2/20/2018

Statistical Forecasting in Nestlé Middle East

SAS Analytics Day

Istanbul February 2017

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Customer service level & freshness

Inventory holding costs

The supply chain trade-off

Logistics

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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 mainly to reduce the cost per unit.

We need to forecast the future orders of our customers to have the right volumes of the right product, at the right location, at the right moment in time.

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The statistical forecasting pyramid journey

Stage 3:

Advanced

Analytics

Stage 2: Causal Modelling

(multivariate modelling, external factors, what-if scenarios,…)

Stage 1: Foundation Building

(univariate models using sales history only)

for planning for forecasting

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PRELIMINARY WORK

BEFORE STARTING THESAS ANALYTICS JOURNEY

IN YOUR COMPANY

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Key Success Factors

✓ Choose the right people

✓ Kick-off the project with a senior manager sponsor

✓ Define a clear structure and process to integrate SAS in the planning platform

✓ Classify the products and define a strategy for each cluster

✓ Define the best forecasting level and hierarchy to answer business needs

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✓ Statistical Modeling / Forecasting (what statistics can and cannot achieve), no real need for Ph.Ds

✓ Business Understanding

✓ Data Management and Programming

StatisticsForecasting

Business Understanding

Data Management

The demand forecast analyst ideal profile

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DA

BU DP

BU DP

BU DPBU

DP

People structure and organisation

DA

BU DP

BU DP

BU DPBU

DP

Centralized Structure

Demand Analyst (DA) BU Demand Planner (DP)

▪ Setup the system▪ Gathers the data▪ Does the modelling▪ Prepares the business cases

▪ Classifies the products▪ Validates the models

De-centralized Structure

Demand Analyst (DA) BU Demand Planner (DP)

▪ Setups the system▪ Gathers the data▪ Trains and coaches the DPs

▪ Classifies the products▪ Does the modelling▪ Supports in business cases ▪ More integrated in the business

approach

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Fore

cast

Per

form

ance

Volatility of Demand

Volatility is driving forecasting performance

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Animal farm classification

I am high demand, easy to forecastYour most precious animal, take special care of me, I am a reliable friends.

Low demand, easy to forecastthrough statistical models. I am a nice animal, easy to take care, never create problems. Just visitme from time to time.

I am low demand but difficult to forecastsorry! I jump all over the place, no way to calm me. But we are so many, no big deal if you miss one of us ☺ Forecast me statistically, don’t waste precious time and don't worry too much about low DPA.

I am high demand and difficult to forecastwith univariate modelling, sorry !I am of high value and difficult to manage. Keep a close eye on me in your planning cycle! Use stats forecast as a reference only! Investigate possibility of using causal modelling

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One possible forecasting strategy…

Horse Mule Jack Rabbit Mad Bull

Short-term:1-3 months horizon

SAS / Judgemental SAS SAS / Judgemental Judgemental

Medium-term 4-12 months horizon

SAS SAS SAS / Judgemental Judgemental / SAS Causal

Long-term: SAS SAS SAS SAS

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Forecasting Level and Hierarchy

Bottom-up

Top-down

A different forecasting level can generate a very different forecast !!

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INTEGRATIONin the planning cycle

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SAS can forecast 5 years of monthly forecast for thousands of time series in less than a minute

Use it as a quick win to show the value of stats vs. judgmental,

Quick win: long-term forecasting

nobody except you has a clue in your organisation !

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Futu

re

Fore

cast

sP

ast

KP

Is

DPA M-3 Bias M-3

Data and insight: The stats dashboard

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Data and insight: SAS vs. bottom-up expected growths bubble chart

Bubble size is proportionalto the volume

Y=X line

LEGEND :

Alert Product C

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Senior manager sponsor testimony

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CAUSAL FORECASTINGfor Mad Bulls

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Identify causal factors impacting our demand

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Causal customer level planning

Once all data are integrated, we can do if-scenario analysis, playing with the causal factor to see which one impacts more the demand

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THANK YOU FOR YOUR ATTENTION