International Industrial Management I - Location Decisions Ibwl.univie.ac.at/fileadmin/user_upload/lehrstuhl_ind_en_uw/lehre/... · A class of locational choice problems Factors for

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International Industrial Management I -

Location Decisions I

| Prof. Franz Wirl |

Email: [email protected] Homepage: http://bwl.univie.ac.at/ieu

mailto:[email protected]

Int. Ind. Management I Chair Industry, Energy & Environment

| Prof. Wirl WS 2015/16 Page 2

Overview | Location Decisions

Choice of locations Introduction A class of locational choice problems Factors for locations Methods

Check lists Benefit analysis (simple, additive) Location break-even analysis Transportation method Steiner-Weber Model Location-alloction Model of Cooper and extensions Hotelling Model

Choice of locations with the firm The basic problem CRAFT Layouts



Location Decisions - Objective

Choose the location that maximizes

the firm’s benefit

There are only three important things concerning locations: 1) Location, 2) Location, 3) Location.



Location Decisions

Importance: Location decisions do not only result in real estate and investment costs but influence in particular fixed and variable costs. Transportation costs

rentals

Wages

Taxes, etc.

Links to the environment (universities, clusters, etc.)



Choosing the United States by Michael E. Porter and Jan W. Rivkin, HBR, March 2012, 81-93

In deciding whether to move existing business activities out of the United States, our HBS alumni respondents reported

certain factors made the difference. Leading reasons for moving out of the U.S. Lower wage rates (in the destination country) 70% Proximity to customers 34% Better access to skilled labor 31% Higher productivity of labor 30% Faster-growing market 29% Lower tax rates 25% More-generous incentives from local authorities 24% Fewer or less expensive regulations 22% Proximity to suppliers 19% Proximity to other company operations 16% Leading reasons for not moving Proximity to U.S. customers 32% Less corruption 30% Better access to skilled labor 29% Greater safety for people and property 27% Stronger intellectual property protection 24% Proximity to home market 22% Similar language and/or culture 22% Better transportation infrastructure 19% Proximity to other company operations 18%



Example: Automobile Industry

Source: Dyer, J.H.,Dedicated Assets: Japan‘s Manufacturing Edge , Harvard Business Review, Nov.-Dec., 1994, S.174ff.



Example: Automobile Industry cont.

Source: Dyer, J.H., Dedicated Assets: Japan‘s Manufacturing Edge , Harvard Business Review, Nov.-Dec., 1994, S.174ff.



Example: Automobile industry cont. Comparing locations of production and organizational units of the

automobile industry in Japan and the US (Dyer, 1994) finds and argues the following:

The huge success of the Japanese car industry is to a large extent due to the close relations with its suppliers and in particular the geographical vicinity.

There exist large differences concerning the average distance between locations of Japanese and American car manufacturers. In particular, the entire ring of Toyota and its suppliers fits between two GM locations.

Inventory as a share of turnover is much larger at US car producers which implies more bound capital. These distances in turn make it hard to implement Just-in-Time Management (see later), which further increases inventory costs.



Example: Automobile industry cont.

Closeness implies much better contacts between relevant people and organizations (including suppliers). It is a grave misunderstanding that personal contacts become redundant once an industry can actually choose its supply in aglobally integrated world by a mouse click; remember this for the following discussion about clusters. Biology/Evolution!!!

If GM had a similar ratio between inventory and sales it would save US$ 6 billions which amounts to annula cost saving of the order between US$ 400 – 500 Millions depending on costs of capital ranging between 6% to 8%.



Introductory Example I

Table: Example of a location decision l

Feasible locations

Distance from procurement center

Transport costs (in 1000 MU)

Distance to center of demand

Transport costs (in 1000 MU)

Sums

Aachen

150

(150*1500*1)= 225

650

(650*1000*1,2)= 780

1005= 4th rank

Braunschweig 270 405 625 750 1155= 6th rank

Dortmund 10 15 650 780 795= 1st rank Erfurt 420 630 380 456 1086= 5th rank

Munich 650 975 10 12 987= 3rd rank

Nürnberg 480 720 170 204 904= 2nd rank

Assumptions: Procurement centered around Dortmund. Volume = 1.500 t and transport cost are 1 MU per t and km. Demand in Munich, Volume = 1000 t, transport cost are 1.2 MU per t and km.



Interdependences (Source Lüder, S.31)

Produktionsprogramm

Produktpreise

Transportwege Transportmengen

Transportmengen

Innerbetriebliche Standorte*

Löhne, Steuern usw.

Investitionsprogramm

Investitionsausgaben je Projekt

Innentransportkosten

Kapitaleinsatz

Sonstige aufwandsgleiche Kosten

+ (Aufwand≠Kosten)

Externe Transportkosten

Aufwand

Ertrag

Profitability of a firm for a given configuration of locations

*: Optimierungsproblem

* Optimization problem



Assumptions (given): A set of locations:

A set of organisational units:

Location of the organisational units:

Objective: Find a mapping that assigns to each unit bi a location

from the set S that maximizes a given objective function:

Mathematical formulation of Location Decisions I

{ }njsS j ,,1; ==

{ }mibB i ,,1; ==

( ) Ssbs ji ∈=

( ) SbsbSB

ii ⊂→

→∑:



Mathematical formulation of Location Decisions II

Further assumptions: O is the set of given locations that are connected to locations bi e.g. customers:

The sets O, S and B are independent (in practice however there are many

interdependencies and feedbacks). Further constraints such as those on capacity.

Objective: Maximize/Minimize

e.g. the locations of the organizational units are chosen in order to minimize the transport costs.

e.g. the (incremental) benefit from the choice of a particular location or locations should be maximized.

{ }rpoO p ,,1, ==

( ) ( ) ( )[ ]mbsbsbsfZ ,,, 21 =



Examples of Constraints: Organizational units that must be close to the market

Public Police, ambulance, fire brigade post

Retail and Services Fast food, gas stations, super markets Pharmacies, shopping malls.

Services M.D., lawyers, barbers Banks (?), mechanics, hotels



Evaluation of locations

Methods

Check lists

(Cost-) Benefit analysis

Location Break-Even Analysis

Transportation Method

Steiner-Weber Model

Location-Alloction Model von Cooper

Hotelling

Etc.



Factors crucial for locations

Market size

Expected profits

Openess of markets

Stability

bureaucracy

Labour (quality and work ethics)

Infrastructure

Technological know how

Labor costs

Resources, primary inputs

Availability of qualified labor

Reserach institutes

Quality of life

Venture-Capital

Costs

Technology

IT-Infrastructure

Bureaucracy

Suppliers & partners

Subsidies

Arthur Anderson Deloitte Touche



(Cost-) Benefit Analysis - additive

Four steps: 1. Scaling: Evaluation via scale (say 1 – 10) the benefits of location with respect

to a particular factor which allows to rank and evaluate also qualitative factors. 2. Weighting: Assign weights {gi} to each individual factor entering the analysis

and to differentiate between important and less important ones. 3. Aggregate: Multiply points the scale in (1) with the weights in (2) and add up

which gives the measure of benefit associated with a particular location: N(sj) = n1j*g1 + n2j*g2 + .....

4. Choose the location with the highest benefit = highest value of N.



A simple example Labor Sales Subsidies

Locations s1 7 2 1

s2 5 1 9

s3 10 4 6

s4 5 7 8

Weights 0.3 0.5 0.2

N(s1) = 7*0,3 + 2*0,5 + 1*0,2 = 3,3 N(s2) = 5*0,3 + 1*0,5 + 9*0,2 = 3,8 N(s3) = 10*0,3 + 4*0,5 + 6*0,2 = 6,2 N(s4) = 5*0,3 + 7*0,5 + 8*0,2 = 6,6 Therefore, location 4 should be chosen.

Extension: Multi-criteria optimization (or decision making)



Check lists

Precise evaluation of the benefits associated with each factor. Similarly to the the above benefits analysis – hierarchical



Benefit analysis

Weights allow to mitigate for outliers Ranking without weights:

C – B – A - D Ranking with weights:

A – C – D – B



Location Break-Even Analysis I

Comparison of the costs at different locations depending on the rate of utilization.

Example Production of a new Volkswagen model. 3 potential locations with their respective costs Assume a sales price Profit:

Standort Fixe Kosten Variable Kosten Verkaufspreis E(Menge)Poznan € 3.000.000 € 7.500,00 € 12.000,00 2.000Pamplona € 6.000.000 € 4.500,00Wolfsburg € 11.000.000 € 2.500,00 Standort Gesamtkosten* Gewinn*

Poznan € 18.000.000 € 6.000.000Pamplona € 15.000.000 € 9.000.000Wolfsburg € 16.000.000 € 8.000.000

* bei einer Menge von 2.000 Stk.



Location Break-Even Analysis II

1. Given a fixed quantity of 2000 pieces Pamplona yields the highest profits.

2. Since the assumption of 2000 pieces depends on a marketing study it is unreliable. Therefore, the management wants to know whether Pamplona remains the most profitable with less or higher production.

Location Break-Even Analysis This procedure calculates the intersections of the cost curves

associated with the different locations depending on the rate of production.

This yields capacity bounds where which location is efficient as well as the minimal costs over all locations contingent on production.



Location Break-Even Analysis III

PoznanPamplona

Wolfsburg

1.000.000

4.000.000

7.000.000

10.000.000

13.000.000

16.000.000

19.000.000

22.000.000

0 500 1000 1500 2000 2500 3000 3500

Menge (Stk.)

Ges

amtk

oste

n

Poznan

Pamplona

Wolfsburg

Depending on production volumes different locations turn out to be the most profitable ones:



1) Countries

2) Regions

3) Final location

Sequential (hierarchical) location decisions



Match Product & Country

Braun Household Appliances

Firestone Tires Godiva Chocolate Haagen-Dazs Ice Cream Jaguar Autos MGM Movies Lamborghini Autos Alpo Petfoods

1. Great Britain 2. Germany 3. Japan 4. United States 5. Switzerland 6. India 7. Italy 8. Denmark



Match Product & Country

Braun Household Appliances

Firestone Tires Godiva Chocolate Haagen-Dazs Ice Cream Jaguar Autos MGM Movies Lamborghini Autos Alpo Petfoods

1. Great Britain 2. Germany 3. Japan 4. United States 5. Switzerland 6. India



A few international comparisons

Productivity Growth Countries Cities Economic liberty Corruption



Christian Thimann, The Microeconomic Dimensions of the Eurozone Crisis and Why European Politics Cannot Solve Them Journal of Economic Perspectives—Volume 29, Number 3—Summer 2015—Pages 141–16





Growth Competitiveness Index – 2006-2007 – Top 20 World Economic Forum



Growth Competitiveness Index – 2006-2007, Rank >100



Business Competitiveness Index – 2006-2007



Global Competitiveness Index 2009



Business Competitiveness



International Productivity by Sectors

Martin Neil Baily und Robert M. Solow, International Productivity Comparisons Built from the Firm Level, Journal of Economic Perspectives, 15/3, 151-172, 2001.



Labor Productivity and Wages



International labor costs in automobile industry €/h

42.2935.52

24.68 28.9436.61 30.71 34.51

21.9912.06

31.46 31.6623.95

€ 0.00€ 10.00€ 20.00€ 30.00€ 40.00€ 50.00

Germ

any

Fran

ce Italy

The N

ether

lands

Belgium UK

Sweden

Spain

Portu

gal

Austria

U.S.A

Japa

n



Corruption

5 Best: Denmark New

Zealand Sweden Singapur Finland

5 Worst: Afghanistan Haiti Iraq Myanmar Somalia

Corruption index 2008

Denmark / New Zealand/ Sweden 9,3 Singapore 9,2 Finland/Switzerland 9,0 Iceland/Netherlands 8,9 Australia/Canada 8,7 Luxembourg 8,3 Austria/Hong Kong 8,1 Germany/Norway 7,9 Ireland/UK 7,7 Belgium/Japan/US 7,3 Afghanistan 1,5 Somalia 1,0 10 = No corruption



Economic Freedom I

2010



Economic Freedom II



Economic Freedom - graphical HONG KONG keeps its top position in the Economic Freedom Index compiled by the Fraser Institute, a Canadian think-tank. The index ranks the policies of 141 countries according to how much they encourage free trade, both internally and with other territories. Countries with fewer taxes, strong property rights, low regulation and sound money score best. Britain and America tie with Canada for fifth place in the list. Germany is ranked 18th—on a par with El Salvador but above Japan. Most of the low-ranking countries are African, except Myanmar and Venezuela, which are both in the bottom ten.



Ranking of Cities

Source: European Cities Monitor 2006: Citiy 2006 2005 1990 Index2006

London 1 1 1 0,91

París 2 2 2 0,59

Frankfurt 3 3 3 0,36

Barcelona 4 5 11 0,27

Brussels 5 4 4 0,24

Amsterdam 6 6 5 0,23

Madrid 7 7 17 0,20

Berlín 8 8 15 0,18

Munich 9 9 12 0,18

Zurich 10 10 7 0,16



Another ranking of cities

London and Paris maintain their position as Europe's two top cities to locate a business Warsaw can expect the biggest influx of international companies over the next five years.

Barcelona is also the city perceived as doing the most in Europe to improve itself as a business location, followed by Prague and Madrid



Hardship Rating



Austria (WIFO)

Advantages Disadvantages

Part of the common market Bureaucracy

Active in Eastern Europe Duration until receiving operating permits

Availability of high quality labor Costs of telecomm (not any more!)

Availability of highly qualified labor High costs for unskilled labor

Consensus seeking society Environmental regulations

Rule of law Lack of venture capital

Environmental quality Little competition in energy markets

Cultural and leisure activities Lack of reforms (and willingness to reform)

Public safety Public administration

Political stability (?) Energy costs



Austria (US-Investor Confidence Study)

Advatantages Disadvatantages Knowledge of foreign languages Tax deductions of investments

Access to Eastern European markets Real estate prices

Sales in Austria Cost of living

Qualifications and effort (of labor) Operating permit (length)

Transport Labor permits for Non-EU residents

Culture and leisure activities Opening hours

Political stability Income taxes

Social peace Labor laws

Costs of electricity

Wages, payroll taxes and other ancillary labor costs



Eastern Europe vs Asia •As in the previous five years, economies in eastern Europe and Central Asia have consistently seen the fastest pace of positive reform. •On average, it takes 21 days to register a business in eastern Europe, which is 27 days faster than in East Asia. Setting up a company in Indonesia costs 77.9% of the average annual income per person; in Georgia it costs 4%—though there is the small matter of political risk to factor in. Firing a worker costs an average of 53 weeks’ salary in East Asia, compared with 27 in eastern Europe. All this cutting of red tape has brought results: Poland now has as many registered businesses relative to its population as Hong Kong does. •Eastern Europe’s rapid progress due to the accession requirements imposed by the European Union (EU), e.g., the EU requires new members to create a “one-stop shop”. Before Macedonia became a candidate for EU membership in 2005, it took 48 days to start a business there. After three years of reforms, it now takes nine days. •East Asian countries still have the edge in some respects: it is easier to move goods across their borders, for example. Businesses in East Asia also face lower taxes. Taxes on profits in eastern Europe are among the lowest in the world, typically around 10%, but labour taxes and compulsory pension contributions increase the overall tax burden on business. •Of course, a few East Asian economies are still miles ahead of eastern Europe. Singapore 1st, Hong Kong 4th but Georgia, Estonia, Lithuania and Latvia in the top 30, Russia = 120th and Azerbaijan was the top reformer.



Countries

Regions

Specific location

1. Political risks, regulation, incentives. 2. Cultural and economical. 3. Location relative to markets (sales and procurement). 4. Labor: availability, productivity, costs, behavior. 5. Availability of suppliers, communication, energy. 6. Exchange rates and their risks. 1. Specifics views and demands of the firm. 2. Atractiveness of region (culture, climate, taxes, etc.) 3. Labor: availability, productivity, costs, unions. 4. Availability and costs of materials. 5. Environmental regulation at federal and city level. 6. Incentives (by regions, cities). 7. Availability and distance to primary inputs. 8. Costs for real estates and construction. 1. Location specific costs contingent on the size of the unit. 2. Air, rail, road and water transport networks. 3. Zoning, in particular restrictions. 4. Availability and distance to suppliers and service providers. 5. Environment and environmental constraints.

Factors crucial for location decisions



Case Study | BMW Spartanburg, USA I

Location decision of BMW Production of BMW Z3

in the USA, mid 1990ies. Expansion to include

BMW X5 und Z4.



Case Study | BMW Spartanburg, USA II

3) Final decision for Spartanburg

2) Choice of South Carolina

1) Choice of the US



Case Study | BMW Spartanburg, USA III Factors decisive for choosing USA

Market: Market size: USA is the world‘s largest market for luxury cars Growing versus declining markets: US growing market due to baby

boomers. Labor:

Lower labor and production costs: (US: US$ 17/h, DEU: US$ 27/h)

High labor productivity (Holidays: US 11, Germany: 31)

Additionally: Low transport costs (US$ 2.500,- less per vehicle) New facilities improve productivity and thus lower the costs per car

(around US$ 2.000-3.000.-) Insurance (‚hedging‘) against high Dollar-Euro exchange rates.



Case Study | BMW Spartanburg, USA IV Factor in favor of South Carolina

Labor: Low wages in South Carolina

Public subsidies: US$ 135 Mio. in terms of tax deductions.

Free trade zone: Duties neither on imported goods nor on car

exports. Infrastructure:

Access to freeway and via the freeway to the airport Charlotte from which Lufthansa operates direct flights to Munich. Port Charleston serves for exports and the imports of engines and gearboxes from Europe

8585

2626

85



Labor Productivity

Keyword: level of labor costs

Cheaper employees => higher profit?

However, cheap labor is NOT everything – countries with cheap labor are often characterized by low labor productity that can erode partially or totally the advantage in terms of labor cost. Compare: Gregory Clark, Farewell to Alms, textile production in England and colonial India. South vs North Italy (Alfa Romeo Sud, see below).

‚Skills‘ of workers, good infrastructure, and technology are often more important to many industries than plain labor costs.



Case Study | Labor productivity

Quality Coils Inc., CT, USA Coil production for electrical equipments 2 potential locations:

Expanding a the present location in Connecticut New factory in Juarez (one of the fastest growing cities in Mexiko along the US border due

to the Maquiladoras) Comparison based on labor costs:

Historical Example: Alfa Romeo - Alfasud

Wage/d Output/worker

Labor cost/ piece

Connecticut Plant US$ 70.- 60 units US$ 1.17/unit

Juarez Plant US$ 25.- 20 units US$ 1.25/unit

unit per costday) per (units tyProductivi

day per Cost LabortyProductivi Labor ==



Cos

t Red

uctio

n C

onsi

dera

tions

High

Low High Low

Local Responsiveness Considerations (Quick Response and/or Differentiation)

Standardized product Economies of scale Cross-cultural learning Examples Texas Instruments Caterpillar Otis Elevator

Global Strategy Transnational Strategy Move material, people, ideas

across national boundaries Economies of scale Cross-cultural learning Examples Coca-Cola Nestlé

International Strategy

Import/export or license existing product

Examples U.S. Steel Harley Davidson

Multidomestic Strategy Use existing

domestic model globally Franchise, joint ventures,

subsidiaries Examples Heinz The Body Shop McDonald’s Hard Rock Cafe

Four International Operations Strategies



Steiner-Weber Model I

Geometric, continuous Model based on the works of the German geometricians and regional scientists: Launhardt (1882), Steiner-Weber (1909) & Lösch, Christaller (1940er)

Model assumptions: The set location where to delivery to, is given. The set of potential locations (S) is the entire plane (infinitely many, actually

uncountable many). The decision involves only a single location s. Transport costs are proportional to the Euclidean distance.

Problem – minimize transport costs: Find the optimal location determined by the coordinates x*, y*

that minimizes from the chosen location the transport costs to the sinks Op with the coordinates xp, yp (p=1,…r).

{ }rpoO p ,1, ==

Ss ∈*



Steiner-Weber Modell II

The objective function depends on: Volume (delivered or for delivery) from the given location p, ap, Normalized transport cost (e.g. EUR/ton/km) kt, Distance measures in terms of direct connection (Euclidean), dp.

Objective

∑∑==

==r

pppt

r

ppTT dakKK

11**

( ) ( )22ppp yyxxd −+−=

dp

xp

x

yp y

(xp, yp)

(x, y)



Steiner-Weber Modell III

Objective min:

Differentiation with respect to x and y and equating to zero yields the first order optimality condition.

Result: two nonlinear equations that require numerical means (e.g. Newton‘s iteration scheme)

Fortunately, Excel does it! (via the Solver).

( ) ( )∑=

−+−=r

pppptTyx

yyxxakK1

22

,*min

( ) ( )

( ) ( )∑

∑

=

=

=−+−

−=

∂∂

=−+−

−=

∂∂

r

p pp

ppt

T

r

p pp

ppt

T

yyxx

yyak

yK

yyxx

xxak

xK

1

!

22

1

!

22

0)(

0)(



Steiner-Weber Modell V - extensions

Nonlinear transport costs but distance depending, e.g., quadratic, or including set up costs for each connection:

It can make sense to link the transport costs to particular sinks p to account for differences in deliveries (e.g., scrap versus sensitive final prducts): A further extension is to allow for the deliveries of multiple goods from the source (i.e., the sought location) s to sinks p. Accounting for the last two recommendations leads to the following cost minimization:

pqkqq

qpT sink toproduct ofcost transport products,,1

:where

=

( ) ( ) ∑∑ ∑∑== ==

−+−==q

qqpqpT

q

q

r

ppppqpqpT

r

pTyx

akyyxxdakK11 1

22

1,***min

( )ppTpT dkk =

( )pkk tt =

Documents

International Industrial Management I - Location Decisions Ibwl.univie.ac.at/fileadmin/user_upload/lehrstuhl_ind_en_uw/lehre/... · A class of locational choice problems Factors for