06 baumgartner - statistischen prognosemethoden nestle

8/11/2019 06 Baumgartner - Statistischen Prognosemethoden Nestle

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Use of StatisticalForecasting Methods to

Support the DemandPlanning Processes atNestl

Predictive Analytics Konferenz, Wien

September 2012

[email protected]


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Agenda

Nestl

Supply Chain Management

Planning and Forecasting Applying Statistical Forecasting

Experiences with SAS

Demand Analysts and CompetenceCenters

2 June 2012


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Nestl at a Glance

3

CHF 83.6 billion in sales in 2011

328,000 employees

461 factories

10,000 brands

1 billion Nestl products sold every day


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4

Nestl vs. our Competitors

Top Food & Beverage Companies in 2011

Food&Beveragesalesin

bnUSD


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5

The Nestl Story


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Nestl requires a flexible

organisation to fulfill

business needs effectively

Zone Asia, Oceania, Africa

Zone Americas

Zone Europe

GeographyZones, Regions

ProductsStrategic Business Units

Supply Chain & Procurement

Finance

Market ing & Sales

Technical

R&D

Human Resources

Functions

Etc


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Supply Chain Management

7

Customers

Suppliers

(Raw and

Packaging

Materials)

Nestl

Supply Chain

Marketing

Finance

Sales

Manu-

facturing

Physical Objects

Information

June 2012


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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 CustomerServicewhilst optimizing the costs, by solving this trade-off.

8 June 2012


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The engine of Supply Chain: Planning

Forecast:A description of where we think we are

heading, based on current assumptions. The

reason to forecast is to make informed

decisons.

Plan:

A set of related future actions designed to

reach an objective.

Planning:The process of defining a set of future

actions with the aim of achieving an

objective.

9 June 2012


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The Need for Forecasting

At Nestl, 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 (largerbatches = smaller costs per unit) and sometimes quality

reasons.

Therefore, we need to forecast the future orders of our clientsto have the right volumesof the right product, at the right

location, at the right momentin time.

June 201210


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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 Nestl, this is a combination of Demand &

Supply Planning andMonthly Business

Planning at Nestl.

June 201211

Available through

www.ibf.org


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Forecasting: Judgmental vs. Statistical

There are basically two ways to make forecasts about future

volumes of our products:

Judgmentally(manually, subjectively, )

StatisticallyResearch 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 usedwisely. See this researchfrom Robert Fildes et al.

June 201212
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V92-4VC73VJ-1&_user=2216264&_coverDate=03/31/2009&_rdoc=3&_fmt=high&_orig=browse&_srch=doc-info(http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V92-4VC73VJ-1&_user=2216264&_coverDate=03/31/2009&_rdoc=3&_fmt=high&_orig=browse&_srch=doc-info(


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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 tocapture 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.

June 201213
http://www.amazon.com/gp/reader/1851687203/ref=sib_dp_kdhttp://www.amazon.com/gp/reader/1851687203/ref=sib_dp_kdhttp://www.amazon.com/gp/reader/1851687203/ref=sib_dp_kdhttp://www.amazon.com/gp/reader/1851687203/ref=sib_dp_kdhttp://www.amazon.com/gp/reader/1851687203/ref=sib_dp_kdhttp://www.amazon.com/gp/reader/1851687203/ref=sib_dp_kdhttp://www.amazon.com/gp/reader/1851687203/ref=sib_dp_kdhttp://www.amazon.com/gp/reader/1851687203/ref=sib_dp_kdhttp://www.amazon.com/gp/reader/1851687203/ref=sib_dp_kdhttp://www.amazon.com/gp/reader/1851687203/ref=sib_dp_kdhttp://www.amazon.com/gp/reader/1851687203/ref=sib_dp_kdhttp://www.amazon.com/gp/reader/1851687203/ref=sib_dp_kd


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Volatility is driving Forecasting Performance

June 201214

ForecastPerfo

rmance

Volatility of Demand


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SAS told us this: the COMET plot !

June 201215

Mike Gilliland


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The Animal Farm: Driving Behavior !

June 201216

Originally publishedby Whirlpoolin a SAP conference in 2009.
http://www.sap.com/italy/about/events/2009_7_2_lean_production/pdf/Whirlpool.pdfhttp://www.sap.com/italy/about/events/2009_7_2_lean_production/pdf/Whirlpool.pdf


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Forecast Value Added (Mike Gilliland)

We have very good methodology to measure theforecast performance.

FVA = The change in a forecasting performance metric

that can be attributed to a particular step or participantin the forecast process.

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Demand

History

Nave

Forecast

Statistical

Forecast

Demand

Planner


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Generic FVA Report

June 201218

Process

StepError

FVA vs.

Nave

FVA vs.

Statistical

Forecast

Nave

Forecast 25%

Statistical

Forecast20% 5%

Demand

Planner30% -5% -10%


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

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Explain Demand

Planners howthe methods

available in SAP

APO DP work

Give Demand

Planners clearguidelines to

apply, without

explanations

Provide fully

automatic

methodavailable in R,based on the

'forecast' library of

Prof. Rob J.

Hyndman

Create a new

role of a

DemandAnalyst, fully

dedicated to

statistical

forecasting


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The Expert in Exponential Smoothing

June 201220

In R, check out the

package 'fpp' andthe function ets().

Simply brilliant !

otexts.com/fpp/

www.exponentialsmoothing.net
http://otexts.com/fpp/http://www.exponentialsmoothing.net/http://www.exponentialsmoothing.net/http://www.exponentialsmoothing.net/http://otexts.com/fpp/


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SAS Forecast Server and Forecast Studio

June 201221


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SAS Forecast Server Highlights

Highly Scalable

Highly Automatic

Hierarchial and Temporal Reconciliation

Event Handling Included

Contains Causal Time Series Forecasting Methods

June 201222


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A Strong Feature: Choosing the AppropriateReconciliation Strategy

June 201223

Bottom-up

Top-down


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Results from One of Our Markets

Context:

We ran the HPF (High-Performance Forecasting)

procedures of SAS Forecast Serveron their defaults.

We used original order history, no cleaning, 3 years of

monthly data.

These are back-tested results, covering a period of 10

months.

We measure performance for 3 months lag forecast.

These results therefore show what can be achieved

with very little effort, and they have a clear potential

for improvements.

June 201224


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Results from One of Our Markets

June 201225

DPA

Demand Plan Accuracy

MFR

The performance of the

existing planning

process, mostlyjudgmental

SAS

The performance of the

SAS engine with very

little changes to the

defaults.


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and only for "Long History" Products

June 201226


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Another Market:Weekly Forecasts

June 201227

70,6%

69,9%

69,6%

69,1%

71,2%

70,7%

70,5%

69,6%

68,0%

68,5%

69,0%

69,5%

70,0%

70,5%

71,0%

71,5%

W-1 W-2 W-3 W-4

Nestl

SAS Forecast Server

Back-test period is 11 weeks

Minimum adjustments to SAS

procedures

The Nestl forecasts are

statistical, but using multiple

regression and not time series

methods

SAS Forecast Studio Ease of Use

Pros Cons

Intuitive navigationNew models more complicated

(training)

Point/click between series, tables,

etc.No way to truncate history in tool

Reasonable initial forecastsEvents difficult to create and

maintain

All-in-one: connect disjointed

processes


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Handling Promotions: Need for CausalMethods

June 201228

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.


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Demand Analysts support Demand Planners

June 201229

Demand Analystprovides statistical

forecast services

and FVA insight,

using best-of-breed

software

Demand Plannerowns plans, focus is on

Mad Bullsand

integration with the

Business

Customer

Historical DataSales and MarketingFinance

Works in Analytical

Competence Center

Fully integrated in the

Nestl Business


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The Competition for Data Scientists hasStarted !

June 201230

Statistical Modeling /

Forecasting (what

statistics can and

cannot achieve), no realneed for Ph.Ds

Business Understanding

Data Management and

Programming

Statistics

ForecastingBusiness

Understanding

Data

Management


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Thank You !

June 201231

06 baumgartner - statistischen prognosemethoden nestle

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