CO – Supply Chain

Wie Nestlé Statistische Prognosemodelle benützt um Kunden und Konsumenten sichere und frische Produkte zu liefern

Marcel Baumgartner marcel.baumgartner@nestle.com

Baden, May 7th, 2013

SAS Forum Switzerland

Your speaker

• Married, two children

• Applied Mathematics, EPFL (1992)

• Masters in Statistics, Purdue University (1995)

• Nestlé Research Center, Vers-chez-les-Blanc (1994 – 2001)

• Nestlé Headquarters, Corporate Unit for Supply Chain (2001 - …)

• Current Role:

Global Lead for Demand Planning Performance and Statistical

Forecasting

• Co-president of the Swiss Statistical Society (www.stat.ch)

• President of the section Business & Industries of the Swiss Statistical

Society

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Agenda

• Nestlé and Nestlé in Switzerland

• Supply Chain Management

• S&OP and Forecasting

• Applying Statistical Forecasting

• Experiences with SAS

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Nestlé at a Glance

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CHF 92.2 billion in sales in 2012

330,000 employees in over 150 countries

468 factories in 86 countries

2’000 local and global brands

Over 1 billion Nestlé products sold every day

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Nestlé vs. our Competitors

Top Food & Beverage Companies in 2011

Food &

Bevera

ge s

ale

s in b

n U

SD

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The Nestlé Story

Nestlé requires a flexible

organisation to fulfill

business needs effectively

Zone Asia, Oceania, Africa

Zone Americas

Zone Europe

Geography… Zones, Regions

Products… Strategic Business Units

Supply Chain & Procurement

Finance

Market ing & Sales

Technical

R&D

Human Resources

Functions…

Etc…

Nestlé in Switzerland

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Supply Chain Management

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Customers

Suppliers

(Raw and

Packaging

Materials)

Nestlé

Supply Chain

Marketing

Finance

Sales

Manu-

facturing

Physical Objects

Information

The Supply Chain Solves Trade-Offs

Two main Key Performance Indicators:

• Customer Service Level (% of orders completely delivered)

• Holding Inventory

To improve Customer Service, you can hold more inventory.

But inventory costs money: cash is blocked, physical storage,

risk of ageing products.

The overall goal of Supply Chain Management is to improve

Customer Service whilst optimizing the costs, by solving this

trade-off.

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The Need for Forecasting

At Nestlé, most of our production is driven by "Make to Stock", and

not "Make to Order".

We often have to produce large batches, both for cost (larger

batches = smaller costs per unit) and sometimes quality reasons.

Therefore, we need to forecast the future orders of our clients to have

the right volumes of the right product, at the right location, at the

right moment in time.

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Balance Demand and Supply

Sales and Operations Planning (S&OP)

• Align demand with supply and financial

plans (budgets, targets, …)

• Integrate operational plans with

strategic plans

• Align product mix with total volume

• Ability to act pro-actively

At Nestlé, this is a combination of

Demand & Supply Planning and Monthly

Business Planning.

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Available through

www.ibf.org

Forecasting: Judgmental vs. Statistical

There are basically two ways to make forecasts about future volumes

of our products:

• Judgmentally (manually, subjectively, …)

• Statistically

Research shows that statistical forecasts, based on adequate

historical data, can perform better. Particularly for low volatile

products.

Judgment will always be necessary, but it needs to be used wisely.

See this research from Robert Fildes et al.

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Six Truths about Forecasting

1. The future is never exactly like the past.

2. "Complex" statistical models fit past data well but

don't necessarily predict the future.

3. "Simple" models don't necessarily fit past data well

but predict the future better than complex models.

4. Both statistical models and people have been

unable to capture the full extent of future

uncertainty and been surprised by large forecasting

errors and events they did not consider.

5. Expert judgment is typically inferior to simple

statistical models.

6. Averaging (whether of models or expert opinions)

usually improves forecasting accuracy.

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Volatility is driving Forecasting Performance

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Fore

cast

Perf

orm

ance

Volatility of Demand

SAS told us this: the COMET plot !

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© Mike Gilliland

The Animal Farm: Driving Behavior !

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Originally published by Whirlpool in a SAP conference in 2009.

Forecast Value Added (© Mike Gilliland, SAS)

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Process

Step Error

FVA vs.

Naïve

FVA vs.

Statistical

Forecast

Naïve

Forecast 25%

Statistical

Forecast 20% 5%

Demand

Planner 30% -5% -10%

FVA = The change in a forecasting

performance metric that can be attributed

to a particular step or participant in the

forecast process.

Applying Statistical Forecasting @ Nestlé Started early 2000, we are at stage 4

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Explain Demand

Planners how

the methods

available in SAP

APO DP work

Give Demand

Planners clear

guidelines to

apply, without

explanations

Provide fully

automatic

method

available in R, based on the

'forecast' library of

Prof. Rob J.

Hyndman

Create a new

role of a

Demand

Analyst, fully

dedicated to

statistical

forecasting

The Expert in Exponential Smoothing

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In R, check out the

package 'fpp' and the function ets().

Simply brilliant !

otexts.com/fpp/

www.exponentialsmoothing.net

SAS Forecast Server and SAS Forecast Studio

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SAS Forecast Server Highlights

• Highly Scalable: only a few Statistical Forecasters to cover a large

geography.

• Highly Automatic Model Selection, very little manual intervation.

• Hierarchial Reconciliation in different directions.

• Can go down to very low levels in customer hierarchy.

• Includes possibility to add promotion mechanics to statistical

models.

• Events (e.g. Christmas) can be included.

• Statisticians love it !

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A Strong Feature: Choosing the Appropriate Reconciliation Strategy

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Bottom-up

Top-down

Fresh Results from one of our MArkets

• We use SAS Forecast Studio to produce forecasts, using default

paratemers (e.g. ARIMA and ESM, minimizing MAPE, no hold-out

sample, …), and 3 years of uncleaned order history.

• Hierarchy:

Market -> Category -> BaseItem -> Customer

• Focus is on accuracy on the Baseitem level. We test middle-out

Baseitem versus bottom-up from Customer.

• Metric:

our Nestlé Demand Plan performance indicators simulated for

August 2012 to January 2013, error measured on level Baseitem,

time-aggregated for 6 months, weighted, lag one and three month.

Compared with the performance of the planners in the market.

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Results

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• DPA: Demand Plan Accuracy

• SAS Bottom Up (BU) is better than Middle-Out (MO).

Surprise !

• SAS comes close to Market performance.

Fcst

HistFcstDPA

1

Results with Segmentation

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• Horses (61% of total volume): good performance of statistical

methods, particularly at M-3.

• Mad Bulls: planners have more information !

Handling Promotions: Need for Causal Methods

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Step 1: Forecast Scan Data using causal

time series methods (e.g. Unobserved

Components UCM in SAS Forecast

Server) and explanatory variables like the

retail price

Step 2: Translate these forecasts into ex-

factory orders, using ad-hoc phasing rules.

Swiss Statistical Society (www.stat.ch): 25 Years !

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3 sections:

•Official Statistics

•Eduation and Research

•Business and Industries

Roughly 450 members.

Join us at the Swiss Days of

Statistics 2013 in Basel

(October 16 to 18, 2013).

www.statoo.ch/sst13

We celebrate

our 25 years, in the year of

Statistics !

Thank You !

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