06 Baumgartner - Statistischen Prognosemethoden Nestle
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Transcript of 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
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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.
June 201217
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
June 201219
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