Some Personal Perspectives on Demand Forecasting Past, Present, Future

Transcription

1 Some Personal Perspectives on Demand Forecasting Past, Present, Future Hans Levenbach, PhD Delphus, Inc. INFORMS Luncheon Penn Club, NYC

2 Presentation Overview Introduction Demand Analysis and Forecasting A historical perspective Useful Forecasting Techniques A personal viewpoint Closing the Loop - A Structured Forecasting Process

3 Demand Analysis and Forecasting From a safe microcosm to a chaotic real world An emerging crisis in telecom The New York City exodus to the suburbs When senior management cares Things begin to happen But,.. the real world has become more chaotic Traditionally, sell what you make push Now, make what you can sell pull

4 Supply Chain Partners - Cooperation

5 Useful Statistical and OR Techniques Time Series Decomposition Model Optimization Database Design Graphical Techniques for Time Series Modeling Forecaster Training (or lack thereof)

6 Minard s Classic: Napoleon s March To Moscow

7 OPEC Oil Prices Poor Graphic Presentations After 18 Months of Stability, Prices Are Due To Rise Again

8 Historical Data Analyses & Projections Monthly Housing Starts over a nineyear period From Time Series Decomposition To Months Census X-variants and SABL M-competitions Many variable models

9 Seasonality: Classical and Data-analytic displays Monthly time plots by year for housing starts 200 S t a r t s H o us S ta rt Month C

10 Basic Features Supported in a Forecasting System Supplementary data (prices, promos,... ) Automatic projection techniques Error measures Unit conversions Judgmental tools (override adjustments)

11 Prepare recurring input data PERIOD PLACE Demand Forecasting PRODUCT

12 Defining The Forecasting Data Structure Units cost sales value gross margin weeks months quarters annual Measures Dimensions Region Channel Account DC / Warehouse Division Brand Prod_Class Prod Line Product Type Feature Annual Quarterly Monthly Weekly Daily Source of Data POS warehouse orders shipments

13 Overuse of Demand Hierarchies

14 Execute Models Exponential Smoothing BJ ARIMA /Transfer State Space Neural Nets Blended with judgmental approaches

15 Evaluate - Performance and Accuracy Measurement

16 Reconcile The Final Forecast

17 Field Sales/Customer Collaboration Demand Forecast Inputs

18 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)

19 PEER Planning A Customer-centric Demand Forecasting and Replenishment Planning Process Prepare Reconcile Execute Evaluate

20 Implementation

21 Virtual Forecasting Log Into A Personalized Dashboard Access Virtual Forecaster tools through your Web browser Access your personalized dashboard by entering user name and password

22 You have access to the sales forecasting and optimization tools as well as help for support and interpretation Access your latest personalized industry and general news Create your own personal Internet links

23 Prepare Forecast Multiple display options You or others can make notes. These notes are stored with the forecast and are retrieved when the associated forecast is accessed 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

24 Execute 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

25 Evaluate The Sales Forecast Select multiple error metrics View the changed forecast, the original statistical forecast or field sales performance

26 Reconcile 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

27 Where Do We Go From Here?