of the groups defined by Wagner et al. (2008). Jüttner (2005) defines the first group,

"demand side risks", as all risks that emerge from the downstream supply chain and result in a

mismatch of demand and supply. Potential disruption can occur during physical distribution

to customers or can originate from customers' unforeseeable demand for certain products.218

One much-discussed phenomenon in this respect is the bullwhip effect, which describes how

customers for haberdashery materials or products tend to order more than required for the

immediate future in order to secure a lasting supply. This effect is one explanation of why

demand fluctuates so markedly.219 Wagner et al. (2008) thus named "unanticipated or very

volatile demand" and "insufficient or distorted information from your customer […]" as

potential demand-side risks.220 A broad spectrum of supply-side risk items is likewise

documented in the literature. Generally speaking, supply-side risks cover all risks relating to

uncertainty with regard to supplier activities.221 Zsidisin (2003) defines supply-side risk as

"[…] an incident associated with inbound supply from individual supplier failures or the

214 Norrman/Jansson (2004), p. 434. 215 E.g. Peck (2005), 215. 216 Peck (2005), p. 214; Wagner/Bode (2006), p. 305; Chopra/Sodhi (2004), p. 54. 217 Wagner/Bode (2008), p. 323. 218 Jüttner (2005), p. 122-123. 219 Lee/Padmanabhan/Whang (1997), p. 1875. 220 Wagner/Bode (2008), p. 310. 221 Jüttner (2005), p. 122.

57

supply market occurring […]".222 This definition therefore includes all risk sources in the

entire upstream supply chain of a company: purchasing, suppliers, supplier networks and

supplier relationships.223 In particular, researchers have named supplier quality issues as a

potential source of risk.224 Quality issues occur when a supplier is not able to meet predefined

specifications, resulting in production failure, waste production and, ultimately, delivery

problems. Other factors named are the sudden demise of a supplier, poor logistics

performance by suppliers and/or logistics providers, price/cost increases, raw materials

shortages and the inability to change technological or product design.225 "Sudden demise"

refers to a situation in which, for example, a supplier goes bankrupt and is therefore unable to

meet its contractual commitments, with all the negative consequences that this entails for the

purchasing party. Poor logistics performance includes shortcomings regarding punctual and

accurate delivery, order fill capacity and reliability caused either by the supplier or the

logistics provider. Price/cost increases may occur due to capacity shortages or to a situation in

which the supplier has gained market power that enables it to increase margins. In cases

where input price rises cannot be passed on to the end customer, higher purchasing prices may

have to be absorbed. Material shortages occur when demand exceeds supply, leading to less

capacity on the supply side. Suppliers that cannot cope with technological advances likewise

represent a source of risk as the competitiveness of final products is at risk. The same goes for

the inability to cope with product design changes: In an age of reduced lifecycles and

increasing product variants, this is a competitive disadvantage. Moreover, as outsourcing

increases, especially to low-cost countries, this risk may become even more important in the

future.226 In a highly regarded paper, Kraljic (1983) was one of the first researchers to

emphasize the need to develop proper mitigation strategies for supply risk sources. He

introduced a decision matrix to assess procurement groups according- to their importance and

to the underlying complexity of the supply market. Based on his assessment of procurement

categories, Kraljic calls for a tailored sourcing mitigation strategy.227

The third group named above comprised regulatory, legal and bureaucratic risks. This

category contains all risk sources that relate to the actions of public authorities. Changes in

laws and policies can have a significant impact on links in the supply chain network. For

222 Zsidisin (2003), p. 222. 223 Wagner/Bode (2008), p. 310. 224 Spekman/Davis (2004), p. 419. 225 Miller (1992), p. 319; Wagner/Bode (2006); p. 304-305. 226 Wagner/Bode (2008), p. 311. 227 Kraljic (1983), p. 111.

58

instance, the new road pricing schedules for freight vehicles in many European countries have

significantly impacted the price structure for logistics services. Changes effected by

administrative, legislative or regulatory agencies can thus severely impact the supply chain –

especially as they tend to occur suddenly and are difficult to predict.228 Specifically, Wagner

et al. (2008) name two risk sources in this group: the "[…] introduction of new laws,

stipulations etc." and "administrative barriers for the setup or operation of supply chains".

Infrastructural risks are defined as fourth group of potential risk sources. This cluster covers

all breakdowns in the supply chain that are linked to the operations of a company. Potential

sources can be of a technical nature, such as machine outages, or of a human nature, such as

strikes. A more extensive list of potential technical failures would also include incidents such

as interference, breakdowns in IT infrastructures and the results of fire or accidents, for

example.229 Human-related network risk sources include labor disputes, sabotage and

occupational accidents.230

The last group of potential risk sources covers all risks that relate to catastrophes or disasters

that can be seen as force majeure. The potential spectrum of such risk sources is broad.

Epidemics, natural disasters, political instability, civil unrest and even terrorism are risk

sources that have the potential to significantly impact supply chains.231 Literature on such

disasters as a source of risk has increased lately as their frequency has increased and the

resultant monetary losses have risen sharply.232 Reinsurer Munich Re, for instance, reports

that the average cost of unpredictable disasters has risen by a factor of 10 since the 1960s.233

Epidemics include all diseases such as BSE in the UK in 2001 and SARS, which broke out in

China in 2002 and ultimately affected the whole world.234 Potential risk sources arising from

natural disasters are flooding, earthquakes, storms (such as hurricanes, typhoons or

tornadoes), tsunamis, aridity and fires. Several such disasters are still fresh in the memory of

most people: Hurricane Katrina in 2005, the Asian tsunami and, more recently, the destructive

flooding in Thailand are just three of many examples.235 Instances of political instability and

civil unrest have likewise been observed of late in the context of what has become known as

the "Arab Spring". Political instability and a lack of transparency about future developments 228 Wagner/Bode (2008), p. 311. 229 Jüttner/Peck/Christopher (2003), p. 201-202. 230 Wagner/Bode (2008), p. 311. 231 Jüttner (2005), p. 122. 232 Stecke/Kumar (2009), p. 1. 233 Tang (2006), p. 33. 234 Norrman/Jansson (2004), p. 120; Stecke/Kumar (2009), p. 2. 235 Stecke/Kumar (2009), p. 1-2.

59

in the transition process also force many supply chains to modify their existing structures.

Terrorist attacks are yet another issue with the potential to affect global supply chains that has

gained increased attention over the past decade. At the latest in the wake of the 9/11 attacks

on the World Trade Center, Western countries woke up to the new dimension of risk posed by

modern terrorism. Since then, the tightening of safety regulations has directly impacted global

supply chains. All these catastrophic risks have in common that they pose a substantial risk to

supply chains, as production facilities and transport systems are very vulnerable to the

resultant disruptions. Handling and containing such events, however, is very challenging, as

their probability is comparatively low. Accordingly, supply chains are forced to prepare for

events whose probability is low but whose impact could be very severe. Many companies

struggle to calculate the probability of this kind of events as the basis on which to justify risk

reduction programs or contingency plans.236 Due to their very low probability of occurrence,

such events have become known in literature as "black swan events".237

2.5.3.2 A literature review of manufacturing performance

Empirical literature on manufacturing performance is very mature. This maturity is reflected

first by the number of papers published on this topic. A literature search based on the term

'manufacturing performance' yielded a total of 36 papers that present empirical studies on the

specific subject, as shown in table 7. However, the maturity of research into this discipline is

also reflected by the comparatively long history of relevant research: The first paper was

published in 1985.

Nor has attention to the topic yet waned, as a dozen papers have been published in the current

decade. As to be expected, the majority of papers investigate the US manufacturing industry.

This dominance, however, has not been at the expense of papers covering other economic

areas. Europe, for example, has been a target for investigation, with a focus on Germany,

Italy, Spain and the UK. Studies have likewise focused on Australia, New Zealand, Japan and

China. In terms of sample size, the huge variety continues. Small-scale studies containing

case studies with just six participants have been conducted, as have studies of up to around

3,000 companies.

236 Tang (2006), p. 36. 237 Taleb (2010), p. 1-480.

60

The objects of investigation are many and varied but tend to center around three key aspects.

The majority of publications focus on existing success factors or drivers of manufacturing

performance. Second, some authors tackle the question of whether certain trade-offs exist in

the different dimensions of manufacturing performance existing and, if so, whether they can

be ignored. Lastly, only a handful of researchers investigate the correlation between

manufacturing performance and overall firm performance. This section outlines the current

status of literature on these three aspects.

Regarding existing success factors or drivers of manufacturing performance, academics have

tested numerous constructs. It is conspicuous that most papers covering that pursue this goal

investigate the correlation between certain manufacturing concepts (such as integrated

manufacturing as such, or its component parts: advanced manufacturing, just-in-time

production and total quality management) and manufacturing performance. Other trends

examined include lean production, automation, manufacturing flexibility, total productive

maintenance and process technology fit. Regarding integrated manufacturing as a success

factor, studies suggest that there is a positive correlation with manufacturing performance.

61

Table 7: Literature review of manufacturing performance

# Year Author AreaSample size Object of investigation Drivers level 1 Drivers level 2

1 1985 Richardson, Taylor, Gordon

n.a. n.a. Corporate Mission/ Manufacturing Performance

• Volume of Output• Cost per unit• Quality• Delivery on schedule• Labor productivity• Ability to introduce new products• Flexibility to product changes• Flexibility to volume changes

2 1989 Cleveland, Schroeder, Anderson

USA 6 Production competence/Business Performance

• Adaptive Manufacturing• Cost-Effectiveness• Delivery Performance• Logistics• Production Economies of Scale• Process Technology• Quality Performance• Throughput and Lead Time• Vertical Integration

3 1990 Ferdows, de Meyer Europe 167 Manufacturing Performance trade-offs

• Quality conformance• Cost

• Delivery dependability

• Speed of new product introduction

• Quality conformance• Unit production cost• Overhead costs• Inventory turnover• On-time delivery• Delivery speed• Development speed

4 1992 Corbett, Harrison Australia, New Zealand

209 Employee involvement/Manufacturing Performance

• Quality improvement• Inventory turnover• Market share• Profitability

5 1993 Appleyard, Brown World 23 Employment practices/Manufacturing Performance

• Defct density• Line yield• Direct labor productivity• Stepper throughput• Cycle time

6 1996 Dean, Snell USA 512 Integrated Manufacturing/Manufacturing Performance

• Quality

• Delivery Flexibility

• Scope Flexibility

• Cost

• Product quality• High product reliability• Exceptional product performance• Conformance to specifications• Dependability• Zero defects• Product serviceability• Durability• On-time delivery• Meeting release dates for new products• Flexibitlity• Scale up/down production quickly• Short lead time from order to delivery• High efficiency/productivity• Economies of scale• Adapting to changes in product mix• Handling difficult/nonstandard orders• Products made to order• Small lots• Low unit costs• Low labor costs• Low material costs

7 1997 Bozarth, Edwards USA 24 Market requirement focus/Manufacturing characteristics/Manufacturing Performance

• Cost• Conformance quality• Delivery speed• Reliability• Product range• Design capability

8 1997 Mapes, New, Szwejczewski

United Kingdom

782 Manufacturing Performance trade-offs

• Cost• Quality• Time

• Flexibility

• Number of changes in projects• Δ average time between two subsequent innovations• Development time for new products• Adherence to due dates• Incoming quality • Distance travelled• Value added time/Total time• Schedule attainment• Outgoing quality• Manufactuing cost• Complexity of procedures• Size of batches of information• Cycle time• Bid time

9 1997 Sakakibara, Flynn, Schroeder, Morris

USA, Japan 822 JIT/Manufacturing Performance

• Lead time• Cycle time• Inventory turnover ratio• On-time delivery

10 1997 Lowe, Delbridge, Oliver

Europe 71 Lean Production/Manufacturing Performance

• Productivity (unit per labour hour)• Quality (parts per million)• Internal defect rate (% of total volume)• Space utilization (units per m²)

62

Table 7: Literature review of manufacturing performance (continued)


11 1998 Filippini, Forza, Vinelli

Italy 43 Manufacturing Performance • Economic

• Quality

• Time

• Invested capital turnover• Return on Sales• Quality consistency• Quality capability• Delivery time• Delivery Punctuality

12 1998 Ettlie World 600 R&D/Manufacturing Performance

• Number of employees• % improved market share• Computerization• % change in agility

13 1999 Ittner, Lanen, Larcker

USA 2,789 Activity-Based-Costing/Manufacturing Performance

• ROA• Quality

• Time

• ROA• Finished products pass first quality yield [%]• Scrap or rework cost / Sales• Manufacturing cycle time• Standard lead time order to shipment

14 2001 Bozarth, McCreery USA 13 Market requirements focus/Manufacturing Performance

• Cost• Conformance quality• Delivery reliability• Delivery speed• Product range• Design capability

15 2001 McKone, Schroeder, Cua

USA, Italy, Germany, Japan

117 Total productive maintenance/Manufacturing Performance

• Low cost• Inventory turnover• Quality• On-time delivery• Fast delivery• Flexibility

16 2001 Das, Narasimhan USA 322 Process-technology fit/Manufacturing Performance

• Manufacturing cost reduction• Quality performance• New product introduction time reduction• Delivery performance• Customization responsiveness

17 2001 Das, Narasimhan USA 322 Purchasing integration/Manufacturing Performance

• Manufacturing cost reduction• Quality performance• New product introduction time reduction performance• Delivery performance• Customization responsiveness performance

18 2002 Challis, Samson, Lawson

Australia, New Zealand

1,289 Integrated Manufacturing/Manufacturing performance

• High customer satisfaction• Positive Cash Flow• Total cost per unit• On time delivery to customers• Lost time due to industrial accidents

19 2002 Schroeder, Bates, Junttila

USA 164 Manufacturing strategy/Manufacturing performance

• Manufacturing cost (% of Sales)• Scrap rate (conformance quality)• Percentage of deliveries on time• # days receipt raw material to customer receipt• Lenght of fixed production schedule

20 2002 Boyer, Lewis USA 110 Operations strategy trade-offs • Cost

• Quality

• Delivery

• Flexibility

• Reduce inventory• Increase capacity utilization• Reduce production costs• Increase labor productivity• Provide high-performance products• Offer consistent, reliable quality• Improve conformance to design spec.• Provide fast deliveries• Meet delivery promises• Reduce production lead time• Make rapid design changes• Adjust capacity quickly• Make rapid volume changes• Offer a large number of products features• Offer a large degreee of product variety• Adjust product mix

21 2003 Merino-Diaz de Cerio Spain 965 Quality Management/Operational Performance

• Cost• Product Quality

• Time based improvements

• Productive hours to total number of hours• Percentage of returned products over sales• Percentage of defective finished products• Percentage of defective products in process• Punct (delivery dates compiled)• Speed (time from material received to moment product is delivered to customer)

22 2004 Henderson, Swamidass, Byrds

USA 1,000 Integrated Manufacturing/ Manufacturing Performance/ROI

• Production planning, scheduling and control• Employee involvement and motivation• Automation and process technology• Just-in-time production• Labor productivity• Quality control/assurance• Decentralization of manufacturing decisions• Product design• Overall evaluation of our manufacturing

23 2004 Rosenzweig, Roth World 867 Competitive progression theory/Manufacturing performance

• Conformance quality• Reliability of delivery times (on time)• Ability to rapidly change production volumes• Manufacture products at lower internal costs than competition

24 2005 Challis, Samson, Lawson

Australia, New Zealand

1,024 Integrated Manufacturing/ Manufacturing Performance

• High customer satisfaction• Positive Cash Flow• Total cost per unit• On time delivery to customers• Lost time due to industrial accidents

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Table 7: Literature review of manufacturing performance (continued)


25 2005 Fynes, Voss, Burca Ireland 202 Supply chain relationship dynamics/Manufacturing Performance

• Quality performance

• Delivery performance

• Cost performance

• Flexibility performance

• Frequency of customer complaints• Adequacy of customer complaint tracking• Speed of delivery relative to competitors• Percentage of orders delivered on-time• Unit cost of product relative to competitors• Unit cost of product over life cycle• Volume flexibility• Variety (product line) flexibility

26 2005 Cordero, Walsh, Kirchhoff

USA 105 Financial incentives/Manufacturing Performance

• Productivity• Product quality• Speed to complete manufacturing orders• Customer satisfaction• Flexibility to manufacture new products• Diversity of product line

27 2005 Narasimhan, Swink, Kim

USA 58 Manufacturing practices/Manufacturing Performance

• New product development• Flexibility• Efficiency• Market-based performance

28 2006 D'Souza USA 193 Manufacturing flexibility/Manufacturing Performance

• Delivery Performance• Quality Performance• Cost Performance

• Percentage of on-time delivery finished products• Extent of non-conformance with design specs• Cost of production per unit

29 2007 Swink, Nair USA 224 Advanced Manufacturing Technologies/Manufacturing Performance

• Cost efficiency• Quality• Delivery• New Product Flexibility• Process Flexibility

30 2008 Liao, Tu USA 303 Automation & integration/Manufacturing Performance

• Cost performance• Quality performance• Delivery performance• Flexibility performance• Innovation performance

31 2008 Karim, Smith, Halgamuge, Islam

Australia, Malaysia

46 Manufacturing practices/Manufacturing Performance

• Product capacity utilization• Product yield rate• Customer return rate• On time delivery

32 2008 Naor, Goldstein, Linderman, Schroeder

USA 189 Organizational culture/Manufacturing Performance

• Cost

• Quality

• Delivery

• Flexibility

• Inventory turnover• Cycle time• Unit cost of manufacturing• Product capabilty and performance• Conformance to product specifications• On time delivery performance• Fast delivery• Flexibility to change product mix• Flexibility to change volume

33 2008 Karim, Smith, Halgamuge

Australia 40 Advanced Quality Practices/Manufacturing Performance

• Improvement in quality over the previous 2 years• Product capacity utilization• Production yield rate• Customer return rate• On time delivery

34 2009 Fullerton, Wempe USA 121 Lean Manufacturing/Manufacturing Performance/ROS

• Inventory turns• Equipment downtime• On-time delivery• Scrap• Rework• Setup times• Labor productivity• Throughput time• Manufacturing cycle efficiency• Vendor performance - Product Quality• Vendor performance - On-time delivery

35 2009 Qi, Sum, Zhao China 301 Functional involvement & improvement programs/Manufacturing Performance

• Ability to offer reliable products• Having good product/service image• Ability to offer durable products/services• Ability to offer good product/service design• Ability to meet customer specifications• Ability to offer effective after-sale service• Ability to introduce new products to market• Ability to offer a broad product line• Ability to respond to changes in new products• Ability to modify existing products/services• Ability to respond to volume changes quickly• Ability to offer shorter delivery lead time• Ability to meet scheduled due date• Ability to provide low-price products• Ability to produce at low-unit cost

36 2010 Naor, Linderman, Schroeder

Japan, South Korea, Germany, USA, Finnland, Sweden

189 National & Organizational culture/Manufacturing Performance

• Cost

• Quality

• Delivery

• Flexibility

• Inventory turnover• Cycle time• Unit cost of manufacturing• Product capabilty and performance• Conformance to product specifications• On time delivery performance• Fast delivery• Flexibility to change product mix• Flexibility to change volume

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In other words, firms that implement integrated manufacturing strategies outperform those

that do not in terms of manufacturing performance.238 At the same time, studies conclude that

total quality management and just-in-time production were found to have the greatest

impact.239

The impact of these manufacturing concepts on manufacturing performance is rooted mainly

in their positive impact on product design and development and on the fact that they indirectly

improve manufacturing infrastructures, e.g. by fostering targets and discipline within the

organization.240 Lean production is found to contribute to strong manufacturing

performance.241 Surveys conducted also find that process control throughout the

manufacturing system and the supply chain drives success. This process control is supported

by lean manufacturing practices, and a positive correlation to manufacturing has been

established. The same applies for automation: In low- and high-uncertainty environments, the

automation of manufacturing systems will have a significant positive impact on

manufacturing performance.242 With regard to manufacturing flexibility, D'Souza (2006)

concludes that not all elements of manufacturing flexibility drive manufacturing performance

to the same extent. The strongest impact is associated with market mobility flexibility.243

Total productive maintenance and process technology fit were also found to boost

manufacturing performance.244

Another field of relevance to drivers of manufacturing performance is cross-functional

collaboration. Researchers have analyzed the effects of R&D intensity, purchasing

integration, supply chain relationships, financial incentives, activity-based costing, functional

involvement and improvement programs on manufacturing performance. In most cases, cross-

functional collaboration has been found to have a positive effect on manufacturing

performance, leading to the conclusion that the exchange and optimization of information

across a company's functions supports manufacturing performance.245 In one specific case,

Ettlie (1998) concludes that R&D intensity, measured as the proportion of sales spent on

238 Dean Jr./Snell (1996), p. 476;Challis/Samson/Lawson (2005), p. 103; D'Souza (2006), p. 750;

Sakakibara/Flynn/Schroeder/Morris (1997), p. 1256; de Cerio (2003), p. 2781. 239 Challis/Samson/Lawson (2002), p. 1960. 240 Sakakibara/Flynn/Schroeder/Morris (1997), p. 1256; de Cerio (2003), p. 2781. 241 Lowe/Delbridge/Oliver (1997), p. 795. 242 Liao/Tu (2007), p. 48. 243 D'Souza (2006), p. 509. 244 McKone/Schroeder/Cua (2001), p. 52; Das/Narasimhan (2001), p. 539. 245 Ettlie (1998), p. 8-10; Ittner/Lanen/Larcker (2002), p. 724-725; Narasimhan/Das (2001), p. 607;

Fynes/Voss/de Burca (2005), p. 14-15; Cordero/Walsh/Kirchhoff (2005), p. 96-97; Qi/Sum/Zhao (2009), p. 657-658.

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R&D, shows a significant positive correlation to improvements in both market shares and

agility.246 A further important function is purchasing. Research supports the view that

purchasing integration serves as a moderator of purchasing practices and manufacturing

performance.247 One last example is provided by the paper published by Qi et al. (2009), who

found that cross-functional collaboration – especially between human resources, information

systems, research and development and public relations units and manufacturing – drives

manufacturing performance.248

Finally, academics have also tested factors relating to the organization as drivers of

manufacturing performance. As far back as the early 1990s, the factors employee involvement

and employment practices were tested for statistical relevance in relation to manufacturing

performance.249 Later, studies of organizational and national cultures followed.250 Corbett et

al. (1992), for example, conclude that firms experience higher manufacturing performance if

they succeed in anchoring a deeper understanding of goals and strategies in lower levels of the

organization. Similarly, firms can raise performance by attaching importance to workforce-

related programs.251 These basic findings have been confirmed in other studies as well.

Appleyard et al. (2001) reports that involving skilled workers in problem solving under the

leadership of engineers improves manufacturing performance.252 Similarly, Naor et al. (2008)

confirm that organizational culture correlates positively to manufacturing performance.253

Naor et al. (2010) complemented their initial study by adding the construct of national culture

to their investigation. According to their findings, organizational culture affects

manufacturing performance significantly more than national culture. This finding supports the

hypothesis that organizational culture has a powerful influence on performance and should

therefore be managed properly.254

Alongside drivers of manufacturing performance, the question of the existence and potential

mitigation of trade-offs in manufacturing performance constitutes the second line of research.

As shown by Skinner (1966), simultaneously optimizing manufacturing in terms of

246 Ettlie (1998), p. 8. 247 Narasimhan/Das (2001), p. 606. 248 Qi/Sum/Zhao (2009), p. 657. 249 Corbett/Harrison (1992), p. 27-31; Appleyard/Brown (2001), p. 463-465. 250 Naor/Goldstein/Linderman/Schroeder (2008), p. 693-694; Naor/Linderman/

Schroeder (2010), p. 194. 251 Corbett/Harrison (1992), p. 27-31. 252 Appleyard/Brown (2001), p. 463. 253 Naor/Goldstein/Linderman/Schroeder (2008), p. 693. 254 Naor/Linderman/Schroeder (2010), p. 194.

66

production cost, time to market and product quality does not appear to be possible. Skinner

argues that companies are facing pressures from the outside, from the inside and from the

impact of accelerating technology that makes traditional concepts of mass production

obsolete.255 According to him and many scholars who concur in his views, increasing a firm's

competitive strength in one of these dimensions will be at the expense of another.256 In light

of this conclusion, decision-makers must choose which dimension to optimize, as the resultant

trade-off will affect manufacturing performance. Increasingly, however, scholars are now

taking the view that manufacturers can indeed achieve outstanding performance in all three

dimensions at the same time. One of the most prominent proponents of this idea was

Schonberger (1986), who outlined his understanding of world-class manufacturing.257 Later,

numerous academics generally supported his hypotheses by conducting empirical studies.

Ferdows et al. (1990), Mapes et al. (1997) and Filippini et al. (1998), to name but a few, all

claim that the supposedly strict trade-off does not exist.258 Others, such as Boyer et al. (2002),

report evidence in empirical data in support of the arguments of Skinner (1966).259 The issue

remains controversial.

The remaining aspect of relevant literature deals with the impact of manufacturing

performance on overall firm performance. Only a limited number of papers have empirically

tested whether a significant correlation exists. Based on the search parameters used, three

papers were found to have covered this research question. To the knowledge of the author, the

first of these was submitted by Henderson et al. (2004). Based on a sample containing the data

for 1,042 plants in the USA, these authors statistically calculated a positive correlation

between non-financial manufacturing performance and the return on investment.260 Although

a different KPI for firm performance was used by Fullerton et al. (2009), their results confirm

the conclusion drawn by Henderson et al. (2004). The non-financial manufacturing

performance measures setup time, cellular manufacturing and quality correlate positively to

firm performance measured as the return on sales. Accordingly, the initial hypothesis – that

lean methods entail both costs and benefits – is accepted.261 Along the same lines, Qi et al.

(2009) reported a positive link between manufacturing performance and financial 255 Skinner (1966), p. 139-146. 256 Ferdows/de Meyer (1990), p. 169. 257 Schonberger (1986), p. 1-252. 258 Ferdows/de Meyer (1990), p. 181; Mapes/New/Szwejczewski (1997), p. 1031; Filippini/Forza/Vinelli (1998),

p. 3399. 259 Boyer/Lewis (2002), p. 18. 260 Henderson/Swamidass/Byrd (2004), p. 1947. 261 Fullerton/Wempe (2009), p. 228.

67

performance, measuring the latter as the return on investment and the return on sales. In their

conclusion, they emphasized that, according to their sample, manufacturing performance

quality and flexibility were the aspects that had the biggest impact on financial

performance.262

Scholars point more or less unisono to four key elements of manufacturing performance: cost,

quality, delivery and flexibility.263 Alongside these four key elements, however, researchers

have also developed an infinite number of other aspects of manufacturing performance.

Although the terminology sometimes changes from study to study, the underlying measures

remain the same in most cases. The same goes for the designations of the key elements. Slight

differences in the nomenclature should, however, not obscure the fact that, conceptually, these

scholars are referred to the same things. With regard to manufacturing costs, the literature lists

items such as unit production cost, labor cost, capacity utilization, labor productivity and

cycle time.264 The listed items indicating manufacturing quality are conformance to

specifications, defect density, outgoing quality, finished products, first-pass quality yield,

scrap/rework costs and customer satisfaction.265 For delivery, academics have identified

aspects such as on-time delivery, delivery speed, throughput time and compliance with release

dates for new product launches.266 Manufacturing flexibility has been described in terms of

items such as flexibility to change product, flexibility to change volumes, adaptation to

changes in the product mix, the number of product features, and the duration of the fixed

production schedule.267

262 Qi/Sum/Zhao (2009), p. 657. 263 E.g. Naor/Linderman/Schroeder (2010), p. 204; Boyer/Lewis (2002), p. 19. 264 Ferdows/de Meyer (1990), p. 172; Dean Jr./Snell (1996), p. 480; Boyer/Lewis (2002), p. 19;

Naor/Linderman/Schroeder (2010), p. 204. 265 Dean Jr./Snell (1996), p. 480; Appleyard/Brown (2001), p. 457; Mapes/New/Szwejczewski (1997), p. 1027;

Ittner/Lanen/Larcker (2002), p. 714; Challis/Samson/Lawson (2002), p. 1950. 266 Ferdows/de Meyer (1990), p. 172; Bozarth/Edwards (1997), p. 166; Rosenzweig/Roth (2004), p. 367;

Fullerton/Wempe (2009), p. 240; Dean Jr./Snell (1996), p. 480. 267 Richardson/Taylor/Gordon (1985), p. 21; Dean Jr./Snell (1996), p. 480;

Boyer/Lewis (2002), p. 19; Schroeder/Bates/Junttila (2002), p. 117.

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2.5.3.3 A literature review of supply chain performance

A modern supply chain is expected to operate efficiently and effectively.268 Supply chains that

manage to do so in practice are considered to perform at a high level. Numerous scholars have

sought to answer the questions what drives the performance of a supply chain and how can it

be measured. A summary of all identified relevant scholars and the objects of their

investigation is presented in table 8. The main objectives of existing scholars in this field can

be divided into papers that investigate the impact of certain enablers on supply chain

performance and papers that examine the nature of the supply chain as driver for success.

Correlations have been tested in empirical studies with sample sizes ranging from 54 to 760

respondents. The USA is the dominant focal area, followed by emerging Asian countries such

as Taiwan and Malaysia. The research topic is clearly on the rise, however: There were no

empirical studies at all on this subject until 2005. Of late, though, a steady stream of studies

has been published, indicating growing attention to the construct of supply chain

performance.

268 Fawcett/Osterhaus/Magnan/Brau/McCarter (2007), p. 358.

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Table 8: Literature review of supply chain performance

# Year Author Area Sample siObject of investigation Drivers level 1 Drivers level 2

1 2005 Eng UK 112 Cross-functional orientation/supply chain performance

• Customer satisfaction

• Supply chain responsiveness

• Product quality• Delivery performance• Sales, service, and/or technical support• Speed in reacting to customer service problems• Management of distant facilities• Reduce the level or paperwork in a supply chain system• Track shipments• Develop innovative new products/services

2 2007 Forslund, Jonsson

Sweden 136 Forecast information quality/supply chain performance

• Corrective actions

• Preventive actions

• Customer service performance

• Subcontracting• Expediting• Part delivery• Re-scheduling• Reservation breaking• Overtime• Express transports• Safety stock in raw material inventory• Safety stoch in finished goods inventory• Safety capacity• Safety lead time• Over-planning (demand hedges)• Promised lead time• On-time delivery• Rush orders when needed• Promised inventory availability• Accurate orders• Availability of delay information

3 2007 Lee, Kwon, Severance

USA 122 Linkage of supplier, internal integration & customer/supply chain performance

• Cost-containment variables

• Reliability variables

• Reduce inbound cost• Reduce outbound cost• Reduce warehousing cost• Increase in asset turnover• Increase inventory turn• Reduce safety stock• Increase in order fill rate• Reduce order obsolesces

4 2007 Fawcett, Osterhaus, Magnan, Brau, McCarter

USA 588 Connectivity & willingness/supply chain performance

• Operational performance

• Competitive performance

• Responsiveness to customer requests• On-time delivery/due-date performance• Overall customer satisfaction• Cost of purchased items• Profitability• Inventory costs• Order fullfillment lead times• Overall product cost• Productivity• Overall product quality• Transportation costs• Market penetration• Product innovation lead times• Cost of new product development• Sales growth in the last three years• Market share growth in the last three years• Growth in return on assets in the last three years• Overall competitive strength

5 2008 Wagner, Bode Germany 760 Supply chain risk/supply chain performance

• Order fill capacity• Delivery dependability• Customer satisfaction• Delivery speed

6 2009 Liu Taiwan 54 Quality management system/supply chain performance

• Expenses of cost• Assets/utilization• Supply chain reliability• Flexibility and responsiveness

7 2009 Ryu, So, Koo South Korea

141 Buyer-Supplier partnership/supply chain performance

• Reduction of product delivery cycle time• Improvement of productivity such as working relationships […]• Decreasing cost such as operating costs and labor cost• Raising revenue such as the relative market share or return on assets

8 2009 Zelbst, Green Jr., Sower, Reyes

USA 145 Supply chain linkage/supply chain performance

• Zero-defect products to final customer• Value-added services to final customer• Eliminate late, damaged and incomplete orders to final customers• Quickly respond to and solve problems of the final customers• Deliver products precisely on-time to final customers• Deliver precise quantities to final customers• Deliver shipments of variable size on a frequent basis to final customers• Deliver small lot sizes and shipping case sizes to final customers• Minimize total product cost to final customer• Minimize all types of waste throughout the supply chain• Minimize channel safety stock throughout the supply chain

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Table 9: Literature review of supply chain performance (continued)

Concerning enablers that potentially impact a firm's supply chain performance, academics

have identified a cross-functional orientation, the specific dimensions of information sharing,

connectivity, willingness and information exchange at sight. The quality of forecast

information and quality management systems too have been on the agenda of researchers. Eng

(2005) identified a strong positive correlation between cross-functional orientation, consistent

interfunctional cooperation, operational linkages, information sharing, a participative

management style and technology integration on the one hand and supply chain performance

on the other.

When firms nurture a cross-functional orientation, the key is clearly to facilitate and exchange

information flows to optimize both resource dependencies and intra-firm and inter-firm

business relationships.269 The issue of information exchange in supply chains was later taken

up by other academics as well.

Forslund et al. (2007) found evidence that companies with a make-to-stock business model

use their finished goods inventory as a way to cope with forecast uncertainty. As forecast

information quality is limited to certain suppliers, especially further upstream in the supply

chain, this policy triggers preventive action by increasing safety stock levels and thereby

affecting supply chain performance as a whole.270 Fawcett et al. (2007) headed in the same

direction, likewise focusing on information exchange as an enabler of supply chain

performance. According to their findings, companies can manage existing obstacles to

information exchange by focusing simultaneously on the connectivity of information

269 Eng (2005), p. 14. 270 Forslund/Jonsson (2007), p. 104.

# Year Author Area Sample siObject of investigation Drivers level 1 Drivers level 2

9 2010 Ramayah, Omar Malaysia 58 Operational & strategic information exchange/supply chain performance

• Delivery per required date• Time taken to respond to any customer's request• Volume flexibility to fulfill demand upside or downside• Cost related to operating the supply chain

10 2010 Lin, Wang, Yu Taiwan 84 Innovation in channel integration/supply chain performance

• Flexibility of delivery systems to meet the customer needs• Strengthen the supplier partnerships• Cost competitivenes• Shorter order cycles• Flexible customer response

11 2010 Vijayasarathy USA 276 Technology use in supply chain/supply chain performance

• Delivery performance• Order fill rates• Order fullfillment lead times• Out-of-stock situations• Total supply chain management costs• Production flexibility• Inventory days of supply

12 2011 Srinivasan, Mukherjee, Gaur

USA 127 Buyer-Supplier partnership/supply chain performance

• Customer order cycle time performance• Customer order fill rate performance• Customer on time delivery performance• Supplier delivery performance• Inventory turnover performance• Supply chain/logistic costs as a percentage of sales

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technologies and the willingness of the organization.271 On this subject, Ramayah et al. (2010)

emphasized the strategic asset value of information: Availability with minimum delays

encourages tactical and strategic decision-making, which in turn fosters sustainable supply

chain performance. In light of the growing need for confidentiality due to competitive

reasons, relationships of trust throughout the supply chain are, of course, an imperative.272

Superior quality management is also regarded as an enabler of supply chain performance. Liu

et al. (2009) thus described the link between adopting a quality system and supply chain

performance. In line with their hypothesis, their data confirmed a positive impact on supply

chain performance.273

Besides the primarily internal enablers that encourage an organization to maximize its supply

chain performance, the nature and structure of the chain are equally important, as evidenced

by the multiplicity of papers that have examined this issue. The extent to which the entire

supply chain is integrated – from the lowest-tier suppliers through internal stakeholders to end

customers – and the consequences thereof for supply chain performance has been analyzed by

Lee et al. (2007). According to their conclusions, supply chain integration is a key

determinant in improving supply chain performance. Their conclusion is that untapped

opportunities can be realized through supply chain integration involving customers, internal

stakeholders and suppliers.274 These findings were later confirmed by Zelbst et al. (2009),

who examined three supply chain integration variables: power, benefits and risk reduction.

The authors concluded that these variables do indeed drive supply chain performance. More

specifically, Ryu et al. (2009) and Srinivasan et al. (2011) singled out the links between

buyers and suppliers in the upstream supply chain. Their conclusions reflect with the holistic

view: "A buyer-supplier relationship must feature reciprocal trusting behavior and committed

effort".275 Based on the statistical findings, superior performance will be the result. In this

respect, the study conducted by Srinivasan et al. (2011) adds one important note: One reason

why integrated supply chains perform to a higher level is the opportunity to bypass additional

transaction costs as relationship operate based on the "arm's-length principle".276 Regarding

the nature of the supply chain, Wagner et al. (2008) measured the effect of supply chain risk

sources on supply chain performance. They identified a negative correlation between both

271 Fawcett/Osterhaus/Magnan/Brau/McCarter (2007), p. 367. 272 Ramayah/Omar (2010), p. 49. 273 Liu (2009), p. 288-292. 274 Lee/Kwon/Severance (2007), p. 450. 275 Ryu/So/Koo (2009), p. 509. 276 Srinivasan/Mukherjee/Gaur (2011), p. 267.

72

demand- and supply-side risks and supply chain performance. Their results must nevertheless

be kept in perspective, however, as the identified impact is rather low.277 Lastly,

Vijayasarathy (2010) revealed of the role that the moderators process innovation and

partnership quality play in fostering the correlation between the use of technology in supply

chains and underlying performance.278

The question what actions should be taken to improve supply chain performance has been

discussed widely among scholars. However, despite the fact that they have come up with

plenty of ways to operationalize this construct, there is little homogeneity in their findings.

Some authors, notably, have clustered actions in subgroups reflecting certain aspects of

supply chain performance. Examples of these subgroups include "customer satisfaction",

"reliability variables" and "cost-containment" variables.279 Other scholars have preferred not

to do so.280 As mentioned above, a wide variety of aspects has been identified. The following

indicators that reflect the responsiveness and reliability of the supply chain have emerged in

research to date: "increase order fill rate", "reduce order obsolescence", "flexibility and

responsiveness", "quickly respond to and solve problems for end customers", "out-of-stock

situations", "shorter order cycles" and "production flexibility".281 This list is inclusive, not

exclusive. As an incremental aspect of supply chain performance, customer satisfaction has

been found by academic studies to be influenced by items such as "product quality", "delivery

performance", "sales, service and/or technical support", "promised lead time", "on-time

delivery", "availability of delay information", "overall customer satisfaction", "zero-defect to

final customer" and "time taken to respond to any customer's request".282 A selection of

aspects that constitute cost-containment variables in the context of supply chain performance

includes "reduce inbound cost", "reduce outbound cost", "inventory costs", "operating and

277 Wagner/Bode (2008), p. 317. 278 Vijayasarathy (2010), p. 365. 279 Eng (2005), p. 10; Forslund/Jonsson (2007); p. 97; Lee/Kwon/Severance (2007), p. 447;

Fawcett/Osterhaus/Magnan/Brau/McCarter (2007), p. 361. 280 Wagner/Bode (2008), p. 324; Liu (2009), p. 291, Ryu/So/Koo (2009),p. 514; Zelbst/Green Jr/Sower/Reyes

(2009), p. 671; Ramayah/Omar (2010), p. 43; Lin/Wang/Yu (2010), p. 328, Vijayasarathy (2010), p. 370; Srinivasan/Mukherjee/Gaur (2011), p. 266.

281 Lee/Kwon/Severance (2007), p. 447; Liu (2009), p. 291, Zelbst/Green Jr/Sower/Reyes (2009), p. 671; Lin/Wang/Yu (2010), p. 328; Vijayasarathy (2010), p. 370.

282 Eng (2005), p. 10; Forslund/Jonsson (2007); p. 97; Fawcett/Osterhaus/Magnan/Brau/McCarter (2007), p. 361; Zelbst/Green Jr/Sower/Reyes (2009), p. 671.

73

labor costs", "minimize all types of waste throughout the supply chain", "inventory turnover

ratio" and the "cost of new product development".283

2.6 Discussion

Despite the fact that academic research into working capital management has its roots back in

the 19th century, researchers have for decades neglected the importance of short-term assets.

This is perhaps because most managers have focused their attention to long-term financial

decisions – understandably, given the superimposing effects and the associated high sunk

costs of single decisions. Aided and abetted by the financial crisis, however, working capital

is now back on the agenda of most decision-makers, serving as a tool to leverage both cash

and profitability. This attention is all the more pronounced since most assets in manufacturing

companies have short-term maturities. The range of measurements needed to constantly

monitor working capital levels and performance is seemingly infinite. In theory and practice,

however, dynamic measurements such as the cash conversion cycle have prevailed. Their

advantage lies in their value as a basis from which to assess and anticipate future financing

requirements. Pervasive historic static measurements such as the current ratio or the quick

ratio are used nowadays to complement the information gained from flow concepts. This shift

of focus represents a fundamental change of thinking in business management: Companies are

now regarded as going concerns rather than based on liquidation assumptions.

Modern working capital management has to satisfy a number of purposes. First, the operating

cycle – the sequence of cash flows generated by the physical activities of the firm's operations

– must be maintained. Malfunctions in complex interrelations can have serious implications.

Yet, driven by increasing competition, companies cannot simply absorb the costs associated

with excessive buffer stocks in their inventories, for instance. Accordingly, working capital

management has to square the circle of being cost-competitive while also sustaining

operational needs. For managers, the trick is to match this underlying trade-off. A transparent

view of the underlying drivers of working capital requirements and how they interact with

related operational disciplines is needed if this venture is to succeed. This transparency is the

precondition to accurately assess the consequences of so many cogs, gears and levers.

283 Lee/Kwon/Severance (2007), p. 447; Fawcett/Osterhaus/Magnan/Brau/McCarter (2007),

p. 361; Ryu/So/Koo (2009),p. 514; Zelbst/Green Jr/Sower/Reyes (2009), p. 671.

74

To be able to judge working capital decisions in the context of the entire firm, the effect on

firm performance indicators such as ROE or ROA is of relevance too. Scientists have

addressed this link in numerous global studies. With the exception of one study, academics

have so far concluded that the correlation between working capital levels, operationalized as

the cash conversion cycle, and firm performance is negative. Given the high degree of

conformity in existing literature, it is highly likely that working capital management

represents an effective lever to boost firm performance. As outlined above, academics are

expected to develop drivers that will steer working capital and, by consequence, maximize

firm performance. Scholars have risen to the challenge and reviewed several hypothetical

drivers. To date, however, the drivers investigated have been limited to accessible primary

data, as there are virtually no empirical studies that gather secondary data on the operations of

a firm. Scholars have in particular analyzed the impact on working capital levels of certain

KPIs reflecting company characteristics. Size, cash flow and growth rates have been

investigated, to name but a few. On top of KPIs reflecting a company's characteristics,

researchers have also assessed parameters such as industry influence or competitive position

in terms of financing costs or the market-to-book ratio, say.

No less relevant is the question whether related operational disciplines impact requirements

and what effect they have. Past literature reviews have singled out three related operational

disciplines – manufacturing performance, supply chain management and supply risk

management – as researchers in these areas have referenced and emphasized existing

correlations. As an example, academics in the field of supply risk management have explicitly

referred to the function of safety stocks in absorbing supply risk. Further examples linking

other topics to working capital management are presented in the above review.

In manufacturing performance research, theorists have focused to a large extent on existing

trade-offs within the discipline or the effects on performance of integrated manufacturing

technologies such as total quality management. Only three papers have so far addressed the

effect of outstanding manufacturing practice on firm performance. Irrespective of the

comprehensive research question, existing papers have developed a profound

operationalization of manufacturing performance as a general construct. In numerous papers,

different items reflecting different aspects of manufacturing performance have been proposed.

Even so, research on links to specific related operational disciplines remains conspicuous by

its absence. In the context of supply chain performance, research academics have focused to a

large extent on the nature of the supply chain, on enablers and on their impact on overall

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supply chain performance. Statements have also been made about manufacturing

performance, and different constructs have largely backed up the hypotheses and been tested

with certain aspects. Links to manufacturing and risk management, however, remain

uncharted territory. With respect to supply chain risk management, apparently only a handful

set of papers have ever empirically tested the identified hypothesis. Classification mainly

consists of separating risk drivers from risk sources. Risk drivers characterize the exposure of

a supply chain to disruptions, while risk sources identify concrete triggers of supply chain

breakdowns. This separation is crucial for practitioners, as risk can be quantified only if risk

sources are assessed, whereas mitigation strategies must address risk drivers. Research in the

area presented is less mature, although supply chain risk drivers and sources have been the

subject of intensive discussion. Predominantly, literature refers to risk drivers such as global

sourcing, supplier concentration and supplier dependency. Prominent risk sources include

demand-side risks such as volatility and supply-side risks such as supplier default. In addition,

infrastructural risks like machine outages, catastrophic risks and regulatory risks are also cited

frequently.

The Configurational Theory postulates that the alignment of different design parameters in the

organization and its environmental context drives efficiency and effectiveness. According to

the underlying concept of "fit", companies that are not able to properly align organizational

parameters will experience inefficiency and ineffectiveness. Subsuming the above reviews of

working capital management, manufacturing performance, supply chain performance and

supply chain risk management under the Configurational Theory, its key elements postulate

optimized firm performance if all disciplines are aligned and matched with the environmental

context. Working capital, manufacturing and the supply chain must be optimized based on an

approach that integrates a company's environment, e.g. supply risk drivers. Only an integrated

approach covering all disciplines simultaneously will ultimately serve to maximize profit and

competitiveness.

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2.7 Conclusion

A review of the literature revealed three main deficiencies. First, scholars have named and, to

some extent, empirically tested drivers of working capital management. As far as production

system, company characteristics, company environments and industry specifics are

concerned, scholars have developed any number of drivers, including capacity utilization,

company size and market power. Despite the fact that these identified drivers have been tested

empirically, the current list appears to be far from comprehensive. The motivation , in the

opinion of the author, has essentially been pragmatic: Most scholars have focused on primary

data, to which they readily have access on a large scale. Drivers of academic research areas at

the interface to working capital management – areas that undoubtedly affect working capital

levels – have yet to be incorporated in the body of research. These areas are manufacturing

performance, supply chain performance and supply risk management. These, to some extent

only, drive the requirements for working capital levels. Another reason for such poor

treatment to date may lie in the time-consuming process and the potential bias in any survey

of required empirical data. Secondary data is particularly in the context of working capital

difficult to obtain, since hardly any primary data is available on purely operational topics such

as quality performance in manufacturing. The gap in the literature has persisted despite the

fact that postulated links have been stated by some academics. Empirical research is still

missing, however. More surprisingly, while the listed constructs have been operationalized

and empirically tested in numerous papers, the link to working capital management is still

missing. Here again, to the knowledge of the author, no empirical study is yet available.

Research is therefore needed on empirically testing the impact of manufacturing performance,

supply chain performance and supply chain risk management on working capital

requirements.

Second, although scholars have indeed analyzed the correlation of working capital levels,

measured in terms of cash conversion cycle, and overall firm performance, no existing

research traces effects back to specific drivers of working capital management. By

consequence, even though the effects of working capital levels on firm performance have

been interpreted, no study has yet suggested which specific drivers of working capital levels

have what degree of impact. This is a deeply unsatisfactory situation, as practitioners must

aim for a detailed analysis of interdependencies if they are to be able to adjust their

operations. Nor does the missing link between drivers of working capital levels and firm

performance only concern the drivers mentioned explicitly in the literature. In the area of

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manufacturing performance, very limited information has ever been published at all on the

effect on overall firm performance. The author knows of no scholars at all who have

evaluated the putative correlations between supply chain performance or supply chain risk

management and firm performance.

Third, based on the literature review, no model incorporating correlations between

manufacturing performance, supply chain performance, working capital performance, supply

chain risk management and firm performance exists at the present time. As outlined earlier,

the listed constructs are expected to affect each other. Optimization, however, necessitates

integration. Furthermore, to make existing interactions and potential trade-offs transparent, all

constructs must be incorporated simultaneously.

The literature review presented has its limitations with regard to the research engine selected

and the applied search criteria. The search engine used – EBSCO Business Source Premier –

covers the majority of academic publications in economic literature. However, it may not

cover scholars whose work relates to areas such as natural science. Similarly, the literature

review is based on specific word stems that were rated to best fit the purpose of the research

question. The applied word stems were searched for in the title or the abstract. Relevant

scholars may therefore have been overlooked in the study for two reasons. First, theorists may

have used different terms to actually describe the same constructs or correlations. Second,

authors may have not have specified the relevant terms in the title or abstract, despite the fact

that a given work contains relevant statements on the topic.

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3 Driving firm performance based on an integrated operations approach

consisting of manufacturing, supply chain management, working capital

management and supply chain risk steering

3.1 Relevance

The financial crisis recently forced many companies to shift their focus.284 Until this historical

turning point, most managers simply strove for ambitious profitability targets. However, as

sales figures dropped sharply and access to finance became more difficult – irrespective of the

interest rate companies were prepared to pay – ensuring sufficient liquidity became the

primary target of decision makers.285 Working capital management has thus drawn

considerable attention to short-term actions to access liquidity by reducing the level of fixed

capital.286 Despite the prominent reason for this shift, the question about what level of

working capital is appropriate to maintain an optimal long-term equilibrium in terms of

maximum firm performance remains unanswered. There are many different aspects to this

question, though. Since working capital is assigned to maintain a company's operations,

operational performance must be integrated in the research question.287 As outlined in the

literature review, significant gaps in existing literature prevent both theorists and practitioners

from drawing conclusions about existing relationships. As the importance of working capital

management and operational efficiency increases, however, this situation remains

unsatisfactory. Practitioners in particular are expected to possess a profound knowledge of the

relevant drivers, of relationships between them and of their impact if they are to set up

operations in a way that maximizes overall firm profitability. The need for this knowledge is

supported by the Configurational Theory, which postulates that only those firms that properly

align organizational parameters with the environmental context will achieve maximum

performance. At the same time, firms also need to achieve a "fit" between intra-firm operating

parameters and their external context, such as the supply chain set-up. Recent examples have

vividly illustrated existing issues in practice: The earthquake in Japan and the flooding in

Thailand in 2011 forced many firms to temporarily suspend production as their supply chains

collapsed.288 Given that Japan produces about 30% of the flash memory used in the world's

284 Kaiser/Young (2009), p. 64. 285 Matson (2009), p. 28. 286 Kaiser/Young (2009), p. 64. 287 Hill/Sartoris (1988), p. 7-8. 288 Simms (12 March 2012).


79

electronic cameras and smart phones and about 15% of the D-ram memory used in the world's

PCs, firms such as HTC, Apple and Motorola were forced to take impromptu counter

measures to minimize supply shortages for their customers.289 Overall, it appears that the

firms affected by the earthquake were not properly ready for such "black swan events".290

Operations typically were not prepared to cope with this kind of incident.

A model that simultaneously investigated the relationships between manufacturing

performance, supply chain performance, cash conversion period and supply chain risk and

their impact on firm performance would enable critical assessment of net effects and lay the

foundation for an optimization model. Existing studies on small parts of this whole system do

not allow insights to be gained into the counter-effects that prevail when investigating

operations superficially. Irrespective of this general shortcoming, large-scale empirical studies

of the operational drivers listed above are, to the knowledge of the author, decidedly rare.

Various studies already explore the relationships between the cash conversion period and firm

performance. Some even examine the correlation between manufacturing performance and

firm performance. Other than that, however, existing literature still evidences huge gaps with

regard to this topic.291 Furthermore, several operational constructs such as supply chain risk

have not yet been evaluated at all in large-scale empirical studies.292 Interdependencies

between operational constructs and firm performance have so far likewise been neglected by

academics.

This study contributes to operational management literature by simultaneously investigating

the correlations for key drivers of operating performance. The study uses structural equation

modeling to examine the relationships between the operational drivers discussed, as outlined

in figure 2. Both the operational drivers of firm performance and the postulated relationships

are illustrated in the figure. This study will thus contribute valuable insights into

interdependencies between efficient and effective operations and firm success. The impact of

each operational construct on firm performance is shown in isolation.

289 D’Altorio (21 March 2011). 290 Taleb (2010), p. 1-480. 291 Fullerton/Wempe (2009), p. 218; Qi/Sum/Zhao (2009), p. 642. 292 Jüttner (2005), p. 139.

80

The objective of this study is:

• To identify and validate the key operational drivers of firm performance and

• To examine the relationship between the identified operational drivers and their

impact on working capital and firm performance.

The results provide a comprehensive understanding of how the different pieces of the puzzle

that make up a firm's operations relate to each other.

Figure 2: Conceptual model

Manufacturing performance

Supply chainperformance

Cash conversionperiod

Supply chain risk

Firm performance

H1

H2

H3

H4

H5

H6H7

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3.2 Research model and hypotheses

3.2.1 Manufacturing performance

Manufacturing performance is a company's ability to make high-quality products with short

lead times and at low unit costs while maintaining reasonable flexibility.293 A comparatively

low cost base, a short time to market, a reasonable amount of claims regarding delivered

products and a stronger competitive position will directly impact either the firm's sales or

profitability, or both.294 The boost that manufacturing performance gives to firm performance

has already been emphasized on a theoretical basis by Skinner (1969), who states that

outstanding manufacturing performance gives a company "[…] an important addition to its

arsenal of competitive weapons".295 This correlation has been tested and confirmed in

subsequent studies. Cleveland et al. (1989) were the first to empirically investigate the link

between production competence and business performance. Production competence –

described as the fit of production process and business strategy – was found to correlate

positively to business performance, confirming the initial thoughts of Skinner (1969). Vickery

(1991,1993) too confirmed the reported correlation, but criticized aspects of the initial

measures in the conceptual model put forward by Skinner.296 Confirming previous studies, Qi

et al. (2009) found a positive correlation between three distinct manufacturing performance

dimensions – cost, quality and flexibility – and financial performance at Chinese firms.

Flexibility in particular was found to be a strategic weapon, as it apparently serves as an order

winner on the Chinese market. The same applies to quality and cost: Delivering high-quality

products fosters an increasing market share, while the importance of production costs

underscores the need for manufacturing to be able to compete on cost.297 These findings have

been confirmed for other business areas as well. For instance, Henderson et al. (2004) found a

positive relationship between manufacturing performance and the return on investment in US

companies.298 To summarize, then, the void in academic literature suggests a positive

relationship between manufacturing performance, as shown by its emphasis on operational

priorities, and overall firm performance. Operational priorities – quality, cost, time and

flexibility – should transform a firm's strategy into practice. As a consequence, matching

293 Naor/Linderman/Schroeder (2010), p. 199. 294 Cole (2011), p. 30. 295 Skinner (1969), p. 145. 296 Vickery (1991), p. 635; Vickery/Droge/Markland (1993), p. 435. 297 Qi/Sum/Zhao (2009), p. 656. 298 Henderson/Swamidass/Byrd (2004), p. 1943.

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business strategy to production competence should result in superior financial performance.

Quality, a dimension of manufacturing performance, is postulated to be a major factor of

influence on firm performance. However, there is a difference between merely intending to

align manufacturing competence in the future and actual manufacturing performance today.

Manufacturing performance is operationalized by the current level of production capabilities.

The following hypothesis is therefore proposed:

H1: A high level of manufacturing performance correlates positively to firm performance.

Manufacturing performance is postulated not only to correlate positively to firm performance,

but also to drive a firm's supply chain performance. Since a firm rarely has direct control over

its entire value chain, there is indeed a genuine link between the focal firm's operations and its

upstream and downstream supply chain.299 The configuration of the supply chain drives value

creation, with the level of value creation depending on the alignment of intra-firm operations

and external chain links.300 In this respect, manufacturing performance plays a decisive role.

Superior supply chain performance in terms of customer satisfaction, delivery time and

reliability is attainable only if it is backed up by a firm's manufacturing performance. Again,

quality as a distinct dimension of manufacturing performance is assumed to play a major role,

because customer satisfaction and reliable delivery are strongly influenced by compliance

with quality specifications.301 The proposed link is partially supported by other academic

studies. Pohlen et al. (2005), for instance, confirm that competitiveness increases when

internal operations are aligned with the value drivers for suppliers and distributors.302

Similarly, Lee et al. (2007) report a positive internal correlation between supply chain cost

containment and reliability.303 Rajagopal (2010) investigates the effect of TQM adoption and

ISO 9,000 certification on supply chain performance. He reports a positive correlation

between TQM adoption and ISO 9,000 certification and supply chain performance,

concluding that implementation could provide leverage to enhance a firm's supply chain

performance.304 Similar conclusions are drawn by Liu (2009), who affirms a positive

relationship between compliance with ISO/TS 16.949 and supply chain performance, while

299 Chen/Paulraj (2004), p. 120. 300 Pohlen/Coleman (2005), p. 45. 301 Lavastre/Gunasekaran/Spalanzani (2011), p. 832. 302 Pohlen/Coleman (2005), p. 47-48. 303 Lee/Kwon/Severance (2007), p. 448. 304 Rajagopal (2010), p. 12.

83

Pero et al. (2010) indicate that the frequency of replanning frequency and forecasting errors

have a negative effect on supply chain performance.305 Based on these considerations, the

following hypothesis is proposed:

H2: Manufacturing performance correlates positively to supply chain performance.

3.2.2 Supply chain performance

Outstanding supply chain performance in terms of customer satisfaction, delivery time and

reliability positively impacts a firm's competitiveness and customer perceptions of the firm.

Products that are delivered on time and after a reasonable lead time drive both repeat buyers

and market reputation.306 The case for a causative link is therefore compelling: Improved

supply chain performance helps a firm to defend its market position or even increase its

market share. Yet outstanding supply chain performance does not necessarily entail higher

costs. Schonberger (1986) thus concedes that the target for world-class manufacturing

companies must be to simultaneously achieve high quality, short cycle times, flexibility and

low costs.307 Outstanding – i.e. lean – supply chains are expected to operate effectively and

efficiently at the same time. Consequently, superior supply chain performance is assumed to

simultaneously drive an increase in sales and market share while reducing a firm's cost base

and, hence, improving its profitability. The following hypothesis is therefore proposed:

H3: Supply chain performance correlates positively to firm performance.

The level of supply chain performance also affects the level of working capital that is required

to maintain a firm's operating cycle. Punctual and accurate delivery will lower average

inventory levels, as the average time that raw materials and finished products spend in stock

will decrease.308 In addition, punctual delivery to customers will reduce the cash conversion

period, reducing accounts receivable. Superior customer satisfaction and reliable delivery will

also limit returns, reducing the inventory of finished goods as the transfer of ownership is

executed. The need for quarantine stocks will be prevented too. Moreover, satisfied customers

305 Pero/Rossi/Noé/Sianesi (2010), p. 118. 306 Chen/Paulraj (2004), p. 121; Timme/Williams-Timme (2000), p. 36. 307 Schonberger (1986), p. 1-252. 308 Nunn (1981), p. 210.

84

tend to pay outstanding invoices faster, improving the firm's collection period. Effects on

accounts payable are somewhat ambiguous. In theory, a strong supply chain can cope with

longer payable period options, because supply chain members are assumed to maintain greater

financial resources. However, firms that sustain a strong supply chain are more likely to pay

their suppliers on time, unlike primarily cost-conscious firms.309 Thus, the enhanced view

recognizes that the key drivers of the cash conversion period are of operational nature.310 To

summarize the above, the following hypothesis is proposed:

H4: Supply chain performance correlates positively to a firm's cash conversion period.

3.2.3 Working capital management

A positive cash conversion period is the period in the operating cycle that needs to be

financed.311 In effect, it reflects the gap between the time when a firm has to pay for its

purchases and the time when customers pay for its products.312 It thus follows that financing

requirements decrease in the measure that the cash conversion period can be reduced. A

reduced cash conversion period allows managers to substitute relatively unproductive assets

such as accounts receivable for investments in potential high-growth areas to stimulate future

profitability.313 In addition, the liquidity freed up by reduced financing needs lowers the cost

of capital.314 In theory, a reduced cash conversion period will also reduce operating expenses,

leading to increased profitability. Increased inventory turnover rates will drive down the cost

of warehousing, insurance and spoilage, for example. Conversely, faster turnover rates for

accounts receivable will drive investigation, collection and insolvency costs down.315

However, attempts to manage the cash conversion period face a trade-off: If the level of

working capital is reduced too much, operations may begin to flounder. An excessive increase

in inventory turnover ratios will eventually result in stock-outs, ultimately triggering

production outages and late delivery. Similarly a very aggressive accounts receivable policy

will most likely bar customers who depend on credit. On the other hand, running up accounts

payable can expose a firm to the risk of forfeiting early payment discounts and can

309 Deloof (2003), p. 585. 310 Hofmann/Kotzab (2010), p. 309. 311 Churchill/Mullins (2001), p. 137. 312 Hager (1976), p. 19. 313 Jose/Lancaster/Stevens (1996), p. 35; Grinyer/McKiernan (1991), p. 17. 314 Gentry/Vaidyanathan/Lee Hei Wai (1990), p. 90. 315 Kamath (1989), p. 27.

85

boomerang back in the form of a less financially stable supplier base.316 It can therefore also

lead to dependency on individual and/or well-resourced suppliers that demand higher buying

prices.317 These arguments thus imply a negative correlation between the cash conversion

period and firm performance,318 a relationship that has been tested in appropriate studies.

Kamath (1989) finds evidence for a negative correlation between a firm's net trade cycle and

its profitability, though these results do not stand for every year of the study. Soenen (1993)

confirms the initial results, however, though he emphasizes that the identified relationship is

not strong. Several subsequent studies in various business areas mostly affirm the negative

relationship between the cash conversion period and firm performance.319 In light of the

above, the following hypothesis is therefore proposed:

H5: A firm's cash conversion period correlates negatively to its performance.

3.2.4 Supply chain risk

Uncertainty is a key driver of working capital accounts.320 Uncertainties such as short-term

demand changes, a rising number of product variants and changes to product features after the

start of production are all drivers of supply chain risk.321 In other words, the more complex

the supply chain, the greater a firm's exposure to supply chain risk. As a general rule, supply

chains therefore require structures that are able to cope with these challenges.322 Inventory

buffers – internal or with the supplier, central or local – are one way to counter short-term

fluctuations in customer demand.323 Along similar lines, the increasing complexity of product

characteristics requires closer overall alignment of the supply chain.324 A growing number of

product variants, for example, will force firms to increase inventory levels if the same level of

stock per product has to be maintained. The more variants of a product a firm offers, the more

single parts it will have to stockpile in order to maintain a given stock level. Changes to

316 Hofmann/Kotzab (2010), p. 305. 317 Jose/Lancaster/Stevens (1996), p. 35. 318 Hager (1976), p. 21. 319 Jose/Lancaster/Stevens (1996), p.43; Shin/Soenen (1998), p. 43; Wang (2002), p. 168; Deloof (2003), p. 585;

Lazaridis/Tryfonidis (2006), p. 34; Raheman/Nasr (2007), p. 294; Uyar (2009), p. 191. 320 Scherr (1989), p. 3. 321 Christopher/Lee (2004), p. 388; Peck (2005), p. 214-215. 322 Fisher (1997), p. 107; Jüttner (2005), p. 121. 323 Zsidisin/Panelli/Upton (2000), p. 187; Zsidisin/Ellram (2003), p. 18-19; Chopra/Sodhi (2004), p. 54;

Giunipero/Eltantawy (2004), p. 699. 324 Khan/Christopher/Burnes (2008), p. 417.

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product features after the start of production will also most likely drive up inventory levels.

As a result, either raw materials will be outdated but still remain in stock for a designated

period, or production rejects generated in the changeover phase will inflate existing inventory

levels. In all probability firms, will also increase inventory levels before effecting a tool

change to ensure that they are able to continue supplying customers during the changeover.

Obsolete products will probably not be transferred to the market, further increasing inventory

levels.325 Moreover, the dynamic product customization implied by a large number of product

variants, fluctuations in demand and changes in product features will also affect accounts

receivable, leading to late receipt of payment as final customer product acceptance may be a

precondition of payment.326 The kind of responsive supply chain that is mandatory for

customized products requires a financially strong network of suppliers that encourages firms

to pay their suppliers on time.327 In light of the above, the following hypothesis is therefore

proposed:

H6: The greater a firm's supply chain risk, the longer its cash conversion period.

However, a firm should not seek to eliminate all uncertainty. Rather, a measure of exposure to

uncertainty that optimizes returns for the underlying risk level should be maintained.328

Flexible production processes and a short order-to-delivery times are key competitive factors

for companies.329 Offering a large amount of product variants and being able to cope with

either highly volatile demand or product changes after the start of production indicate a

company's capability to satisfy market expectations. Firms that take on and manage these

supply chain risks can thus be expected to satisfy their existing customer base and increase

their market share. Zhang et al. (2007) suggest a positive relationship between product variety

and firm performance, confirming our hypothesis.330 As market share increases, sales and

sales growth are expected to be comparatively higher than for companies with less flexible

production processes.331 Additionally, firms that offer their customers high-value innovative

325 Jüttner/Peck/Christopher (2003), p. 3-4. 326 Hofmann/Kotzab (2010), p. 308. 327 Fisher (1997), p. 108; Franca/Jones/Richards/Carlson (2010), p. 292. 328 Miller (1992), p. 326. 329 Jüttner/Peck/Christopher (2003), p. 197-198; Zsidisin/Ellram (2003), p. 15. 330 Zhang/Chen/Ma (2007), p. 3147. 331 McGrath (2011), p. 96.

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products are in a position to charge higher prices relative to their cost base, resulting in higher

profitability.332 To summarize the above, the following hypothesis is proposed:

H7: A higher supply chain risk correlates positively to firm performance.

The hypothesized correlations will be controlled for the variables of firm size and competitive

intensity to eliminate undesirable sources of variance. Firm size might correlate with firm

performance as larger firms possibly make use of their market power to drive profitability.333

Similarly, financial performance might be driven by the intent to which a firm perceives it's

competition to be intense. Thereto we control our result for the extent to which a firm has to

compete to retain it's market shares.334

3.3 Methodology

3.3.1 Data collection and descriptive statistics

The target firms chosen for this study were manufacturing firms located in Germany,

Switzerland and Austria. These countries were selected due to their homogeneity in terms of

business culture and language. Since the focus of the study is on the relationships between

operational drivers and their impact on firm performance, only manufacturing firms were

included in the study. Service providers such as banks and insurance firms were excluded

from the study. A representative sample of manufacturing firms was obtained from the Dafne,

Hoppenstedt and Thompson database on the basis of industry codes. The initial listing

entailed a sample of 1,687 firms in 19 industry sectors. Executives were contacted a

maximum of three times either via Internet, by mail or by telephone. Primarily, we requested

contacts in the areas of manufacturing, supply chain management, controlling or purchasing

who would be capable of answering the questions on drivers of operating success.

Of the 349 questionnaires that were returned, a total of 274 usable responses were included in

the study, resulting in a response rate of 21 percent. This response rate is above the

recommended thresholds.335 Responses were received across 19 different industry sectors; a

detailed breakdown is presented in table 9. As shown in table 9, the study covers a wide

spectrum of industries, each with differing average sales figures. The majority of respondents

332 Fisher (1997), p. 106. 333 Cao/Zhang (2011), p. 174; Prater/Ghosh (2006), p. 524-525; Ettlie (1998), p. 5; Park/Ro (2011), p. 298. 334 Jaworski/Kohli (1993), p. 61. 335 Malhotra/Grover (1998), p. 414

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had titles similar to CxO/vice president, director/department head, manager or team leader.

Average management experience in their chosen discipline amounted to 13 years. The

respondents' functions were predominantly in controlling, supply chain management/logistics

and procurement. A detailed breakdown of respondents' sales clusters, job titles, management

experience and functions is presented in table 10.

Table 10: Breakdown of participants by industry sector

Industry a n % AVG sales b

Aerospace & Defense 6 2.19 619.00

Automotive & Parts 35 12.77 18,580.33

Chemicals 34 12.41 2,636.33

Construction & Materials 12 4.38 3,862.13

Electricity 2 .73 317.60

Electronic & Electrical Equipment 14 5.11 6,035.83

Food & Beverages 28 10.22 1,679.19

Forestry & Paper 14 5.11 202.88

Household Goods & Personal Goods 3 1.09 665.70

Industrial Metals 22 8.03 853.58

Machinery & Plant Engineering 48 17.52 626.93

Medical Equipment 7 2.55 5,961.43

Mining 1 .36 1,606.25

Oil & Gas 4 1.46 651.47

Pharmaceuticals & Biotechnology 7 2.55 1,271.36

Sports & Luxury Goods 2 .73 290.74

Technology Hardware & Equipment 1 .36 79,883.13

Textiles 8 2.92 1,588.28

Others 26 9.49 5,212.52

Total 274 100.00 4,596.79 a N = 274 b EUR million, average 2007-2009, source: Dafne database

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Table 11: Firm size, respondent's job title and function

Sales clustera Nb %

<100 47 17.15

100-250 91 33.21

250-500 47 17.15

500-1,000 20 7.30

1,000-10,000 44 16.06

> 10,000 25 9.12

Respondent's job title N %

CxO/vice president 17 6.20

Director/department head 88 32.12

Manager 81 29.56

Team leader 38 13.87

Other 50 18.25

Respondent's management experiencec N %

0 – 4 51 18.61

5 – 9 51 18.61

10 – 14 47 17.51

15 – 19 44 16.06

> 20 81 29.56

Respondent's function N %

Corporate development 14 5.11

Controlling 117 42.70

Supply chain management/logistics 71 25.91

Production/manufacturing 13 4.74

Procurement 33 12.04

Other 26 9.49 a EUR million, average 2007-2009; source: Dafne database b N = 274 c Years

An analysis of non-response bias, as suggested by Malhotra et al. (1998) and Chen et al.

(2004), did not indicate any proportional difference between respondents and non-

respondents.336 Following the approach suggested by Lambert et al. (1990) size, performance

and industry cluster distributions of the first wave of respondents were compared with those

of the last wave of respondents,337 where a test for survivorship bias indicated no significant

336 Malhotra/Grover (1998), p. 414; Chen/Paulraj (2004), p. 129. 337 Lambert/Harrington (1990), p. 21.

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differences in sales and profitability figures when comparing the 274 respondent firms with

non-participants.

3.3.2 Questionnaire design and measurement items

3.3.2.1 Questionnaire design

All questions addressing the constructs manufacturing performance, supply chain

performance, supply chain risk and firm performance are based on a five-point Likert scale.

The questionnaire is presented in appendix one. Participants were asked to compare their

employer with their main competitor and assess the firm's relative position.338 The questions

regarding manufacturing performance, supply chain performance, supply chain risk and firm

performance were arranged as multi-item scales. Some academics argue that the goodness of

objective scales are superior to subjective ones. In this study, however, subjective scales were

applied based on the assumption that firms most probably would have been reluctant to

disclose exact and sensitive firm data. A similar argument is proposed by Ward et al.

(2000).339 Especially with regard to a firm's quality performance or supply chain risk

exposure, participants may have felt obliged to stop filling in the questionnaire if they had

been asked for objective data. Having said that, experienced managers who are well-

acquainted with operating performance can reasonably be assumed to provide realistic

assessments.340 General research confirms this assumption, since the subjective evaluations of

survey participants correlate closely to objective performance data.341 To validate the

accuracy and clarity of the questions, the questionnaire was pilot tested on eleven operations

managers. After these pre-test members had submitted the completed surveys, interviews

were arranged to critically review intended meanings and what participants had actually

understood. Every question was discussed during these interviews. Modifications were made

in cases where the participants' understanding did not match the intended meanings and in

cases where questions revealed ambiguous meanings. To encourage participation, a summary

of the research findings was promised to all respondents.

338 Naor/Linderman/Schroeder (2010), p. 204. 339 Ward/Duray (2000), p. 129. 340 Choi/Eboch (1998), p. 63. 341 Dess/Robinson Jr. (1984), p. 271; Venkatraman/Ramanujam (1986), p. 806-812.

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3.3.2.2 Measurement items

Despite the paucity of academic research on correlations for operational drivers outlined in

the first chapter, the constructs manufacturing performance, supply chain performance, cash

conversion period, supply chain risk and firm performance have been operationalized at least

in either theoretical research or empirical studies covering different objects of investigation,

such as the correlation between integrated manufacturing and manufacturing performance.342

Various items from past research were adapted for the purposes of this study and utilized to

cover different constructs.

Realized manufacturing performance is used in this paper to gauge the degree to which a

firm's manufacturing objectives are achieved. The dimension of quality in manufacturing is of

particular interest, with research focused on a firm's capability to deliver high-quality

output.343 Measurement items were adopted from Naor et al. (2010) and supplemented by

items proposed by Challis et al. (2002, 2005), Flynn et al. (1995), Cordero et al. (2005),

Schroeder et al. (2002) and de Cerio (2003).344 A total of four measurement items – "product

capability and performance", "quality consistency", "frequency of customer complaints", and

"defective products in process" – were incorporated in the questionnaire. For each item,

respondents were asked to indicate their firm's relative performance compared to its main

competitor on a five-point Likert scale, where one indicates "much worse" and five indicates

"much better".

To operationalize the construct supply chain performance, three single items – "delivery

time", "delivery reliability" and "customer satisfaction" – were selected. The measures applied

were drawn primarily from a study conducted by Wagner et al. (2008).345 However, the items

used can be found in numerous studies on the topic of supply chain performance

measurement.346 For each single item, the respondents were asked to assess their firm's

relative position compared to its main competitor, again using a five-point Likert scale where

one indicates "much worse" and five indicates "much better".

Unlike the previous construct, cash conversion period performance was measured using

secondary data. In line with the approach suggested by Venkatraman et al. (1986), data for

342 Challis/Samson/Lawson (2002), p. 1941-1964. 343 Tellis/Yin/Niraj (2011), p. 14. 344 Naor/Linderman/Schroeder (2010), p. 204; Challis/Samson/Lawson (2002), p. 1950; Challis/Samson/Lawson

(2005), p. 89; Flynn/Sakakibara/Schroeder (1995), 1360; Cordero/Walsh/Kirchhoff (2005), p. 92; Schroeder/Bates/Junttila (2002), p. 117, de Cerio (2003), p. 2770.

345 Wagner/Bode (2008), p. 324. 346 Ramayah/Omar (2010), p. 43; Srinivasan/Mukherjee/Gaur (2011), p. 266; Forslund/Jonsson (2007), p. 97-98.

92

financial indicators such as the cash conversion period can be retrieved from secondary

sources provided that data on financial performance is not be revealed by the participants due

to confidentiality issues.347 On the other hand, utilizing primary data for operational indicators

seems to be appropriate, since broader conceptualization may be required to address specific

research questions. The measurement item used for cash conversion period performance is the

related indicator net trade cycle. The net trade cycle was favored over the cash conversion

cycle as a measure of the cash conversion period because studies confirm that the net trade

cycle provides similar information to that provided by the cash conversion cycle, but the

required data points for calculating the net trade cycle are more readily accessible.348 In line

with our objective, other studies too use the net trade cycle to assess a firm's cash conversion

period.349 Secondary data from participants firms for the years 2007 to 2009 was obtained

from the Dafne and Thompson database and served as the basis for calculation of the net trade

cycle. Table 11 presents the sample size and average net trade cycle days for each industry

cluster.

Table 12: Average NTC per industry cluster

Industry cluster a N % AVG NTC b

Engineered products 75 27.37 81

Electrical equipment 21 7.66 78

Process industry 88 32.12 68

Consumer goods 55 20.07 56

Automotive 35 12.77 55

Total 274 100.00 69 a N = 274 b Days, average 2007-2009; source: Dafne database

The items selected to depict the construct supply chain risk are the "number of product

variants", "unanticipated or very volatile demand" and "changes in product design and

production process". The measurement items reflect triggers for required adoptions by a firm's

supply chain, as the product characteristics directly impact complexity in terms of product

variants or demand patterns. Selected measurement items were drawn from studies conducted

347 Venkatraman/Ramanujam (1986), p. 806. 348 Kamath (1989), p. 26. 349 Soenen (1993), p. 54-55; Shin/Soenen (1998), p. 38-39.

93

by Chopra et al. (2004), Spekman et al. (2004) and Wagner et al. (2008).350 For each single

item, participants were asked to assess their firm's relative position compared to its main

competitor using a five-point Likert scale, where one indicates "much lower" and five

indicates "much higher".

Measurement items selected for the dependent variable firm performance constitute a mix of

financial indicators – prior work on the measurement of firm performance is extensive. In

particular, the items "sales", "sales growth", "market share" and "profitability" are utilized to

reflect overall firm performance. Previous studies utilize similar items to depict the construct

firm performance.351 For each single item, participants were asked to assess their firm's

relative position compared to its main competitor using a five-point Likert scale, where one

indicates "much worse" and five indicates "much better".

3.3.3 Construct reliability and validity

To identify which constructs should be part of the structural equation model, an exploratory

factor analysis (EFA) was conducted. Hereby the four constructs manufacturing performance,

supply chain performance, supply chain risk and the control variable competitive intensity

were included in the model. A small number of missing data points were replaced by the

means. Using principal component factor analysis with varimax rotation, four constructs

emerged: firm performance, manufacturing performance, supply chain performance and

supply chain risk. Items with cross-loadings and items with loadings smaller than .50 were

excluded from the analysis.352 Similarly, factors that did not load on the constructs as

anticipated were eliminated.353 Provided that all loadings exceed a value of .50, discriminant

validity is supported, suggesting that the above factor analysis was used appropriately. Details

of the factor analysis for the selected items are shown in table 12. Table 13 lists the five

reflective constructs of the model together with their correlation coefficients, means and

standard deviations. In the upper left part of the table, the squared correlation coefficients are

listed. To assess the internal consistency of the four constructs, Cronbach's α is used.354 In

350 Chopra/Sodhi (2004), p. 54; Spekman/Davis (2004), p. 419-420; Wagner/Bode (2008), p. 323. 351 E.g. Voss/Voss (2000), p. 69; Kaynak (2003), p. 431-432; Capon/Farley/Hoenig (1990), p. 1149;Huselid

(1995), p. 652; Zajac (1990), p. 225; McGuire/Sundgren/Schneeweis (1988), p. 1988;Megginson/Nash/van Randenborgh (1994), p. 422; Ruf/Muralidhar/Brown/Janney/Paul (2001), p. 149; Greenley (1995), p. 5.

352 Bagozzi/Yi (1988), p. 82. 353 Chen/Paulraj (2004), p. 129. 354 Cronbach (1951), p. 297-334.

94

line with Flynn et al. (1990), existing constructs should maintain values higher than .70, while

newly compiled constructs should maintain values above .60.355

355 Flynn/Sakakibara/Schroeder/Bates/Flynn (1990), p. 266.

Tab

le 1

3: R

otat

ed c

ompo

nent

mat

rixa

Item

F

irm

per

form

ance

(F

P)

Man

ufac

turi

ng

perf

orm

ance

(M

P)

Supp

ly c

hain

pe

rfor

man

ce (

SP)

Supp

ly c

hain

ri

sk (

SR)

Com

peti

tion

in

tens

ity

(CI)

(1

) Sa

les

(FP

1)

.807

.0

57

.030

.1

58

-.07

0

(2)

Sale

s gr

owth

(FP

2)

.597

.0

50

.302

-.

034

-.06

1

(3)

Mar

ket s

hare

(F

P3)

.7

80

.036

.0

49

.230

-.

191

(4)

Pro

fita

bilit

y (F

P4)

.7

13

.217

.0

12

-.08

0 .0

31

(5)

Pro

duct

cap

abil

ity

and

perf

orm

ance

(M

P1)

.0

86

.683

.0

87

.243

-.

145

(6)

Qua

lity

cons

iste

ncy

(MP

2)

.072

.7

65

.098

.2

14

-.05

1

(7)

Cus

tom

er c

laim

s (M

P3)

.0

23

.750

.2

02

-.04

4 .0

29

(8)

Def

ectiv

e pr

oduc

ts in

pro

cess

(M

P4)

.1

60

.675

.0

11

-.09

2 -.

030

(9)

Lea

d tim

e (S

P1)

.0

22

.005

.8

10

.059

.0

83

(10)

Sup

ply

relia

bilit

y (S

P2)

.0

69

.138

.7

78

.005

.0

42

(11)

Cus

tom

er s

atis

fact

ion

(SP

3)

.230

.2

64

.710

.0

52

-.04

6

(12)

Num

ber

of p

rodu

ct v

aria

nts

(SR

1)

.083

.1

64

-.00

8 .7

58

.052

(13)

Fre

quen

cy o

f de

man

d ch

ange

s (S

R2)

.1

20

-.01

7 .2

25

.721

.0

36

(14)

Pro

duct

mod

ific

atio

ns (

SR

3)

-.00

2 .0

54

-.08

6 .7

58

-.12

4

(15)

Cut

thro

at c

ompe

titi

on (

CI1

) -.

122

-.03

3 -.

016

.002

.7

32

(16)

Hig

h pl

agia

rism

(C

I2)

.023

-.

152

-.03

9 -.

058

.722

(17)

Hig

h pr

ice

com

petit

ion

(CI3

) -.

108

.041

.1

47

.013

.7

36

N =

274

; a Rot

atio

n co

nver

ged

in s

ix it

erat

ions

; Ext

ract

ion

met

hod:

Pri

ncip

al C

om p

onen

t Ana

lysi

s; R

otat

ion

met

hod:

Kai

ser

Nor

mal

izat

ion

95

96

Table 14: Component transformation matrix

Construct Items 1 2 3 4 M SD

1. Firm performance 4 .570 .578 .433 .341 3.1241 .7586

2. Manufacturing performance 4 -.333 .206 .615 -.203 3.5000 .4860

3. Supply chain performance 3 -.600 .419 -.263 .629 3.4221 .5373

5. Supply chain risk 3 .235 -.582 .123 .661 3.2819 .6781

6. Competition intensity 3 .387 .330 -.592 -.104 3.4173 .5464

N = 274; Extraction method: Principal Component Analysis; M is the mean; SD is the standard deviation; Rotation method: Kaiser Normalization

In the study, the Cronbach's alpha coefficients for the newly defined constructs are as follows:

firm performance (α = .74), manufacturing performance (α = .73), supply chain performance

(α = .71), supply chain risk (α = .65) and competition intensity (α = .60).

All values are higher than the critical value of .60 as suggested by Flynn et al. (1990). The

reliability of the study constructs is assumed. The measurement model using the items

resulting from the EFA was tested using confirmatory factor analysis (CFA) to ensure

construct validity, as suggested by O'Leary-Kelly et al. (1998) and Gerbing et al. (1988).356

The results of the CFA are presented in table 14, which lists the relevant fit criteria for the

five reflective constructs. The statistical software AMOS 16.0 was used to validate the items

of the constructs firm performance, manufacturing performance, supply chain performance

and supply chain risk. Composite reliabilities and average variances for all constructs meet

the thresholds of .70 and .50 suggested by Nunnally et al. (1994) and Bagozzi et al. (1988)

respectively.357 All measurement items for the emerged constructs show high factor loadings

significant at ρ < .001 suggesting item validity.

356 O'Leary-Kelly/Vokurka (1998), p. 387-405; Anderson/Gerbing (1988), p. 418-421. 357 Nunnally/Bernstein (1994), p. 264-265; Bagozzi/Yi (1988), p. 74-94.

Tab

le 1

5: E

valu

atio

n of

ref

lect

ive

cons

truc

ts

Con

stru

cts

and

item

s C

ronb

ach

alph

a T

otal

va

rian

ce

Com

mon

-al

ities

It

em-t

o-to

tal

corr

elat

ion

Com

posi

te

relia

bilit

y A

VE

Fa

ctor

lo

adin

g t v

alue

SE

IR

Firm

Per

form

ance

(FP

) 0.

741

0.61

9 0.

870

0.49

2

FP 1

0.

693

0.65

5 0.

832

-a -b

0.73

0

FP 2

0.

391

0.17

5 0.

626

6.38

0 0.

064

0.40

8

FP 3

0.

677

0.69

8 0.

823

11.0

35

0.09

4 0.

739

FP 4

0.

491

0.24

0 0.

701

7.51

6 0.

072

0.46

1

Man

ufac

turi

ng P

erfo

rman

ce (

MP

) 0.

728

0.19

1 0.

842

0.49

0

MP

1

0.55

3 0.

467

0.74

4 -a

-b 0.

644

MP

2

0.65

5 0.

609

0.80

9 9.

005

0.12

9 0.

694

MP

3

0.56

7 0.

351

0.75

3 7.

896

0.11

8 0.

523

MP

4

0.43

3 0.

220

0.65

8 6.

471

0.10

6 0.

390

Supp

ly C

hain

Per

form

ance

(S

P)

0.71

3 0.

182

0.82

5 0.

643

SP 1

0.

626

0.38

4 0.

791

-a -b

0.57

0

SP 2

0.

674

0.49

0 0.

821

7.48

2 0.

151

0.62

9

SP 3

0.

606

0.48

3 0.

778

7.47

5 0.

147

0.63

0

Supp

ly C

hain

Ris

k (S

R)

0.65

3 0.

281

0.81

7 0.

683

SR 1

0.

620

0.48

5 0.

788

-a -b

0.59

1

SR 2

0.

559

0.33

4 0.

748

6.08

2 0.

119

0.61

1

SR 3

0.

595

0.34

9 0.

772

6.11

3 0.

121

0.62

3

Not

e. A

ll ite

ms

wer

e m

easu

red

on f

ive-

poin

t L

iker

t ra

ting

scal

es. S

E i

s th

e st

anda

rd e

rror

fro

m t

he u

nsta

ndar

dize

d so

lutio

n. A

VE

is

the

aver

age

vari

ance

ext

ract

ed. I

R i

s th

e in

dica

tor

reli

abil

ity

(For

nell

and

Lar

cker

, 198

1).

a t

valu

es a

re f

rom

the

unst

anda

rdiz

ed s

olut

ion;

all

are

sign

ific

ant a

t the

0.0

01 le

vel (

two-

tail

ed).

b F

acto

r lo

adin

g w

as f

ixed

at 1

.0 f

or id

enti

fica

tion

pur

pose

s.

79

98

Tab

le 1

4: E

valu

atio

n of

ref

lect

ive

cons

truc

ts (

cont

inue

d)

Con

stru

cts

and

item

s C

ronb

ach

alph

a T

otal

va

rian

ce

Com

mon

-al

ities

It

em-t

o-to

tal

corr

elat

ion

Com

posi

te

relia

bilit

y A

VE

Fa

ctor

lo

adin

g t v

alue

SE

IR

Com

petit

ion

inte

nsit

y 0.

595

0.27

7 0.

711

0.48

1 -a

-b

CI

1 0.

559

0.33

3 0.

832

0.52

7

CI

2 0.

517

0.27

5 0.

626

4.92

7 0.

231

0.38

5

CI

3 0.

569

0.36

4 0.

823

4.94

9 0.

235

0.47

9

Not

e. A

ll ite

ms

wer

e m

easu

red

on f

ive-

poin

t L

iker

t ra

ting

scal

es. S

E i

s th

e st

anda

rd e

rror

fro

m t

he u

nsta

ndar

dize

d so

lutio

n. A

VE

is

the

aver

age

vari

ance

ext

ract

ed. I

R i

s th

e in

dica

tor

reli

abil

ity

(For

nell

and

Lar

cker

, 198

1).

a t

valu

es a

re f

rom

the

unst

anda

rdiz

ed s

olut

ion;

all

are

sign

ific

ant a

t the

0.0

01 le

vel (

two-

tail

ed).

b F

acto

r lo

adin

g w

as f

ixed

at 1

.0 f

or id

enti

fica

tion

pur

pose

s.

98

99

According to Hair et al. (1995) and Hu et al. (1998), the recommended fit criteria for CFI and

IFI is .90 or higher.358 An RMSEA value below .08 is acceptable according to academic

literature. Some scholars indeed claim that a value of only less than .05 is acceptable.359 The

model fit values achieved in this study were RMSEA = .052, Chi-square divided by the

degrees of freedom (χ²/df) = 1.745, CFI = .917 and IFI = .919. The root mean square error of

approximation (RMSEA) indicates a reasonably good fit, with .052 and a 90 percent

confidence interval ranging from .040 to .064, as well as a p value of .369 for a test of close

fit (RMSEA < 0.05) (Browne & Cudeck, 1993). The comparative fit index (CFI = .917) and

incremental fit index (IFI = .919) (Bollen et al., 1989) are evaluated based on their closeness

to 1, where a value > .9 indicates good fit.360 Overall, both the CFI and the IFI indicate a

good relative fit compared to the independence model. The results of the other fit criteria

likewise indicate very good fit measures, as the recommended thresholds are met in all cases.

At 81,190 and a 90 percent confidence interval of 46,745 to 123,496, the estimated non-

centrality parameter (NCP) is rather low, again indicating a good fit. The expected cross-

validation index (ECVI) for the postulated model is 1,019 with a 90 percent confidence

interval of .893 to 1,174. The ECVI is 1,121 for the saturated model and 4,202 for the

independence model. In light of the ECVI, the postulated model indicates a good fit compared

to the two alternative models. The root mean square residual (RMR) of .036 and the goodness

of fit index (GFI) of .925 all indicate a good absolute fit (Jöreskog & Sorbom, 1984). Overall,

we find that all goodness of fit indices point to a reasonably well fitting model and assume the

model to represent the data structure fairly well.

Convergent validity is also ensured. All factor loadings for selected items in the confirmatory

factor analysis model are greater than .50, and the t values are greater than 2.0. As such,

multiple attempts to measure the same constructs are expected to produce the same results.

The results of the CFA also permit an assessment of discriminant validity. To ensure that

individual constructs are discrete, the unconstraint model was compared with the constraint

model used in the study.361 A high delta of χ² between the two models is an indicator of high-

discriminant validity.362 The results of the CFA confirm discriminant validity, as the

differences in χ² are significant. Table 15 shows both inter-construct correlations and squared

358 Hair/Anderson/Tatham (1990), p. ; Hu/Bentler (1998), p. 449. 359 Byrne (2010), p. 176; Kline (2011), p. 205-207. 360 Bollen/Long (1993), p. 36. 361 Bagozzi/Yi/Phillips (1991), p. 448-451 362 Choi/Eboch (1998), p. 64-66

100

correlations. Since all measures meet commonly accepted thresholds, the convergent and

discriminant validity of the reflective constructs is assumed.

To ensure content validity two main standards, a representative collection of items and

sensible methods for test construction have to be ensured.363 Thereto items for the different

constructs were used that had already been successfully tested in the relevant literature. A

comprehensive literature review was conducted to identify common measurement items for

the various constructs. The items were selected based on the literature review. Additionally,

expert interviews were conducted in preparation for the survey and during the pre-test. Before

the questionnaire was distributed, senior experts were asked to evaluate and review the

completeness of the selected measurement items. Pre-test members were also asked to

scrutinize selected measurement items for their appropriateness.

To address common method bias, primary and secondary data were combined. Since the

construct cash conversion period is based on objective secondary data, concerns about

common method bias can be discarded.364

363 Naor/Linderman/Schroeder (2010), p. 199 364 Craighead/Ketchen Jr./Dunn/Hult (2011), p. 583

Tab

le 1

6: D

escr

ipti

ve s

tati

stic

s an

d va

riab

le c

orre

lati

ons

Var

iabl

e M

ean

SD

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(1

1)

(12)

(1

3)

(14)

(1

5)

(16)

(1

7)

(1)

Prod

uct c

apab

ility

and

per

form

ance

(M

P1)

3.74

45

.640

85

.564

.3

47

.275

.1

96

.161

.1

86

.055

.1

81

.280

-.

122

-.19

1 -.

030

.177

.1

22

.193

.1

65

(2)

Qua

lity

cons

iste

ncy

(MP2

) 3.

6204

.6

5332

.5

64

.454

.3

29

.244

.1

25

.149

.1

36

.145

.3

03

-.06

6 -.

142

-.00

9 .2

06

.103

.1

54

.151

(3)

Cus

tom

er c

laim

s (M

P3)

3.40

88

.685

32

.347

.4

54

.431

.1

12

.051

.0

15

.156

.2

46

.299

.0

06

-.08

1 -.

003

.082

.1

35

.067

.1

47

(4)

Def

ecti

ve p

rodu

cts

in p

roce

ss (

MP4

) 3.

2263

.6

4043

.2

75

.329

.4

31

.088

.0

43

.014

.0

87

.111

.1

73

-.11

7 -.

089

-.02

6 .0

88

.137

.1

38

.248

(5)

Num

ber

of p

rodu

ct v

aria

nts

(SR

1)

3.61

68

.762

59

.196

.2

44

.112

.0

88

.384

.4

19

.063

.0

65

.086

-.

020

-.02

6 .0

05

.157

.0

48

.211

.0

59

(6)

Freq

uenc

y of

dem

and

chan

ges

(SR

2)

3.35

40

.664

82

.161

.1

25

.051

.0

43

.384

.3

58

.185

.0

96

.215

.0

03

-.04

7 .0

44

.166

.1

09

.234

.0

58

(7)

Prod

uct m

odif

icat

ions

(SR

3)

3.29

56

.665

90

.186

.1

49

.015

.0

14

.419

.3

58

-.02

9 -.

011

.005

-.

091

-.07

9 -.

096

.079

.0

55

.120

.0

35

(8)

Lea

d ti

me

(SP1

) 3.

2664

.6

8887

.0

55

.136

.1

56

.087

.0

63

.185

-.

029

.484

.4

10

.039

.0

50

.128

.0

72

.205

.0

77

.045

(9)

Supp

ly r

elia

bilit

y (S

P2)

3.41

24

.691

12

.181

.1

45

.246

.1

11

.065

.0

96

-.01

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101

102

3.4 Results of the structural equation model

All constructs were tested for correlations based on the following model:

��

��

��

��

�

��

��

��

��

��

��

��

��

��

��

��

��

� ��

��

��

��

��

�

With:

�� Firm performance

� Correlation coefficient

�� Size

� Competitive intensity

�� Manufacturing performance

�� Supply chain performance

� � Net trade cycle

�� Supply chain risk

�� Residual

where �� controls for the firm size and

� for the competitive intensity of each firm ��.

Figure 3 depicts the identified drivers of firm performance. Correlation coefficients, f values

and coefficients of determination have been added. Table 16 documents the standardized

coefficients, t values and adjusted R² for the structural equation model correlations. All the

correlations are significant (t > 1.96) at the .90 significance level.365 Most of the correlations

are even significant at the .99 level. All path estimates show high values; most exceed the .20

level.366 The results thus provide empirical support for all our hypotheses. The adjusted R²

values range from .011 to .094 and are in a similar range as have been found by comparable

previous studies.367

365 Chin (1998), p. 8. 366 Chin (1998), p. 13. 367 Singer/Donoso/Rodríguez-Sickert (2008), p. 499; Lanier Jr./Wempe/Zacharia (2010), p. 11.

103

Figure 3: Results of the structural equation model

Notes: N = 274. Measurement models are estimated using ML. * ρ < 0.10, ** ρ < 0.05, ***ρ < 0.01. Model fit indices: χ², 190.190; degrees

of freedom, 109; ρ, .000; IFI, 0.919; CFI, 0.917; RMSEA, 0.052; AIC, 278.190 (saturated model, 306.00; independence model, 1147.21)

Table 17: Direct effects of the structural equation model

Construct Hypothesis Standardized

coefficient

t values Adjusted R² Hypothesis

validated

MP → FP 1 .258 4.398*** .066 Yes

MP → SP 2 .307 5.317*** .094 Yes

SP → FP 3 .261 4.465*** .068 Yes

SP → NTC 4 -.156 -2.601** .024 Yes

NTC → FP 5 -.106 -1.752* .011 Yes

SR → NTC 6 .213 3.598*** .045 Yes

SR → FP 7 .199 3.351*** .040 Yes

Notes: N = 274. Measurement models are estimated using ML. * ρ < 0.10, ** ρ < 0.05,*** ρ < 0.01.

Table 17 shows the coefficients of determination for the dependent constructs in the model.

Results have been controlled for size and competitive intensity. Yet, neither size nor

competitive intensity has any statistically significant influence on the results presented above.

Manufacturing performance (MP)

Supply chainperformance (SP)

Supply chain risk(SR)

Net trade cycle(NTC)

Firm performance(FP)

.258***

.261***

-.106*

.199***.213***

-.156**

.307***

104

Table 18: Coefficients of determination

Independent variable Dependent variable R²

Manufacturing performance (MP) Firm performance (FP) .157

Supply chain performance (SP)

Cash conversion period (CP)

Supply chain risk (SR)

Manufacturing performance (MP) Supply chain performance (SP) .115

Supply chain risk (SR) Cash conversion period (CP) .081

3.5 Discussion

Drivers of firm performance

Results suggest that manufacturing performance, supply chain performance, cash conversion

period, and supply chain risk all correlate positively to firm performance at the ρ < .010

significance level. Companies that are able to achieve high levels for these operational drivers

were found to outperform firms that do not. Previous studies have already found that

manufacturing performance which focuses on high-quality output boosts firm performance.368

Efficient and effective production operations help firms to operate at a comparatively low cost

base. However, merely maintaining a comparatively low cost base neglects top-line effects:

Ensuring high-quality output and state-of-the-art technology is critical to generate sales and

increase market share.369 The results of the study thus suggest that firms with effective and

efficient manufacturing achieve higher sales and sales growth figures as well as reaching

higher profitability levels due to their competitive cost base.

The fact that these firms also claim larger market shares is a logical conclusion of such above-

average sales growth performance. The need for high-quality performance is especially

relevant for the European firms in the study, as their customers demand high quality

standards. These results support H1, which postulates a positive correlation between

manufacturing performance and firm performance. The effect of an enhanced supply chain

likewise appears to be critical in affecting firm performance. The results of the study imply

that a strong supply chain with a good fit supports a firm's competitive position. Fast delivery

and reliable delivery were found to be key enablers in driving firm performance. It follows

368 Qi/Sum/Zhao (2009), p. 657; Tellis/Yin/Niraj (2011), p. 14. 369 Cole (2011), p. 30.

105

that firms that achieve a high degree of customer satisfaction with regard to their supply chain

outperform those that do not.

The results further support the proposition that European-based companies must guarantee

(and keep to) due dates and must be able to respond flexibly to customer requests. This

assertion is all the more valid because recent trends such as on-time delivery concepts and

reduced safety stock levels necessarily require smooth supply processes. Acceptable levels of

supply chain disruptions will nevertheless keep customers satisfied, triggering an inflow of

orders from both the existing customer base and from new customers. Supply chain

performance is not the exclusive preserve of the downstream supply chain, however.

Reliability and fast delivery in the upstream supply chain too fosters intra-firm process

efficiency and effectiveness. Aligned and reliable supply processes enable firms to apply lean

concepts and reduce buffers (such as safety stocks) in the supply chain. Firms that

successfully apply such concepts will ultimately reduce their cost base. These results thus

support hypothesis H3.

The effect of a firm's cash conversion period on firm performance has been investigated by a

handful of academics.370 The results of this study reinforce the conclusions drawn by existing

literature. Based on the statistical outcomes, a negative correlation exists between the cash

conversion period and firm performance. Having substantial liquidity tied up in assets such as

accounts receivable and inventories can be a burden on a firm that finds its resources invested

in comparatively unproductive assets. A sudden drop in sales figures, for example, could

force firms to tie up excess capital at the expense of profitable operations if inventory

mismanagement accompanies the decline in sales.371 For this reason, firms that manage to

keep to the required minimum levels of accounts receivable, inventories and accounts payable

can be expected to perform better. The above arguments support H5, which postulates a

negative correlation between the cash conversion period and firm performance.

The correlations presented in figure 3 imply that supply chain risk correlates positively to firm

performance. Based on these results, European-based firms ought to operate at reasonable

supply chain risk levels in order to successfully compete on the market. Several of today's

customers' expectations with regard to product characteristics drive the complexity and

fragility of supply chains.372 More and more customers are demanding customized products,

370 Soenen (1993), p. 57; Jose/Lancaster/Stevens (1996), p. 33; Shin/Soenen (1998), p. 43; Wang (2002), p. 168. 371 Lazaridis/Tryfonidis (2006), p. 35. 372 Christopher/Lee (2004), p. 388.

106

leading to a large number of product variants.373 However, the rising number of product

variants increases the work involved in managing variants, the number of interfaces and

exposure to supply chain disruptions.374 Similarly, customers are growing accustomed to ever

shorter lifecycles and faster times to market. As a consequence, modifications to the

production series in place are forcing firms to operate more flexible supply chains that can

quickly handle product changes. Today's customers expect short order lead times too. More

and more fluctuations in demand are thus making it more and more difficult for firms to plan

reliably. This erosion of planning reliability in turn places heavier demands on the supply

chain. However, since customers expect products that necessitate a certain level of supply

chain risk, and since their order habits reflect this insistence, only those firms who do indeed

take such risks can achieve above-average performance. These findings support the

hypothesis H7.

Drivers of supply chain performance

Manufacturing performance can be considered to be an integral part of a firm's supply chain.

Its impact on supply chain performance should therefore not come as a surprise. As implied

by the results of this study, manufacturing performance correlates positively to supply chain

performance. The less customer complaints or quality issues arise from production failures,

the less a supply chain will have to deal with unexpected situations. Conversely, unexpected

situations that have to be dealt with drive supply chain complexity and reduce performance in

terms of fast and reliable delivery. The results of the study therefore seem to confirm H2.

Drivers of the cash conversion period

As indicated by the results of this study, supply chain performance correlates negatively to the

cash conversion period. The better the performance of a firm's supply chain, the less time the

firm needs for its cash conversion period. Supply chains that achieve fast delivery and supply

goods both on time and to the required quality level will most likely also reduce cash cycle

times. The faster products are delivered, the shorter the time it takes to cash in on assets such

as raw materials and finished products. Also, satisfied customers tend to settle outstanding

invoices more quickly than customers who have lodged claims for rework or replacement. As

373 McGrath (2011), p. 96. 374 Cole (2011), p. 33.

107

expected, therefore, hypothesis H4 is supported by the stimulatory effect of supply chain

performance on the cash conversion period.

The results of the study point to a positive correlation between supply chain risk and a firm's

cash conversion period. A firm's supply chain exposure stimulates longer cash cycles,

indicating a rising need for financing to fund the operating cycle. It is postulated that an

increased number of product variants or modifications will drive inventory levels, since stock

levels inevitably rise if safety stocks per product are kept constant. Operating on markets that

experience pronounced fluctuations in demand puts pressure on stock levels as demand peaks

still have to be covered. Not only physical complexity increases, however: Rising complexity

also hampers administrative effectiveness. This can, for example, negatively impact collection

processes, as varying product specifications have to be assimilated. Hypothesis H6 is

therefore supported by the results of the study.

Trade-off: firm performance versus the cash conversion period

The results generated by the structural equation model suggest a trade-off between firm

performance and the cash conversion period. Supply chain risk stimulates firm performance,

as a significant positive correlation exists with a correlation coefficient of .199. Accordingly,

the supply chain risk level is another key operational success factor for firm performance,

alongside manufacturing performance, supply chain performance and the cash conversion

period. At the same time, supply chain risk also drives longer cash conversion periods. This

hampers firm performance, given that the length of the cash conversion period was identified

as a key success driver too. The results of the model imply correlation coefficients of .213 for

the regression "supply chain risk and cash conversion period" and -.106 for the regression

"cash conversion period to firm performance". The direct positive effect of supply chain risk

on firm performance (.199) is superposed by the indirect effect via the cash conversion period

(-.023). The effect still has to be considered by decision-makers, however, as the results

reveal that higher levels of firm performance require longer cash conversion periods too.

Realized new market potential or cost base reduction is accompanied by greater exposure to

supply chain risks, which in turn translates into longer cash conversion periods. Yet, to make

this clear, this should not be regarded as invitation to solely increase working capital levels in

case of an increased supply chain risk exposure.375 In light of these assumptions, stand-alone

375 Tilston (2009), p. 57.

108

initiatives to reduce either working capital levels or cash conversion periods do not seem

appropriate. Rather, firms should set up cross-functional teams that evaluate the optimum for

the firm as a whole instead of optimizing individual functions.376

The study focuses on operational success drivers that impact either the physical production of

goods, the supply of raw materials and intermediate products or the distribution of finished

products. The model setup and its regressions build to some extent on existing academic

research. In particular, scientists have so far focused on specific single point-to-point

correlations between operational success drivers, or on their impact on firm performance. This

study contributes to existing literature by depicting correlations that have not yet been

analyzed, and by making the complex general context of interrelations within operational

success factors and their impact on firm performance transparent. Existing literature has

primarily focused on the impact of the cash conversion period on firm performance and the

stimulatory effect of manufacturing performance – including lean concepts – on firm

performance. To the knowledge of the author, no comprehensive large-scale empirical studies

available have yet investigated either the reciprocal links between manufacturing performance

and supply chain performance or the effect of supply chain performance on the cash

conversion period. Nor does any significant literature exist on the impact of supply chain risk

on either firm performance or the cash conversion period. This gap has now been closed,

however, as results suggest that manufacturing performance has a stimulatory effect on supply

chain performance, and that both of these drivers have a similar effect on firm performance.

Furthermore, the implied trade-off between the cash conversion period and a firm's

performance optimum as rooted in supply chain risk is also illustrated. The results prompt one

fundamental question: How can firms align their operational success drivers to maximize their

performance?

The first part of the answer to this question is fairly self-evident. European-based firms must

continuously make their manufacturing and supply chain processes more competitive. They

must reliably deliver high quality with short lead times. The effects of doing so are twofold: a

direct positive impact on firm performance, while improved manufacturing performance also

drives supply chain performance. The second part of the answer to this question is rather more

complex. Based on the study results, it appears to be impossible to simultaneously maximize

firm performance and optimize the cash conversion period by adjusting supply chain risk

376 Lavastre/Gunasekaran/Spalanzani (2011), p. 828.

109

levels. It follows that firms must perform cross-functional evaluations to identify the target

supply chain risk level that will enable them to leverage market potential while keeping the

financial drawbacks of increased supply chain risk exposure within manageable limits.

3.6 Conclusion

The operational success factors that are found to have a significant effect on firm performance

are manufacturing performance, supply chain performance, the cash conversion period and

the level of supply chain risk. Alongside their impact on firm performance, manufacturing

performance was also found to stimulate supply chain performance, as superior supply chain

performance leads to a shorter cash conversion period. With regard to supply chain risk, the

results suggest that firms face a trade-off: Positive firm performance presupposes a significant

level of supply chain risk, which in turn leads to a longer cash conversion period.

Several limitations to this study must be considered. All of them are common in survey-based

studies, however, and should not be overrated, as the chosen compromise is in line with

previous research. First, it is assumed that participants replied to the questions in the survey

conscientiously and truthfully. Several steps were nevertheless taken to avoid distortions. To

name but three of these steps: a comprehensive glossary was provided to participants, full

confidentiality was assured and potential misinterpretations were eliminated during a pre-test

procedure. Study conclusions were drawn based on individual representatives of the selected

target firms. Multiple key informants of each firm would have allowed us to test for inter-rater

reliability, but were not surveyed. On the other hand, the broad spread of participant functions

covered, the high level of seniority and the supporting use of objective secondary data should

have reduced potential biases to a reasonable level. Geographically, the study focused on

Germany, Switzerland and Austria. Tests for country effects produced a negative result.

Notwithstanding, the answers given may be specific to the selected business area, such that

applying the results to other geographic areas could be problematic. The data collected for the

survey is of static nature, preventing any statements on the evolution of items or constructs.

The chosen partial research model postulates that other variables also exert a considerable

influence on the dependent variable firm performance. Indicators such as sales performance,

the economic situation and the brand image certainly impact firm performance too. In

particular, general business decisions about the business model, such as the level of

outsourcing level and the product portfolio, will severely impact both working capital levels

and firm performance. Since the specific objective of this study focuses on the influence of

110

operational drivers, an R² significantly different to one is expected. For the sake of

completeness, it should be mentioned that the firm's main product line, as referred to in the

questionnaire, is not representative of its full sales figures. This fact must likewise be

considered in any critical review.

These limitations to the study results constitute opportunities for future research. In addition,

the author believes it would be very interesting to investigate potential approaches or

initiatives taken by firms to manage supply risk. Accordingly, the prioritization of those

initiatives that best serve to maximize firm performance is expected to be of considerable

value to academics and practitioners alike.

111

4 Boosting firm performance: working capital management & supply risk

chain steering as drivers

4.1 Competitive pressure on operations

The degree to which operations prospers is expected to play a major role in determining

corporate success or failure. Surprisingly, however, even supposed pioneers in this field

stumble time and again. Toyota, the inventor of lean management, has suffered from recalls

and quality problems, while experts even question whether its legendary manufacturing model

is at fault.377 Apple, the dethroned master of controlling the entire supply chain with

supposedly maximum transparency, has struggled recently as e.g. its main supplier Foxconn

reported serious industrial accidents, leading to a bad press.378 In the wake of the tragic

earthquake in Japan in March 2011, Apple's supply chain in general then ran into serious

problems.379 These examples emphasize the considerable relevance of this topic to

practitioners, as firms constantly need to enhance their operational alignment even if they see

themselves in a pioneering role. The speed at which today's stars become tomorrow's fallen

heroes can be astonishing if firms fail to adapt to the changed environment in which they

operate. Indeed, recent economic and social distortions have made this topic more urgent than

ever.

The global economy has been severely hit in recent years, starting in 2008 with the financial

crisis that was triggered by a real estate bubble.380 Only three years later, decision-makers

were again faced with alarming conditions as Greece had to seek refuge under the EU's rescue

umbrella. Both scenarios had a severe impact on the economy, culture and private households

given that, for the first time, turbulence on financial markets spilled over and threatened the

real economy. At this time, however, supply chain risk steering381 – a tool designed to help

firms cope with the threat of disruptions to the real economy and to their own value chain –

was only beginning to take shape.382 Not surprisingly, many firms suffered badly; many did

377 Cole (2011), p. 29. 378 Plambeck/Lee/Yatsko (2012), p. 43. 379 The Wall Street Journal online, 2011. 380 Zandi (2008), p. 1. 381 A term commonly used in both practice and academic research is "supply chain risk management". The

author has consciously chosen to depart from this nomenclature, however, as the concept of supply chain risk management – and risk management in general – is normally associated exclusively with downside risk. By contrast, supply chain risk steering is posited on the conviction that a reasonable level of supply chain risk is required to achieve above-average firm performance, while at the same time recognizing that active involvement is needed to maintain the desired level risk exposure.

382 Borison/Hamm (2010), p. 51.


112

not even survive. Share prices tumbled: Germany's DAX index, for example, plunged by 40

percent in 2009. The total number of insolvencies in Europe reached an all-time high of

178,235 in 2009.383 Yet while the fallout did not leave decision-makers unscathed, more and

more firms – such as large OEM manufacturers in general and Germany's automotive

companies in particular – returned to record-breaking sales figures in 2011.384 Apparently,

firms were successfully able to shift their focus from liquidity concerns during the crisis to

growth and profitability goals;385 and this change was accompanied by an increasing readiness

to shoulder new supply chain risks in order to leverage market opportunities such as

shortening design cycles.386 It is a misconception that higher returns come for free: The

traditional risk/profitability curve still applies, meaning that increased profitability is always

accompanied by greater risk exposure. Driven by a more and more heavily integrated

economy, arbitrage opportunities are thus growing scarce. This is the point at which the

financial and euro crises discussed above have been catching up with today's managers, who

are desperate to avoid a similar shake-up in future.387 This is all the more imperative now that

they experienced at first-hand how utterly impossible it is to react once the economy has been

hit. They have learned their lesson: You need to build the dikes before the flood strikes.388 Or,

as Miguel de Cervantes, author of the classic Don Quixote, put it: "To be prepared is half the

victory". US President Barack Obama has likewise recognized the tremendous importance of

resilient supply chains, addressing the topic at the 2012 World Economic Forum in Davos.

"We have seen that disruptions to supply chains […] can adversely impact global economic

growth and productivity." Obama went on to emphasize the need to address these

challenges.389 It is, then, safe to assume that the need for action will have been recognized by

many managers too. Once again, it is the automotive industry that is taking the lead. Franz

Hermes, Head of Controlling at the ZF Group, one of the world's largest automotive suppliers,

says: "Many companies experienced significant growth rates in 2011. However, the potential

operational risks have risen as well. To make those risks transparent and steer them properly

is outstandingly important."390 Other industries too are discovering the power of supply chain

risk steering. Cisco, for example, has launched a supply chain risk initiative to accommodate 383 Gude (08 February 2011), p. 2. 384 Schaal (01 March 2012). 385 Kaiser/Young (2009), p. 64. 386 McGrath (2011), p. 96. 387 Blome/Schoenherr (2011), p. 43. 388 Cole (2011), p. 111. 389 Obama (January 2012), p. 2. 390 Expert interview, Friedrichshafen, 2nd December 2011.

113

the rising complexity triggered mainly by more than 1,500 contract manufacturers, Cisco's

own acquisitions and the sheer variety of products it rolls out.

As such managers face a dilemma: On the one hand, shareholders are pushing for growth and

profitability which requires that companies e.g. appreciate diversity and distance.391 Yet at the

same time they expect firms to be better prepared in case such disruptions occur again. How,

then, are managers to square this circle? Part of the answer is that they need to implement

powerful supply chain risk steering tools that enable the organization to consciously expose

itself to a targeted level of risk. Different ways to drive a companies' supply chain risk

exposure are shown in figure 4.

Figure 4: Drivers of supply chain risk

Dr. Bruno Niemeyer, CFO of the Wagner Group, the German market leader for coating

systems, gets to the heart of this economic maxim: "The traditional risk/profitability curve is

valid for most company decisions. It is therefore essential for operations that companies

achieve profitability in line with their risk profile and question current risk taking."392 Many

practitioners equate the notion 'risk' with potential threats to the company. Yet this is only one

side of the coin. Risk always inherently presents both upside and downside potential, as

reflected in the results of this study. To examine supply chain risk steering in isolation would

therefore clearly be to miss the point. A firm's operations are a highly complex construct. Its

value drivers are all interconnected. Supply chain risk steering is thus only one of many

components that need to dovetail perfectly with manufacturing performance, supply chain 391 Ghemawat (2011), p. 92. 392 Expert interview, Markdorf, 22nd November 2011.

• Time until new product release• Facelifts, features, colors, etc.• Volatility of the product market• Extent of order changes• Value added by company

Product lifecycleNumber of product variantsDemand uncertaintyOrder changesOutsourcing degree

A.B.C.D.E.

STARTING POINT RISK DRIVERS EXPLANATION

• Knowledge, capacity, etc.• Number of suppliers used• Only one supplier used• Sourcing overseas

Supplier dependencySupplier concentrationSingle sourcingGlobal sourcing

F.G.H.I.

• Customer concentration• Selling overseas• Credibility of customer• Number of sales channels used

Customer dependenceGlobal sellingFinancial strength of customerSales channel complexity

J.K.L.M.

PRODUCT CHARACTERISTICS

UPSTREAM SUPPLY CHAIN

DOWN-STREAM SUPPLY CHAIN

114

performance and the working capital level. The crucial question is therefore: How can these

drivers of operational excellence be aligned in a way that fosters profitability but keeps supply

chain risk exposure to a targeted (and desirable) level?

4.2 What are the operational drivers for your company?

Before answering this question, let us first take a step back and focus our attention on the

company's vision and strategy. Questions about the alignment of a company's operational

drivers must necessarily be derived from the long-range roadmap. With regard to a company's

strategy, managers must define relevant cornerstones such as the preferred business model,

the product strategy and how the company sees its own core competencies. These vital

decisions will affect all subordinate operational matters. For example, the depth of value-

added that is targeted for each product family and derived from how the firm sees its own core

competencies has a huge impact on manufacturing processes, supply chain set-up and so on.

As a consequence, companies first need to streamline and define their corporate strategy

before drilling down into the details of operations. Once the company's strategy has been

formulated, the foundation for efficient and effective operations has been laid. Managers now

need to bring all the various cogs into line with this strategy such that the primary targets for

most enterprises – sales, sales growth, a greater market share and profitability – are

maximized. The trick is to know exactly how the operational cogs interlock with each other.

Only then can an optimized strategy be defined. To get to the bottom of this matter, a study of

German manufacturing companies was conducted. Based on a universe of 274 participants

from senior management, the study highlighted the key interfaces between the most important

cogs. Respondents management experience, industry cluster and firm size are presented in

figure 5. As presented in the figure the participants experience year show a significant high

seniority with 46% having more than 15 years of management experience. Further, 25% of

the firms in the sample generate sales of more than one billion EUR.

115

Figure 5: Participants experience years, industry cluster and firm size

Figure 6 lists some of the companies that participated.

Figure 6: Participating companies in the study

In addition to the statistical analysis of the returned questionnaire in-depth interview have

been conducted with senior managers on C-level or directors. Details of managers interviewed

are presented in Appendix two. According to the study findings, all operational drivers –

manufacturing performance, supply chain performance, competitive working capital levels

and supply chain risk – positively impact firm performance. Up to this point, then, finding the

ideal strategy is simple: Maximize the performance of these drivers to generate superior firm

BY EXPERIENCE YEARS

46% of the participant have a management experience of more than 15 years

BY INDUSTRY

Focus on manufacturing industries

BY SALES

25% of blue chips with sales >EUR 1 bn

274 Companies

Automotive

13%Electrical equipment

8%

Consumergoods

20%

Process industry

32%

Engineeredproducts

27%

<100 m

17%

100-250 m33%

250-500 m

17%500-1,000 m7%

1-10 bn16%

10 bn and more

9%

274 Companies 274 Companies

>2030%

15 - 19

16%

10 - 14

18%

5 - 919%

0 - 4

19%

116

performance. As ever in the real world, however, things are not as clear-cut as that: The study

also revealed that the drivers are not independent of each other.

One highly interesting finding is that a higher level of supply chain risk goes hand in hand

with higher levels of working capital. This maneuvers managers into a typical catch-22

situation. While superior firm performance requires a reasonable level of supply chain risk,

the latter also increases working capital levels – another important driver of firm performance.

In practice, the dilemma becomes clearly apparent. Today's customers are growing more and

more demanding, wanting more and more individualized products to be globally available

with shorter lead times at reasonable prices.393 This demand pattern makes supply chains

more vulnerable and increases risk exposure. Yet aspiring companies have no choice in the

matter. Successful companies have to respond to customer requests. Otherwise, their market

shares will decline as other companies sooner or later manage to offer the required services or

products. However, greater supply chain risk exposure comes at the expense of increased

working capital requirements. The larger number of products drives inventories of raw

materials and/or finished goods, as do shorter lead times and short-term shifts in demand. The

less reliable a production forecast is, the more management will be forced to use inventory

buffers. The relevant correlations are illustrated in figure 7.

Figure 7: Study results correlations of constructs

393 McGrath (2011), p. 96

Working capital performance has a significant positive impact on firm performance

Higher levels of supply chain risk cause higher levels of firm performanceAdditional drivers of firm performance are:• Manufacturing performance• Supply chain performance

1 DRIVERS of firm performance

Three drivers steer working capital performance:• Manufacturing performance• Supply chain performance• Supply chain risk level

2 DRIVERS of working capital performance

Positive impact Negative impact

1) Sales, sales growth, market share, profitability

FIRM PERFORMANCE1)

Working capital performance

Supply chain performance


Supply chain risk level

117

To summarize, strong firm performance is contingent on a significant level of supply chain

risk, which comes at the expense of working capital performance. The consequence is that

simultaneously maximizing firm performance and minimizing working capital levels seems

impossible. In the constellation of conditions where firm performance reaches its zenith,

working capital will not be at the lowest possible level. Considering these study results, one

crucial point becomes obvious: A company can never hope to achieve superior firm

performance if it only looks at each operational driver in isolation. Rather, all drivers must be

examined simultaneously in light of the interdependencies that exist. This requires a cross-

functional approach, as several functions need to be involved. R&D, procurement, production,

sales, controlling, supply chain management must all participate in the process if overall

performance is to be maximized. Let us take an example: If controlling is pushing for

comparatively low working capital levels and thereby missing out on business opportunities

that would benefit the bottom line, that is of no use to anyone. On the other hand, grasping

after each and every business opportunity without giving due consideration to the rising

complexity of operations and the pressure that could put on working capital levels cannot be

the aim of proper management either. Picking up on this theme, Federico Ruckert, head of

working capital initiatives at engine and propulsion system supplier Tognum, notes that

"There is certainly a direct correlation between 'operational risk level' and 'net working capital

performance'. Optimum working capital allocation can only be achieved with end-to-end

process excellence and cross-functional commitment."394 That is true not only for working

capital management, however. Another example is the new product development process, in

which most companies use a gate keeping process that forces new products to pass defined

maturity stages before the start of production. Usually, the involvement of all relevant

functions in this process facilitates a profound assessment of potential repercussions on

supply chain risk levels, and of the costs of any necessary mitigating strategies. Even so,

companies fall short if they fail to factor growing exposure to supply chain risk into their

calculations. Rainer Dickert, Head of Logistics at automotive supplier ZF Group, concurs

fully in this conclusion: "Operational risks must be quantified and evaluated across functions

early in the product development process, followed by an ongoing review of the existing

product portfolio, e.g. via simulation tools."395 The question whether a new product should be

introduced and what features should be offered must likewise be based on the hard facts of 394 Expert interview, Friedrichshafen, 8th November 2011. 395 Expert interview, Friedrichshafen, 28th November 2011.

118

supply chain risk consequences. During this process, the differing preferences of different

functions will inevitably come into conflict. Sales units, for example, most likely will support

initiatives to expand the product portfolio, while functions such as production, procurement

and supply chain management will emphasize the consequences of increased complexity.

That is why a cross-functional approach is so hugely important: The varying perspectives of

all these functions must be elaborated jointly if the conclusions drawn are to lead to the best

possible outcome for the company as a whole. Accordingly, no function should be allowed to

push its preferences through at the expense of another function, as this will not ultimately

benefit the firm. A very strong sales department, say, might get projects approved whose

operational drawbacks actually outweigh any additional market potential. Conversely, a

dominant operations department might prevent bottom-line-profitable projects because of

technical or process concerns. Rainer Dickert of the ZF Group thus makes a further important

point: It is not only about incorporating supply chain risk management in the new product

development process, but also about constantly reviewing the existing portfolio with a view to

supply chain risk matters. In today's fast paced world, the prevailing conditions can vary

frighteningly quickly. New suppliers arrive on the scene, demand patterns shift and new

technologies become ready for market. All these factors impact supply chain risk exposure,

which requires constant monitoring so that action can be taken if the bottom-line costs

triggered by supply chain risk exceed the potential benefits.

Delving back into the past, we discover that each successive decade has tended to focus on a

single function. In the wake of World War II, manufacturing industries concerned themselves

primarily with production efficiency. One major concern was whether a company could

simultaneously realize different dimensions of manufacturing performance. Was it possible to

produce at very low unit costs but ensure high quality output as well? Accordingly, the

production function played a major role in company decisions. Beginning in the 1970s, the

focus then shifted toward managing the value chain. The triad markets became lucrative

supply sources that offered unbeatable cost/benefit levels. European-based companies thus

increasingly reduced the depth of value added in order to stay competitive. The task of

consistently managing what became known as the supply chain across numerous suppliers on

a global basis thus grew more and more complex, forcing companies to establish a dedicated

supply chain management function. The targeted depth of value added, coupled with the

supply chain configuration, thus occupied the majority of managers' attention. A decade or so

119

later, this topic gave way to the latest fashionable issue: shareholder value management.396

The business community realized that what matters is not just to report profits but to increase

the value of the company. Critical to this consideration was the question of how much capital

expenditure was required to achieve reported profits. To answer this question, new key

performance indicators such as the Economic Value Added™ (EVA) or Cash Flow Return on

Investment (CFROI) were introduced.397 In this context, working capital management moved

center-stage: Best-in-class companies were able to reduce working capital levels, generating

profits with comparatively fewer invested assets. Aspects of this focus on value-based

management become outdated at the turn of the millennium, however, as attention shifted

toward risk management. Since then, several economic crises have hit companies hard,

starting with the new economy bubble and followed by the U.S. property crisis and the euro

crisis. Upturn and downturn cycles grew significantly shorter, raising another new question

for shareholders, managers and politicians: What kinds – and volumes – of toxic assets are

lying dormant on corporate balance sheets? In response, companies were encouraged to report

the existence of risks, for example by the introduction of laws such as KonTraG, Germany's

Management Control and Transparency Act, in 1998.398 This sketched review of different

company focuses over time is illustrated in figure 8.

396 Hachmeister (1997b), p. 825-826. 397 Hachmeister (1997a), p. 556-560. 398 Hachmeister (1999), p. 1453.

120

Figure 8: The changing focus of management attention over time

Looking at the results of the study in light of the focal topics addressed in recent decades, the

need for action becomes clear. The study results explicitly indicate that optimum firm

performance is only to be achieved if all operational drivers are considered simultaneously.

Focusing on only one driver, such as working capital or risk management, will inevitably lead

to a second-best solution. Managers should therefore be encouraged to steer their operations

holistically. Regrettably, however, this is still far from common practice, for obvious reasons:

First, actively steering so many operational drivers simultaneously is highly complex.

Numerous variables have to be considered, all of which interfere with each other. Second, the

cross-functional approach usually triggers precisely the kind of political conflicts that many

managers try to avoid. To help managers overcome these obstacles, this study shows how a

holistic operations management strategy can be implemented successfully.

4.3 Supply chain risk steering – a tool to boost firm performance

The first step is to make a firm's strategic options transparent. The operational drivers that can

be influenced directly are manufacturing performance, supply chain performance and supply

chain risk. Depending on the level selected for each driver, companies will require a certain

level of working capital and achieve corresponding firm performance. Hence, a company's

strategic options comprise any conceivable permutations of manufacturing performance,

supply chain performance and supply chain risk level. Based on the findings of the study,

Quality vs. cost leadership

Value chain management

Value-based management

Risk management

Integrated approach

• Sarbanes-Oxley• Basel I & II• Ratings• Financial crisis

• Shareholder Value• Lean concepts• Cost of capital• Globalization

• Integrated approach to maximize company performance

• All operational drivers of company performance considered simultaneously

~ 1950 - 1970 ~ 1970 - 1990 ~ 1990 - 2000 ~ 2000 - 2010 TO BE

Firmperformance

Firmperformance

Firmperformance

Firmperformance

Firmperformance

Manu-facturingperformance


Working capitalperformance

SC risk

Manu-facturing

perfor-mance


Working capitalperformance

Supply chain risk

121

these three dimensions can in fact be merged into two, given that manufacturing performance

and supply chain performance have the same directional effect as shown in figure 9.

Figure 9: The supply chain risk trade-off

To work out the main strategic options for a firm's operations, four different quadrants are

formed for the dimensions manufacturing performance/supply chain performance and supply

chain risk, as shown in figure 10. As illustrated, a firm can choose between pursuing a high

level of manufacturing performance/supply chain performance and supply chain risk or a low

one. A mixed strategy of high manufacturing performance/supply chain performance and low

supply chain risk, or vice versa, would be another option. The decision about which strategy

to opt for is contingent on the expected level of working capital and firm performance. Before

outlining the relationship between the different strategic options and expected firm

performance levels, however, let us examine the four strategic options in some more detail.

Each of the strategic options shown in figure 10 has been given its own name.


Firmperformance




��

��

��

��

��

Correlations

Risk trade-off

1)

I

II

Positive correlation Negative correlation��

122

Figure 10: Strategic operational options

The first option, "price", combines a high degree of supply chain risk exposure with relatively

low manufacturing and supply chain performance. Firms positioned in this quadrant accept

drawbacks in reliability and deliverability, so these competitive disadvantages have to be

compensated for. Customers will only go for the resultant products if the lower price offsets

the identified drawbacks. "Home grown" companies align their operations to a low degree of

supply chain risk, but also operate at relatively low levels of manufacturing performance/

supply chain performance. Typically, what are known as the hidden champions – companies

that have experienced strong growth – struggle to consolidate their structures in order to

maintain efficient and effective manufacturing and supply chains. Yet their supply chain risk

exposure is still comparatively low. Usually, these companies offer a manageable product

range, maintain a significant degree of local sourcing and can call on well-established

relationships with their suppliers. These are just a few of the characteristics that exemplify the

moderate supply chain risk levels. Other firms – in the "quality" quadrant – become standard-

setters in terms of manufacturing and supply chain performance. These companies operate

very flexible production, provide best-in-class quality products with a very short time to

market. In addition, their supply chains operate very reliably. These companies also

emphasize a comparatively low level of supply chain risk, keeping exposure to potential

disruptions to a minimum. Last, but not least, there are firms – referred to here as

"champions" – that operate their manufacturing and supply chain at similar performance

levels, but that also accept higher levels of supply chain risk. Based on the strength of their

Manufacturing performance/ "CHAMPIONS""QUALITY"

"HOME GROWN" "PRICE"

• High risk exposure

• Very volatile profitability

• Existential threat in case of 'black swan' events


"GAMBLE"

• Superior manufacturing ensures short lead time, quality and flexibility

• Limited supply chain complexity ensures high reliability

• Manufacturing excellence in line with lean processes

• Leveraging cost & profit advantages in the supply chain while ensuring deliverability

• Ordinary Manufacturing Performance

• Local and focused customers

• Low complexity in the up-/ downstream Supply Chain

• Cost focus• Competitive advantage

optained by price• Drawbacks in reliability

and deliverabili ty


I

IIFirm's operation risk exposure

Ordinary business risk & risk management in place Disproportionate risk

123

operations, they have consciously decided to take on additional risks in the supply chain by

increasing the number of product variants, shortening product life cycles and adding extra

sales channels, for instance.

The important question now is: What is the best strategic option for manufacturing

companies? The results of the study point to an unequivocal answer: Companies in the

"champions" quadrant achieve by far the highest firm performance in terms of sales, sales

growth, profitability and market share. Their average firm performance is 14% above the

lowest-performing reference group, the "home grown" quadrant. The second-best results are

achieved by companies in the "quality" quadrant, followed by firms in the "price" quadrant.

Performance figures and the sample sizes for each quadrant are shown in figure 11.

Figure 11: Average performance for each strategic option

Close analysis of these results reveals two things: First, firms that achieve comparatively

higher manufacturing and supply chain performance are more successful. Second, firms that

accept a reasonable level of supply chain risk outperform firms that adopt risk-averse

strategies. The first finding is fairly intuitive. If a firm has set up efficient and effective

processes in its manufacturing and supply chain activities, this will boost repeat sales while

keeping the cost base competitive. The second finding requires further discussion. Firms that

avoid any supply chain risks miss out on business opportunities that could have had a positive

impact on their net present value. Excessive caution thus detracts from firm performance in

their case. We do not advise companies to hastily increase their exposure to supply chain risk

Firm's operation risk exposure

"CHAMPIONS""QUALITY"

"PRICE"


Ordinary business risk & risk management in place Disproportionate risk

"GAMBLE"

1) Scale 1-5 (5 being best) 2) Net trade cycle [days] 3) # of companies in quadrant

Firm performance1)

Working capital performance2)


Firm performance


Firm performance


3.3 57 3.6 64

3.1 64 3.2 99

673) 63

95 49

Improvement strategies

Ø

Ø

Manufacturing performance/


+8%

Firm Performance

"HOME GROWN"+6%

124

in order to achieve higher performance figures. However, the results of the study do suggest

that companies must these days open themselves to prudent supply chain risks, as customer

demand patterns quite simply require them to do so. Shorter life cycles, more individualized

products, short times to market, low prices: All these expectations by today's customers drive

supply chain risk. Companies that hesitate to assume these risks will therefore suffer from

reduced sales and, ultimately, lower profitability. At this point, it should be noted that this is

true only for supply chain risk levels that involve regular business risks that are actively

managed by the organization. Even then, disproportionate risks that could lead to a potential

threat if a "black swan event" occurred would still have to be mitigated.399

Furthermore, figure 11 draws our attention to another insight gained from the study. For the

strategic option "champion", which maximizes firm performance, working capital levels are

not at a minimum. On the contrary, the increased level of supply chain risk necessitates

additional working capital, while the revenue from additional business opportunities exceeds

the accompanied higher cost of capital. The following example illustrates the point: If a

company decides to develop new geographical markets to boost sales, its existing supply

chain will have to be properly adapted. In all probability, new storage facilities will have to be

built, thereby increasing inventory. Further relationships with customers must be established,

including payment terms. Depending on the precise situation, the sales department may grant

sales credit to get a foot in the door. Similarly, suppliers will insist on a short payment period

as no established business relationship exists. In other words, the increased supply chain risk

– triggered by the development of new geographical markets – will have increased working

capital levels. The hope is, of course, that this downside will be outweighed by the additional

sales generated in the new market. In this way, increased supply chain risk puts pressure on

working capital and thus boosts firm performance.

The good news for managers is: A company's current position is not set in stone. The

challenge to companies is to realign their operations in order to shift their position toward the

"champions" quadrant. Two caveats should be observed in advance, however: Companies that

need to improve their manufacturing and supply chain performance first need to craft suitable

processes before taking on new supply chain risks. Similarly, companies in the "price"

quadrant first need to reduce their risk exposure to stabilize operations before consciously

399 Taleb (2010), p. 1-480.

125

increasing risk exposure again to the targeted level. These strategies for the realignment of

companies' operations are illustrated by the orange arrows in figure 11.

In line with the strategic realignment of operations, working capital requirements and firm

performance will change too. This relationship is shown in figure 12.

Figure 12: Strategic working capital management

Shifting from the lower quadrants to "quality" will simultaneously lower working capital

levels and boost firm performance. Yet the final step – shifting from the "quality" quadrant to

"champion" – will further improve firm performance, thus increasing working capital

requirements as discussed above. The primary objective – firm performance – is thus

optimized at the expense of the secondary target as working capital performance decreases.

In practice, the current situation is very sobering. Despite the fact that most companies are

continuously working to improve manufacturing and supply chain performance, their efforts

with regard to supply chain risks are negligible. In light of the findings of this study, this is an

untenable situation. Companies are squandering opportunities to improve their performance

by ignoring the power of supply chain risk steering. Without proper supply chain risk

steering, three detrimental situations are conceivable. First, excessive risk aversion could lead

companies to miss out on business opportunities that would otherwise boost their

performance. Second, companies might operate at disproportionate risk levels, leading to an

Working capital performance(Secondary target)

Firm performance(Primary target)

A B

Companies are able to increase Firm performance by

• Increasing manufacturing/supply chain performance ("Home grown" to "Quality")

• Increasing manufacturing/supply chain performance and improving risk management ("Price" to "Quality")

A

To achieve superior firm performance companies need to increase supply chain risk level based on proper risk management

B

Improvement strategies

126

existential threat if a "black swan event" occurred.400 Immediate action to mitigate risk is

required in such cases. Third, companies might, by sheer weight of circumstances, find

themselves in the "champions" quadrant. However, without active risk steering, the changing

environment in which they operate will sooner or later maneuver them into a suboptimal

position. Their current, optimized position is highly likely to be only short-lived.

One main pitfall in this respect is that most companies do not clearly assign responsibilities

for supply chain risk management. As a result, the different functions such as R&D,

procurement, production, sales, controlling and supply chain management each operate in

their own best interests on supply chain risk topics. What is lacking is a global perspective

that seeks the optimum for the entire firm. These organizational deficits lead to four main

obstacles in practice. First, most companies only have visibility for a small portion of their

total supply chain risk exposure, including transparency about supply chain risk drivers,

sources and attitudes. This is somewhat surprising, as most companies have established

comprehensive monitoring systems for financial risks such as exchange rate risks and interest

rate change exposure.401 It appears, then, that this level of penetration is missing in most cases

for operational risks. To make matters worse, those operational risks that are monitored are

not accessible to the entire company. On the contrary, these specific risks, such as supplier

solvency, are monitored in isolation in the different functions. Obviously, when that happens,

no overall assessment of monitored supply chain risks ever takes place.

Second, transparency typically tends to decrease continually the further one moves away from

headquarters. While companies are normally very well aware of existing supply chain risks

for central operations, risks at decentralized units are not comprehensively monitored,

reported or consolidated. This leaves companies out on a limb. Disruptions further down the

supply chain in a decentralized unit, for example, have the potential to cause severe losses.

One prominent example was the earthquake in Japan, which brought production in many

plants to a standstill, affecting the entire supply chain of global blue-chip companies such as

Sony, Toyota, Honda and Nissan.402 The lack of visibility regarding risks to decentralized

operations also prevents headquarters from pursuing a hedging strategy. If full visibility were

given, one would expect to see a risk mitigation strategy taking due account of all prevailing

400 Taleb (2010), p. 1-480. 401 Lhabitant/Tinguely (2001), p. 343-344. 402 Gregory (11 March 2011).

127

supply chain risks. Risks in one country could then be offset by another within the overall

evaluation. Without proper transparency, however, this opportunity is forfeited.

Third, there is no cross-functional optimization of supplier risk steering. For want of an

instance with overall responsibility, the typical silo mentality flourishes. Operational

responsibilities that have grown organically are usually allocated to the different functions. In

particular, responsibilities relating to supply chain risk steering are mostly to be found in the

different functions, depending on the topic. The sales department, for example, is usually in

charge of managing country risk; procurement takes care of supplier solvency risk; and

production develops contingency plans to cover for machine downtimes. Antje von Dewitz,

CEO of outdoor specialist Vaude, explains the point: "Operational risks are often quantified

and analyzed separately in the company's functions. To achieve overall transparency,

however, these puzzle pieces need to be put together across functions."403 Overall

transparency is the prerequisite for comprehensive management. The whole is more than the

sum of its parts: This perception, borrowed from synergy research, can be mapped onto the

topic of supply chain risk too.404 Certain supply chain risks can mutually reinforce each

other's dramatic impact. Quantifying a company's total supply chain risk exposure and

effectively steering risk to a targeted level is feasible only when functions collaborate closely

and exchange information frequently. Again, however, this precondition is still lacking in

most companies.

Fourth, supply chain risk steering could, at most companies today, be described as monitoring

defined threshold values for a manageable number of KPIs. Only when the defined thresholds

are exceeded are ad-hoc actions triggered to push the value back below the limit. This

impression is confirmed by Bernhard Scherer, CEO of plant engineering company Zeppelin

Systems. According to Scherer, "The focus of current operational risk management is on

preventing serious negative incidents. Instead, operational risk management should become a

proactive tool designed to assure maximum profitability."405 This statement provides useful

hints. It also explains why the approach adopted by many companies – who attempt to steer

supply chain risk merely by managing thresholds – can never lead to a company's optimum.

These companies maintain "black boxes" in all cases where the threshold is not exceeded. Yet

the assumption must surely be that some optimum supply chain risk level also exists below

403 Expert interview, Tettnang, 21st September 2011. 404 Ennen/Richter (2010), p. 207. 405 Expert interview, Friedrichshafen, 22nd September 2011.

128

the ultima ratio level that should not be exceeded under any circumstances. Companies that

do not actively monitor and steer target risk levels are missing a tool to maneuver their

operations to this target level. The situation is further aggravated by the fact that the ad-hoc

actions that have to be taken when managing thresholds are usually not as effective or

efficient as continuously steering supply chain risk levels. Ad-hoc actions must first be

defined; nor can implementation revert to existing processes. Many of the steps often taken

also begin to "bite" only after a certain period – lost time in which the supply chain continues

to operate at a disproportionate risk level. In summary, companies that actively steer their

supply chain risk levels can be expected to outperform companies whose risk management

consists solely of intervening if predefined values are exceeded or undershot. However, many

companies still neglect this strategic lever for boosting performance.

4.4 Four consecutive steps to a successful supply chain risk steering

While the benefits of active cross-functional supply chain risk steering as outlined in the

previous section are obvious, pioneering users of this tool are few and far between. Why do

most companies neglect comprehensive supply chain risk steering? The need to ask this

question is even more surprising given that, as we pointed out in the introduction, the need is

clearly recognized by most operations managers – a point underscored by the numerous

quotations in the previous section. In the expert interviews we conducted, two main reasons

for the hesitant behavior of many companies emerged: First, supply chain risk steering is

highly complex and involves numerous dependencies that are only partially transparent.

Second, it is recognized as a topic with serious potential for conflicts, as the required cross-

functional collaboration presupposes information exchange and, possibly, over the transfer of

responsibility to a supply chain risk manager, say. It is no secret that relinquishing power is

not in everyone's interests. Despite these obstacles, however, companies are increasingly

beginning to pay attention to the topic of supply chain risk steering, and to act accordingly.

Companies would therefore do well to prevent overzealous action, as guidelines on how to

implement this pragmatic but powerful tool are usually still unavailable. Many managers in

charge of the implementation of supply chain risk steering stumble for lack of practical

experience with this new topic. Nor has academic research yet produced any guiding

principles. Accordingly, the section that follows proposes a recommended course of action for

the successful implementation of supply chain risk steering.

129

All in all, four steps are required to successfully implement supply chain risk steering. First,

supply chain risk policy must be derived from the company's vision and strategy. Second,

organizational responsibility for supply chain risk steering must be defined. Third, supply

chain risk steering needs to be linked to the companies process landscape. Fourth, a

sophisticated approach to the daily execution of supply chain risk steering must be worked

out. These requirements are discussed in detail below, one after the other.

A supply chain risk policy must be formulated as each company needs to specify its targets

for supply chain flexibility, cost, delivery time and quality. It goes without saying that these

strategic targets contain inherent trade-offs and therefore cannot be met simultaneously.406 Is

the prime concern a highly flexible supply chain that copes with last-minute order changes or

product variety? Or is cost consciousness more important? Is punctual delivery valued more

highly than the quality of delivery? A policy on supply chain risk steering creates the

framework for all actions taken and has to be part of a firms general risk management.407

However, the supply chain risk steering policy should not be defined in isolation. Rather, it

should be derived from a company's vision and strategy. This pyramidal approach is

illustrated in figure 13. A company's strategy must itself be derived from its vision: the

desired future long-term state of the company. The strategy, made up of elements such as

product strategy and the business model, is a cornerstone that must be reflected in the supply

chain risk policy. In addition, an ambitious vision of growth based on high-value products, for

example, must be reflected in the supply chain risk steering policy.

406 Tellis/Yin/Niraj (2011), p. 16. 407 For a definition of risk management, please see Trossmann/Baumeister (2006), p. 48.

130

Figure 13: Derivation of supply chain risk policy

The options for the organizational anchoring of responsibility for supply chain risk steering

are many and varied. Whatever the case, however, the supply chain risk steering manager

must be empowered in such a way that decisions can even be taken in the event of resistance

from individual functions. As we saw earlier, the main goal of supply chain risk steering is to

boost firm performance by adopting a holistic approach that sees each function's different

objectives through the eyes of the entire company. Inevitably, individual functions will, in

isolated cases, have to defer to the authority of the newly established institution. Three of the

options preferred by the experts we interviewed are illustrated in figure 14.

131

Figure 14: Three organizational options for supply chain risk steering

One obvious model is to assign responsibility for supply chain risk steering to supply chain

management. The advanced operational expertise that supply managers have acquired in their

day-to-day work often makes them the ideal people to properly assess what actions should be

taken. The cross-functional orientation of supply chain management also lends further weight

to the arguments for this organizational option. In many ways, however, this is like asking the

wolf to guard the sheep. Supply chain management is an aspect of operations that pursues its

very own aims. It is hard to find a supply chain manager who will voluntarily reduce the

number of suppliers to generate synergies that lead to savings at the expense of delivery

reliability. Similarly, managers will most likely be hesitant to enter new volatile markets,

increase product variety or shorten lifecycles if doing so could pose a threat to delivery times

or quality. These are attitudes that are utterly inappropriate for successful supply chain risk

steering. The person in charge should view supply chain risk steering from a company

perspective, not from an operational point of view.

A second option is to establish a staff position that is solely dedicated to supply chain risk

steering. In all probability, this staff position would be supervised by the CxO level of top

management; and that is likely to be the biggest advantage of this option, as it guarantees

substantial top management attention to the topic. On the other hand, any such stand-alone

SCM

R&D Procure-ment

Produc-tion

Sales

Controlling

Supply chain managementA

ControllingC

Staff positionB

SCM

R&D Procure-ment

Produc-tion

Sales

Controlling

SCM

R&D Procure-ment

Produc-tion

Sales

Controlling

Staff function

132

satellite, disconnected from the day-to-day business, could lack both transparency and the

acceptance of the functions involved. In some companies this handicap is diminished by

temporarily assigning employees from the different function to the staff function team. In this

way, operational knowledge is ensured while still emphasizing the foremost aim of the staff

function: supply chain risk steering. Ultimately, however, this organizational option too is not

entirely convincing.

The third and last option is to assign supply chain risk steering to the controlling department.

The latter's reputation as a neutral institution within the company is regarded as the main

advantage, as this should facilitate discussion of conflicting positions. Controlling does not

stand accused of being biased toward this or that interest, as its sole target is to maximize firm

performance. As a result, controlling should act as a neutral data-providing mediator. Since

controlling is usually already in charge of e.g. financial risk management or controlling of

project risks suitable structures and tools should already be in place.408 This makes

implementation much easier than if everything had to be built from scratch. Here again,

however, there is a catch that must be resolved: The different functions must supply the

required information to controlling, as the latter is not deeply involved in day-to-day business.

Controlling would, in other words, need to be empowered such that access to the functions is

assured. This option is regarded as the most promising organizational approach.

The third step to successful implementation of supply chain risk steering involves linking

supply chain risk steering to a company's existing process landscape. This factor is seen as

increasingly important, because supply chain risk steering must be involved in the new

product development process and the ongoing review of existing business processes.

Nowadays, most companies operate a gatekeeping process in which products have to pass

predefined maturity stages. Product maturity is achieved by obtaining input from the different

functions during stages such as ideas generation, concept development and testing, business

analysis and technical implementation. The next stage in the development process is launched

only when the prerequisites defined for the previous stage have been met. Reviews of the

consequences of supply chain risk are thus usually lacking in this process. Bearing in mind

the above discussion, it is essential for the consequences of supply chain risk levels to be

considered at this early stage. Potential deviations from the target risk level could be adjusted

408 Trossmann/Baumeister/Ilg (2007), p. 15-16; Trossmann/Baumeister (2006), p. 46-48.

133

by appropriate actions at this stage. On the other hand, failure to consider the potential impact

on supply chain risk levels would mean that crucial information on cost drivers would be

neglected, which cannot be in the interests of managers. It should once again be stressed at

this point that risk steering is not only about preventing risks to the supply chain. On the

contrary, there are situations in which higher risks to the supply chain could be appropriate,

provided that exposure remains within reasonable limits and the product contribution margin

is increased. As discussed above, the integration of supply chain risk steering often finish as

soon as the new product development process has been completed successfully. Conversely,

however, ongoing business during the commercialization phase also requires constant review

with regard to supply chain risk steering. This is extremely important, since the environmental

context changes frequently. Existing structures disappear, suddenly forcing companies to

revector their supply chain. By consequence, the underlying supply chain risk level changes

too. To take just one example: Several companies recently assessed the risk of engaging in

business relationships with Iran – a completely different scenario – during the financial crisis.

Order books were empty and the political situation had eased slightly, encouraging companies

to accept orders. Since then, however, the political situation has grown continually more

fraught and production in certain sectors of industry cannot keep up with demand. This

change in risk evaluation has led many companies to terminate their business relationships

with Iran. Another example refers to the supplier landscape: Consolidation, geographical

shifts in demand and technological innovation are constantly changing the supply network.

Companies must continuously monitor these developments, draw their own conclusions with

regard to supply chain risk steering and take actions to adjust exposure in line with the

targeted level. The required link between supply chain risk steering and company processes is

outlined in figure 15.

134

Figure 15: Link between supply chain risk steering and company processes

Lastly, the organization needs to be provided with a powerful approach that underpins the

daily execution of supply chain risk steering. The proposed approach is shown in figure 16.

As indicated in the figure, supply chain risk steering should not be viewed in isolation.

Figure 16: Integrated approach for supply chain risk steering

BOOST MANUFACTURING PERFORMANCE

BOOST SUPPLY CHAIN PERFORMANCE

SUPPLY CHAIN RISK STEERING

R&D

Procurement

Production

Controlling

INTEGRATED APPROACH

Supply Chain Management

Sales

135

Manufacturing performance and supply chain performance are closely related to supply chain

risk steering, as their performance level directly drives working capital requirements.

Conversely, higher manufacturing performance and supply chain performance suggest a

reduced need for working capital. At the same time, increased manufacturing and supply

chain performance could serve as tools to compensate for the negative effects of increased

supply chain risk levels on working capital. Based on these considerations, a concerted effort

should be made to improve manufacturing and supply chain performance as a complement to

supply chain risk steering. The more, the better: Higher levels of manufacturing and supply

chain performance reduce working capital requirements and boost firm performance. The

steps in the approach to executing supply chain risk steering are portrayed in the inner circle

of the figure. Five consecutive and recurrent steps are required for successful day-to-day

implementation. First, a company's specific supply chain risk drivers and sources must be

defined. Supply chain risk drivers such as global or single sourcing comprise all the supply

chain characteristics that can foster disruptions. Risk sources consist of potentially devastating

ripple effects in a supply chain, such as natural hazards or the solvency of a supplier. Defining

the supply chain risk drivers as well as sources for disruptions is the basic prerequisite for any

further considerations. The next step is to create transparency on as-is exposure in terms of

probability and impact. Once there is transparency about actual exposure – step three – the

company is empowered to define the desired target risk level for each risk. Based on the

identified gaps, operations has to come up with countermeasures in step four to fill in the

gaps. Last but not least, recurring reports must be generated in step five to point out existing

risk exposure and identify the need for action to decrease or increase risk levels. Monitoring

supply chain risk steering is extremely important, as it serves as a powerful tool to attract top

management attention. Thus, as we have already stressed many times, cross-functional

collaboration is key. This is also hinted at by the arrows in the figure, which emphasize the

simultaneous involvement of R&D, procurement, supply chain management, production,

sales and controlling. These five steps are now presented in detail.

The crucial trick in step 1 – defining supply chain risk drivers and sources – is to juxtapose

sources and drivers in a matrix form. Aligning risk sources vertically and drivers horizontally,

as shown in figure 17, allows each risk source to be linked to those drivers that impact total

exposure to this source.

136

Figure 17: Definition of risk sources and drivers

One can thus clearly see that, in Europe, the risk source "capacity constraints" for part A in

Europe will most likely be driven by risk drivers such as the number of product variants,

supplier density and global sourcing. Those risk sources of relevance to a given company can

then be broken down to a reasonable level. The example in figure 16 breaks down into five

levels: overall risk level, clusters of risk sources, individual risk sources, product groups and

geographical markets. This structure will need to be adapted to the specific company's

situation. The mere existence of supply chain drivers – the number of product variants, for

example – is per se neither good nor bad for a company, so it is not possibility to derive a

certain probability of disruption from the number of product variants. The situation is

different for supply chain risk sources, however. It is indeed possible to quantify total

exposure (consisting of impact and probability) for risk sources such as natural disasters,

capacity constraints and machine breakdowns. To be able to adjust the exposure level, the risk

drivers come into play again. Steering the different risk drivers will vary exposure to certain

risk sources. Companies thus need to be aware that steering one driver could also alter their

exposure to other risk sources at the same time. That is why it is important in step one to map

both supply chain risk sources and drivers and to make their interrelationships transparent in

the form of a matrix.

Based on the identified causalities, exposure to the various risk sources and the characteristics

of the risk drivers must be quantified. The former can be done by linking the probability of

the identified risk source to its expected impact. For example, the probability of an earthquake

137

in a business market such as China might by very low even if its impact would be dramatic.

This exercise must be conducted for each individual risk source at the lowest level of the

breakdown. Similarly, the characteristics of the identified risk drivers have to be documented:

How many product variants is the company providing? In which product groups and markets

is single sourcing practiced? How long is the product lifecycle for the different products? All

these questions need answering.

Logically, the next step, step three, is about defining the target risk level based on the

transparency achieved. The company knows every single risk source to which it is exposed.

Under the guidance of the supply chain risk steering manager, the different functions now

need to define the target levels for each one. Wherever a gap exists between the target level

and the current situation, corrective action must be taken. As we have seen, supply chain

drivers serve as the steering tools for adjusting the risk level. This is probably the most

challenging aspect of the whole exercise. The various functions must jointly evaluate how far

they can push without crossing the line. Bernd Baader, Head of Logistics at MTU

Friedrichshafen clarifies the point with an example: "Supply chain requirements, such as on-

time delivery, are clearly defined in most companies. The challenge in a business with high

amount of variants is to find the right level of working capital performance to support these

supply chain requirements."409 Accordingly, the functions need to find the level of supply

chain risk that they all agree is just about acceptable. As illustrated in figure 18, "fever

curves" can be a useful tool to point out how the different drivers need to be adjusted to reach

the desired risk exposure. Figure 18 also shows that supply chain risk steering should also

explicitly take note if the characteristics of even just one risk driver reach a critical threshold.

In this case, immediate action is required to prevent serious losses arising from, say, a "black

swan" event such as the earthquake in Japan.

409 Expert interview, Friedrichshafen, 27st September 2012.

138

Figure 18: Defining the targeted level of supply chain risk – Example

Step four involves implementing the roadmap that has thus been plotted. To change the

characteristics of risk drivers such as supplier density, it is the turn of procurement to take

action. If the target is to further reduce the supplier base, business relationships will have to

be terminated. If the opposite is the case, new suppliers will need to be cultivated. Another

example, the steering of supplier default risk, is illustrated in figure 19.

Figure 19: Steering of operations – Example: supplier default risk

PRODUCT CHARACTERISTICS

Product lifecycleNumber of product variantsDemand uncertaintyOrder changesOutsourcing degree

A.B.C.D.E.

STARTING POINT SUPPLY RISK DRIVERS AS "STEERING TOOLS"

UPSTREAM SUPPLY CHAIN

Supplier dependencySupplier concentrationSingle sourcingGlobal sourcing

F.G.H.I.

DOWN-STREAM SUPPLY CHAIN

Customer dependenceGlobal sellingFinancial strength of customerSales channel complexity

J.K.L.M.

1 2 3 4 5

As-is To be Critical risk

Risk adjustment leads to higher com-pany performance

Critical Supply Chain Risk level identified –In case of 'black swan event' serious loss potential (e.g. Japan earthquake)

Identify Direct Material Suppliers to Finance.Includes:• Supplier Name• Material Category

Identify high risk suppliers using market intelligence along with D&B/Moody Ratings

Reviews and updates with the following:1. Single/Sole Source Identification2. Determine OI Impact3. Highlight DB Rating of 9 &, Single/Sole OI

impact of >USD 15 MM4. Contingency Plan for each supplier

Calculate Suppliers Financial Risk (SER) and sends to Finance

Approves contingency plans

Posted to Document Center

Reviews and send to D&B for Financial Risk Rating

Communicate highlighted Summary to Finance

Linkage to business continuity plan

• Annual spend

Annually

Annually

15th of the month

QrtlyReport

Exception Ratings are developed using the following criteria:1. Supplier Bankruptcy2. DB (SER) Supplier Evaluation Risk Rating Score of 9 =

estimates a 17% chance of supplier failure in 12 months3. Moods's rating of CA = highly speculative in debt obligations4. Analysis of private suppliers financial statements

PRO.COE

FINANCE

BUYING GROUP

DUNN & BRADSTR.

BU'S

KNOWLEDGE CENTER

139

As shown in the figure, the different functions need to closely work together to adjust a

company's supplier base in line with the desired level of supplier default risk. Step five – the

final step – should make the initiatives launched and their effect transparent. Different risk

sources and their corresponding level of exposure should be presented in order of importance.

The report should be tailored to target groups and updated regularly. This powerful tool

should also attract the attention of top management to current supply chain risk initiatives. To

this end, the report should be kept pragmatic to avoid tying up too much capacity in supplying

the necessary data and providing updates. One thing must be avoided at all costs: Acceptance

of supply chain risk steering should not be compromised by reporting that is too complex and,

hence, impracticable.

4.5 Managerial implications

In the past, most companies have seen managing supply chain risk as unimportant. Yet

managers' attitude is changing. The tremendous upheavals triggered by recent economic crises

have sharpened their sensitivity to and awareness of the risk-profitability trade-off in

operations.410 With most companies now back on course for growth, this new attitude is

leading to the critical review of existing risks. There is no doubt that companies who want to

stay on the market must take risks. The only question is: How much risk exposure is good for

them? This study suggests that an inherent trade-off exists: Strong firm performance requires

a significant level of supply chain risk at the expense of working capital performance. The

key conclusion drawn from this finding is that firm performance can be maximized only if the

topic of supply chain risk steering is addressed on a cross-functional basis. The anachronistic

but still common silo mentality with regard to supply chain risk steering falls short of the

mark. Most companies do not actively steer cross-functional supply chain risk in a way that

creates transparency and optimizes risk-profitability decisions. Best-in-class companies

outperform others by up to 14% in terms of firm performance. So word that there is money on

the table is gradually filtering through the business community. Having said that, most

initiatives on supply chain risk steering are still in their infancy. A start has been made,

though, and more and more companies will follow suit. Most importantly, companies must

assign cross-functional responsibility for supply chain risk steering and ensure balanced but

critical collaboration between the different stakeholders. Based on a supply chain risk policy

410 Seshadri/Subrahmanyam (2005), p. 1.

140

tailored to each company's specific needs, the existing process landscape must be linked to

supply chain risk steering. Having thus laid the foundation, a strategy to ensure the day-to-day

execution of supply chain risk steering must be developed and implemented. Considering risk

aspects in top management decisions is key.411 Yet all these steps will be worthless if the new

mindset on supply chain risk steering is not nurtured throughout the organization. Top

management must therefore communicate clearly and present its objectives for supply chain

risk management. It is worth the effort. Supply chain risk steering is a powerful tool to boost

firm performance while simultaneously guarding a company's market position against

possible disruptions.

411 Trossmann/Baumeister/Ilg (2007), p. 15.

141

5 Coverage of the analysis

The initial intent of this academic research was to investigate the interrelationships between

operational drivers that affect the physical production, supply and/or distribution of goods. In

the course of the study, several limitations became obvious that are described below.

The Contingency Theory was selected to depict the theoretical basis of the study. However,

any attempt to explain firm performance on the basis of operational drivers can only ever

paint part of the picture, as numerous additional factors of influence may exist. This study

should thus be understood as seeking to provide insights into the contribution made by

operations to a firm's performance. The study lays no claim to completeness with regard to

drivers of firm performance. An unrestricted general equilibrium model would need to include

further factors of influence. The contribution made by this academic research should be

understood as a step in this direction.

Alongside these theoretical restrictions, several methodological limitations exist with regard

to data gathering for the literature review and both the empirical survey and its analysis.

These limitations are addressed in sections 2.7 and 3.6 respectively.

There are still ample opportunities for further research projects relating to this study. In

particular, researchers should be encouraged to further investigate both operational success

drivers in structural models and the relevant interdependencies. Additional success drivers

would need to be integrated in such models, above and beyond drivers that originate solely

from operations. Most functions will likely affect operations to some extent, which is why

their impact and strategies to optimize that impact should be of considerable interest. In

particular, fresh insights into the effect of sales activities on operational success drivers such

as working capital requirements, supply chain risk and manufacturing performance, and vice

versa, can be expected to benefit academic research and practitioners alike. Similarly, a

company's commercial activities such as the budgeting process, controlling activities and the

work of the treasury department will also affect operations. Hence the author's suggestion that

the model presented herein be expanded in terms of drivers that affect both operations and

firm performance.

More than ever, firms must pursue convincing strategies if they are to compete in today's

challenging global markets. This raises the question how certain aspects of a firm's strategy –

such as the business model, defined core competencies and the product strategy – affect


142

operations.412 Since it is the responsibility of operations to put the chosen strategy into

practice, the consequences of certain strategic options should be of great interest.413 One

recent development illustrates these considerations: Companies such as Apple and Germany-

based hardware specialist Medion are pursuing a strategy whose depth of value added is

almost zero. All production and logistics activities have been outsourced to suppliers,

although the whole supply chain is strictly controlled. At the other end of the scale, German

drugstore company dm-drogerie markt is successfully implementing a strategy of vertical

integration by expanding its portfolio with the private label Balea. Apparently there is need to

understand which activities and competencies one should outsource in the different business

models.414 Is there a preferred strategy that optimizes support for operations? What are the

key operational success factors for the various strategic orientations?415 These questions are

not addressed in the present study, yet their relevance is expected to be high. Researchers

should therefore be encouraged to continue research into these topics.

412 McGrath (2011), p. 96-98. 413 Casadesus-Masanell/Ricart (2011), p. 103; Park/Ro (2011), p. 289. 414 Zirpoli/Becker (2011), p. 59. 415 Sinfield/Calder/McConnell/Colson (2012), p. 89.

143

6 Summary

• Academic research has identified several operational drivers in manufacturing companies

that have an effect on either the physical production of goods or its distribution: working

capital requirements, manufacturing performance, supply chain performance and supply

chain risk.

• Despite the fact that these four operational drivers have been operationalized in empirical

studies and theoretical discourses, no-one has yet conducted a holistic study of how they

interrelate and what specific contribution they make to overall firm performance.

• The key question addressed by this study is therefore how companies should align these

operational drivers of excellence to achieve superior firm performance. The results are

based on a universe of 274 top-class manufacturing companies based in Germany,

Switzerland or Austria and more than 15 interviews with top executives.

• Based on the Configurational Theory it is hypothesized that only those firms maximize

firm performance that have properly aligned their operations. All named operational

drivers are expected to significantly impact firm performance – Alongside their impact on

firm performance it is hypothesized that manufacturing performance fosters supply chain

performance and ultimately shorter cash conversion periods. Supply chain risk is

hypothesized to drive longer cash conversion periods.

• A structural equation model was applied to reveal existing relationships. The results appear

to be highly significant and suggest that all relevant goodness-of-fit criteria are met. The

results suggest that all hypothesis are confirmed. First, all drivers correlate positively to

firm performance and, second, the drivers affect each other such that manufacturing

performance fosters supply chain performance and supply chain performance fosters lower

working capital requirements. Supply chain risk is expected to drive higher working capital

requirements.

• By consequence, a trade-off exists: Strong firm performance requires a significant level of

supply chain risk at the expense of working capital performance. This trade-off emphasizes

the specific need for comprehensive supply chain risk steering, alongside the more obvious

need to continuously improve efficiency and effectiveness in manufacturing and supply

chains.

• Companies that accept a reasonable level of supply chain risk while maintaining high

manufacturing and supply chain performance outperform the lowest-performing reference


144

group by 14% in terms of sales, sales growth, profitability and market share ("firm

performance").

• The need for comprehensive supply chain risk steering has become especially acute in

response to recent changes in customers' demand patterns, such as the desire for more

individualized products, shorter times to market and shorter product lifecycles, all of which

drive supply chain complexity – However, most companies still have no active cross-

functional supply chain risk steering instance that creates transparency and optimizes risk-

profitability decisions. This assumption, based on the study outcomes, was confirmed

during the expert interviews. Therefore, companies are recommended to review their

existing business models and implement cross-functional supply chain risk steering in

terms of policy, organization, processes and execution.

• Above all, further constructs that intermesh with the physical production or distribution of

goods should be incorporated. Factors of relevance could be sales, planning or controlling,

for example. Similarly, the specific impact of a company's strategy (e.g. its business

model) on the operational drivers is very important but was not investigated in detail in this

study.

145

Appendix 1: Questionnaire of the empirical survey

C. Faden, Optimizing Firm Performance, Schriften zum europäischen Management,DOI 10.1007/978-3-658-02746-9, © Springer Fachmedien Wiesbaden 2014

146

147

148

149

150

151

Appendix 2: Participants expert interviews

Expert name Company Job title Date

Frederico Ruckert Tognum AG Coordinator of the group wide net working capital initiative

21/09/2011

Dr. Antje v. Dewitz Vaude GmbH & Co. KG CEO 21/09/2011

Bernhard Scherer Zeppelin Systems GmbH CEO 22/09/2011

Bernd Baader MTU Friedrichshafen GmbH Head of Logistics 27/09/2011

Rainer Dickert ZF Friedrichshafen AG Head of Logistics 28/09/2011

Oliver Knapp Roland Berger Strategy Consultants GmbH

Partner 30/09/2011

Frederico Ruckert Tognum AG Coordinator of the group wide net working capital initiative

08/11/2011

Thomas Rinn Roland Berger Strategy Consultants GmbH

Head of Competence Center Operations Strategy

11/11/2011

Dr. Bruno Niemeyer J. Wagner GmbH CFO 22/11/2011

Rainer Dickert ZF Friedrichshafen AG Head of Logistics 28/11/2011

Dr. Steffen Kilimann Roland Berger Strategy Consultants GmbH

Senior Project Manager 01/12/2011

Prof. Dr. Hubertus Christ - - 02/12/2011

Franz Hermes ZF Friedrichshafen AG Head of Controlling 02/12/2011

Dr. Michael Zollenkop Roland Berger Strategy Consultants GmbH

Principal 09/12/2011

Hartmut Jenner Alfred Kärcher GmbH & Co. KG

CEO 15/02/2012

Dr. Joachim Stark Lapp Holding AG Head of Quality 23/02/2012


153

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