SAP APO Demand Planning Forecast techniques

1. Selecting the Right Forecasting Method

2. Agenda • Traditional Sales Forecasting Methods • Current Sales Forecasting Methods and Techniques Being Used • Underlying Theory of Forecasting Methods • Sales Forecasting Methodologies • Quantitative vs Qualitative • Tool Kit Approach • Build a Model • Components of Applied Market Response Modeling • Analyze an Actual Model Using Live Data • Introduction to Multi-Tiered Causal Analysis • Composite Forecasting Application 2

3. Traditional Sales Forecasting Methods... • Most companies seem to use simple techniques that are easy to comprehend and mostly those that involve judgment by company employees. • A method widely used results in forecast goal-setting, this is not really forecasting. • Here companies begin their planning process with a corporate goal to increase sales by some percentage. • This target often comes directly from the chief executive officer. • Then everyone backs into their target based on what each business unit manager thinks they can deliver. • Finally, if they don’t meet the target when totaled the CEO either assigns targets to particular business units or puts a financial plug in place hoping someone will over deliver. 3

4. Current Sales Forecasting Methods and Techniques Being Used • More focus on utilizing time series methods to predict baseline sales demand • Primarily using Winter’s Exponential Smoothing • Also, some Decomposition/Census X-11 • Very little ARIMA/Box-Jenkins • Judgmental techniques still seem to be the dominant method of choice • Sales Force Composites • Jury of Executive Opinion • Delphi Approach • Multiple Regression is beginning to be utilized • More Universities are teaching Regression Applications • Accessing causal data is becoming easier • Regression is required to evaluate and predict sales promotions 4

5. Underlying Theory of Forecasting Methods... 5

6. Underlying Theory of Forecasting Methods... Forecast = Pattern + Randomness 6

7. Underlying Theory of Forecasting Methods... Forecast = Pattern + Randomness This simple equation is really saying that when the average pattern of the underlying data has been identified some deviation will occur between the forecasting method applied and the actual occurrence. 7

8. Underlying Theory of Forecasting Methods... Forecast = Pattern + Randomness 8

9. Sales Forecasting Methods... 9

10. Two Types Of Sales Forecasting Methodologies • Qualitative • Also Known as “Judgmental” or Subjective • Quantitative • Also known as “Mathematical” or Objective 10

11. Qualitative Methods • Are also known as Judgmental • Rely on subjective assessments of a person or group of people • Using intuitive or gut feelings based on their experience and savvy • Who understand the current marketplace and what’s likely to occur 11

12. Qualitative MethodsSubjective or judgmental derived forecasts using intuitive or gut feelings • Independent Judgment • Committees • Sales Force Estimates 12

13. Qualitative MethodsSubjective or judgmental derived forecasts using intuitive or gut feelings • Independent Judgment • Committees • Sales Force Estimates • Also known as “Sales Force Composites” 13

14. Qualitative MethodsSubjective or judgmental derived forecasts using intuitive or gut feelings • Independent Judgment • Committees • Sales Force Estimates • Also known as “Sales Force Composites” • Juries of Executive Opinion 14

15. Qualitative MethodsSubjective or judgmental derived forecasts using intuitive or gut feelingsAdvantages • Low cost to develop • Executives usually have a solid understanding of the broad-based factors and how they affect sales demand • Provides input from the firm’s key functional areas • Can provide sales forecasts fairly quickly 15

16. Qualitative MethodsSubjective or judgmental derived forecasts using intuitive or gut feelingsDisadvantages • They are always “biased” toward the user group • They are “not’ consistently accurate over time • Some executives may not really understand the firm’s sales situation since they are too far removed from the actual marketplace • Not well suited for firms with a large number of products 16

17. Quantitative Methods 17

18. Quantitative Methods • One Dimensional or Reactive Methods • Time Series Techniques, using only past sales history alone Time Series F o r e c a s t Shipments 18

19. Quantitative Methods • One Dimensional or Reactive Methods • Time Series Techniques, using only past sales history alone • Multidimensional or Proactive Methods • Causal Techniques, built on a relationship(s) between past sales and some other variable(s) Causal Time Series F o r e c a s t Price F o r e c a s t Shipments Shipments Promo 19

20. Quantitative MethodsObjective mathematically derived forecasts.Times Series Techniques • Naive • Simple Moving Averaging • Exponential Smoothing • Brown’s Double Exponential Smoothing • Holt's Two Parameter Exponential Smoothing • Winter’s Three Parameter Exponential Smoothing • Decomposition • Multiplicative • Additive • Census X-11 • Box-Jenkins 20

21. Time Series Methods(One Dimensional or Reactive Methods)Advantages • They are well suited to situations where sales forecasts are needed for a large number of products • They work very well for products with fairly stable sales • They can smooth out small random fluctuations • They are simple to understand and use • They can be easily systematized and require little data storage • Software packages are usually accessible, and • They are generally good at short-term forecasting 21

22. Time Series Methods(One Dimensional or Reactive Methods)Disadvantages • They require a large amount of historical data • They adjust slowly to changes in sales • A great deal of searching may be needed to find the weighted (Alpha) value • They usually fall apart when the forecast horizon in long, and • Forecasts can be thrown into great error because of large fluctuations in current data 22

23. Quantitative MethodsObjective mathematically derived forecasts.Causal Techniques • Simple Regression • Multiple Regression • Econometric Modeling • Robust Regression 23

24. Things to Remember about Regression... • When and who invented regression? • The term regression was introduced by Francis Galton in 1886. • He called it the “Law of Universal Regression.” • His friend Karl Pearson confirmed the theory by collecting a thousand records of heights for children of tall and short parents. • Sir Henry Moore in 1918 developed the first Econometric Model. 24

25. Why Haven’t Causal Methods been Used? • They are more time-intensive to develop and require a strong understanding of statistics • They require larger data storage and are less easily systematized, and • They tend to be more expensive to build and maintain 25

26. Why Are Causal Methods the wave of the Future? • Enabled by the advent of the PC and Client Server Technology • Available in most software packages • Provide accurate short-, medium-, and long-term forecasts • Are capable of supporting “What-if” analysis 26

27. Causal Methods(Multidimensional or Proactive Methods)Advantages • They are available in most software packages • They are inexpensive to run on computers • These techniques are covered in most statistics courses so they have become increasingly familiar with managers • They provide accurate short-, medium-, and long-term forecasts, and • They are capable of supporting “What-if” analysis 27

28. Causal Methods(Multidimensional or Proactive Methods) Disadvantages • Their forecasting accuracy depends on a consistent relationship between independent and dependent variables • An accurate estimate of the independent variable is crucial • A lack of understanding by many managers who view it as a “black box” technique • They are more time-intensive to develop and require a strong understanding of statistics • They require larger data storage and are less easily systematized, and • They tend to be more expensive to build and maintain 28

29. Tool Kit Approach Selecting the Appropriate Method Based On Portfolio Management 29

30. Data Product Portfolio Stable Incomplete Complete Unstable 30

31. Data Product Portfolio Stable Incomplete Complete • Simple Moving • Average • Committees • Independent • Judgment • Sales Force • Composites Unstable 32

32. Data Product Portfolio Stable • Census X-11 • Box-Jenkins • Winters Incomplete Complete • Simple Moving • Average • Committees • Independent • Judgment • Sales Force • Composites Unstable 33

33. Data Product Portfolio Stable • Census X-11 • Box-Jenkins • Winters • Multiple Regression • Simple Regression Incomplete Complete • Simple Moving • Average • Committees • Independent • Judgment • Sales Force • Composites Unstable 34

34. Data Product Portfolio Stable • Census X-11 • Box-Jenkins • Winters • Multiple Regression • Simple Regression Incomplete Complete • Simple Moving • Average • Robust • Regression • Committees • Independent • Judgment • Sales Force • Composites Unstable 35

35. Data Product Portfolio Demand Pull Stable • Census X-11 • Box-Jenkins • Winters • Multiple Regression • Simple Regression Incomplete Complete Business Strategy • Simple Moving • Average • Robust • Regression • Committees • Independent • Judgment • Sales Force • Composites Factory Push Unstable 36

36. Data Product Portfolio Stable • Census X-11 • Box-Jenkins • Winters • Multiple Regression • Simple Regression Incomplete Complete • Simple Moving • Average • Robust • Regression • Committees 10% • Independent • Judgment • Sales Force • Composites Unstable 37

37. Data Product Portfolio Stable • Census X-11 • Box-Jenkins 50% • Winters • Multiple Regression • Simple Regression Incomplete Complete • Simple Moving • Average • Robust • Regression • Committees • Independent • Judgment • Sales Force • Composites Unstable 38

38. Data Product Portfolio Stable • Census X-11 • Box-Jenkins • Winters • Multiple Regression 35% • Simple Regression Incomplete Complete • Simple Moving • Average • Robust • Regression • Committees • Independent • Judgment • Sales Force • Composites Unstable 39

39. Data Product Portfolio Stable • Census X-11 • Box-Jenkins • Winters • Multiple Regression • Simple Regression Incomplete Complete • Simple Moving • Average • Robust • Regression 5% • Committees • Independent • Judgment • Sales Force • Composites Unstable 40

40. Data Product Portfolio Stable • Census X-11 • Box-Jenkins 50% 35% • Winters • Multiple Regression • Simple Regression Incomplete Complete • Simple Moving • Average • Robust • Regression 5% • Committees 10% • Independent • Judgment • Sales Force • Composites Unstable 41

41. Benefits of the Forecast Tool Kit Approach • Better understand what method(s) to apply to each product group in your product portfolio • Determine where additional data is required • How to staff your forecasting resources • Justifies the requirements for a system support tool that encompasses the complete tool kit of forecasting methods 42

42. Building A Model... Yi = B0 + B1X1...BnXn + ei 43

43. Components ofApplied Market Response Modeling • Specification: The model building activity. • Involves the client (i.e., Product Management) • Estimation: Fitting the model to the data. • Includes collecting the data. • Verification: Testing the model. • Prediction: Forecasting Four Phases 44

44. Three Major By-Productsof Market Response Models • Structural Analysis • Estimation of the impact of such things as price and advertising on demand as measured by elasticity's. • Policy Evaluation • The impact of policies that may affect consumer demand, such as pricing changes. • Forecasting • Forecasting demand of particular items in either the short-range or long-range. 45

45. Model BuildingProcess • First, we will identify and assess the factors that make up the marketing mix (consumption) for a particular product. This is known as “Structural Analysis.” • Next, via simulation, begin to determine possible alternative policies. • Then, we will produce sales forecasts for consumer demand. • Finally, tie the outcome to factory shipments via a second model to forecast customer demand (shipments). • This process of linking causal models together is known as “Multi-Tiered Causal Analysis” 46

46. In other Words... Economics 101 Retail Market (Demand) 47

47. In other Words... Economics 101 Retail Market (Demand) Factory Shipments (Supply) 48

48. In other Words... Economics 101 Retail Market (Demand) Factory Shipments (Supply) 49

49. In other Words... Economics 101 Retail Market (Demand) Factory Shipments (Supply) 50

50. In other Words... Economics 101 Retail Market (Demand) Factory Shipments (Supply) 51