some personal perspectives on demand forecasting – past...

Some Personal Perspectives on Demand Forecasting –

Past, Present, Future

Hans Levenbach, PhDDelphus, Inc.

INFORMS Luncheon

Penn Club, NYC

October 18, 2007

INFORMS, NYCOctober 18, 2007 www. Delphus.com

Presentation Overview

� Introduction

� Demand Analysis and Forecasting

– A historical perspective

� Useful Forecasting Techniques –

– A personal viewpoint

� Closing the Loop - A Structured

Forecasting Process


Demand Analysis and Forecasting –From a safe microcosm to a chaotic ‘real world’

� An emerging crisis in telecomThe New York City exodus to the suburbs

� When senior management caresThings begin to happen

� But, . . the real world has become more chaotic

� Traditionally, sell what you make � push

� Now, make what you can sell � pull


Supply Chain Partners - Cooperation


Useful Statistical and OR Techniques

� Time Series Decomposition

� Model Optimization

� Database Design

� Graphical Techniques for Time Series Modeling

� Forecaster Training (or lack thereof)


Minard’s Classic:

Napoleon’s March To Moscow


OPEC Oil Prices – Poor Graphic Presentations

– After 18 Months of Stability, Prices Are Due To Rise Again


Historical Data– Analyses & Projections

Monthly Housing Starts over a nine-

year period

0

50

100

150

200

Months

From

Time Series Decomposition

To

� Census X-variants and SABL

� M-competitions

� “Many” variable models


Seasonality:Classical and ‘Data-analytic’ displays

Monthly time plots by year for housing

starts

0

200000

400000

600000

1 2 3 4 5 6 7 8 9 10 11 12

Month

Sta

rts

121110987654321

200

150

100

C3

Ho

us

Sta

rt


Basic Features Supported in a Forecasting System

� Supplementary data (prices, promos, . . . )

� Automatic projection techniques

� Error measures

� Unit conversions

� Judgmental tools (override adjustments)


Prepare recurring input data

Demand Forecasting

PRODUCTPLACE

PERIOD


Units cost sales value gross margin

weeks months quarters annual

UnitsUnits costcost sales valuesales value gross margingross margin

weeksweeks monthsmonths quartersquarters annualannual

Feature

Product Type

Prod Line

Prod_Class

Wee

kly

Mon

thly

Qua

rter

ly

Annu

al

DC

/ Warehouse

Accoun

tC

hannelR

egionD

ivision

Brand

POS warehouse orders shipments

Measures

Dimensions

Source of Data

Dai

ly

Defining The Forecasting Data Structure


Overuse of Demand Hierarchies


Execute Models

� Exponential Smoothing

� BJ ARIMA /Transfer

� State Space

� Neural Nets

Blended with judgmental approaches


Evaluate -

Performance and Accuracy Measurement


Reconcile The ‘Final Forecast’


Field Sales/Customer Collaboration

Demand Forecast Inputs


Streamlining the Forecasting Cycle –The PEER Process

� Recurring and supplementary data (prices, promos, discounts)

� Complexities of automated forecasting techniques

� Performance measurement

� Management tools (override adjustments, conversions, intros, mix)


PEER PlanningA Customer-centric Demand Forecasting and

Replenishment Planning Process

Execute

Prepare

Evaluate

Reconcile


Implementation


Virtual ForecastingLog Into A Personalized Dashboard

Access Virtual Forecaster tools through your Web browser

Access your personalized dashboard by entering user name and password


Access your latest personalized industry and general news

Create your own personal Internet links

You have access to the sales forecasting and optimization tools as well as help for support and interpretation


PPreparerepare Forecast

Multiple display options

View the changed forecast, the original statistical forecast or history only

View the forecast in units, $ revenue, $ purchase cost or the gross margin of the product

You or others can make notes. These notes are stored with the forecast and are retrieved when the associated forecast is accessed


EExecutexecute The Sales Forecast

Change the forecast by moving the graph lines up or down. These “eyeball” changes cause the underlying numbers to change

Change the forecast, on the spreadsheet, by adding, subtracting, replacing numbers or by changing the forecast by a percentage change, up or down

An option is to provide each field sales collaborator with a sales forecast. Each location can only change their forecast. You, as the forecaster can view and change the individual and aggregated forecasts


EEvaluatevaluate The Sales Forecast

Select multiple error metrics

View the changed forecast, the original statistical forecast or field sales performance


RReconcileeconcile The Sales Forecast

Manage products at the higher (Brand) level but see what impact it is having on the individual SKU’s

that are part of the higher level.

Allow for:· New product launches· Override items based on percentage mixes· Change mix percents and reallocate the forecast


Where Do We Go From Here?

some personal perspectives on demand forecasting – past...

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