Chapter 15

1. Chapter 15 Demand Management and Forecasting

2. Learning Objectives • Understand the role of forecasting as a basis for supply chain planning. • Compare the differences between independent and dependent demand. • Identify the basic components of independent demand: average, trend, seasonal, and random variation. • Describe the common qualitative forecasting techniques such as the Delphi method and Collaborative Forecasting. • Show how to make a time series forecast using regression, moving averages, and exponential smoothing. • Use decomposition to forecast when trend and seasonality is present.

3. Characteristics of Forecasts • Guessing at the future: educated guessing game • Seldom correct • No perfect forecast • Objective is to minimize forecast errors • It is only a tool used to set: • Production plan and budgets • Work schedules • Forecasts are more accurate in aggregation • Long-term forecasts are less accurate than short-term forecasts • Forecasts are means to an end

4. Demand Management • Strategic forecasts: forecasts used to help set the strategy of how demand will be met • Tactical forecasts: forecasted needed for how a firm operates processes on a day-to-day basis • The purpose of demand management is to coordinate and control all sources of demand • Two basic sources of demand • Dependent demand: the demand for a product or service caused by the demand for other products or services • Independent demand: the demand for a product or service that cannot be derived directly from that of other products LO 2

5. Demand Management Continued • Not much a firm can do about dependent demand • It is demand that must be met • There is a lot a firm can do about independent demand • Take an active role to influence demand • Offer incentive to customers • Wage campaigns to sell products • Take a passive role and respond to demand • Especially if at full capacity • High cost of advertisement LO 1

6. Types of Forecasts • Basic types of forecasts • Quantitative—use historical data • Time series analysis • Causal relationships • Simulation • Qualitative—based on subjective estimates/opinion • Time series analysis is based on the idea that data relating to past demand can be used to predict future demand • Primary focus of this chapter LO 1

7. Components of Demand • Average demand for a period of time • Trend • Seasonal element • Cyclical elements • Random variation • Autocorrelation LO 3

8. Common Types of Trends LO 3

9. Time Series Analysis • Short term: forecast under three months • Tactical decisions • Medium term: three months to two years • Capturing seasonal effects • Long term: forecast longer than two years • Detecting general trends • Identifying major turning points LO 5

10. A Guide to Selecting an Appropriate Forecasting Method LO 5

11. Pick Forecasting Model Based On • Time horizon to forecast • Data availability • Accuracy required • Size of forecasting budget • Availability of qualified personnel LO 5

12. Linear Regression Analysis • Regression: functional relationship between two or more correlated variables • It is used to predict one variable given the other • Y = a + bX • Y is the value of the dependent variable • a is the Y intercept • b is the slope • X is the independent variable • Assumes data falls in a straight line LO 5

13. Example 15.1: The Data and Least Squares Regression Line LO 5

14. Example 15.1: Equationsand Calculating Totals LO 5

15. Example 15.1:Calculating the Forecast LO 5

16. Calculating the Forecast Y = 143.5 + 6.3x What is forecast for x=100? Y = 143.5 + 6.3(100) = 774

17. Decompositionof a Time Series • Time series: chronologically ordered data that may contain one or more components of demand • Decomposition: identifying and separating the time series data into these components • Seasonal variation • Additive: the seasonal amount is constant • Multiplicative: the seasonal variation is a percentage of demand LO 6

18. Additive and Multiplicative Seasonal Variation Superimposed on Changing Trend LO 6

19. Example 15.3:The Data and Hand Fitting LO 6

20. Example 15.3: Computing Seasonal Factors and Computing Forecast LO 5

21. Decomposition Using Least Squares Regression • Determine the seasonal factor • Deseasonalize the original data • Develop a least squares regression line for the deseasonalized data • Project the regression line through the period of the forecast • Create the final forecast by adjusting the regression line by the seasonal factor LO 6

22. Steps 1-3Deseasonalized Demand LO 6

23. Steps 4 – 5 LO 6

24. Simple Moving Average • Useful when demand is neither growing nor declining rapidly and does not have seasonal characteristics • Moving averages can be centered or used to predict the following period • Important to select the best period • Longer gives more smoothing/less sensitive • Shorter reacts quicker to trends LO 5

25. Simple Moving Average Formula LO 5

26. Forecast Demand Based on a Three- and a Nine-Week Simple Moving Average LO 5

27. Forecast Demand Based on a Three- and a Nine-Week Simple Moving Average

28. Weighted Moving Average • The moving average formula implies an equal weight being placed on each value that is being averaged • The weighted moving average permits an unequal weighting on prior time periods • All the weights must sum to one if fractions • Otherwise, weights can be real numbers. If so divide by sum of weights: • Ft = LO 5

29. WMA Example Question: Given the weekly demand information and weights of 0.6, 0.1, and 0.3, what is the weighted moving average forecast for the 5th period or week? F5 = (0.6)(655)+(0.1)(680)+(0.3)(755)= 688

30. Choosing Weights • Experience and trial-and-error are the simplest ways • Generally, the most recent past is the best indicator • When data are seasonal, weights should be established accordingly LO 5

31. Exponential Smoothing • Most used of all forecasting techniques • Integral part of all computerized forecasting programs • Widely used in retail and service • Widely accepted because… • Exponential models are surprisingly accurate • Formulating an exponential model is relatively easy • The user can understand how the model works • Little computation is required to use the model • Computer storage requirements are small • Tests for accuracy are easy to compute LO 5

32. Exponential Smoothing Model Ft = Ft-1 + (At-1 - Ft-1) Where: = Forecast value for the coming t time period = Forecast value in 1 past time period = Actual occurrence in the past 1 time period = Alpha smoothing constant • Premise: The most recent observations might have the highest predictive value • Therefore, we should give more weight to the more recent time periods when forecasting LO 5

33. Exponential Smoothing Example (=0.20) LO 5

34. ES Example (=0.10, 0.60)

35. ES Example (=0.10, 0.60) Note how the smaller alpha results in a smoother line in this example

36. Trend Effects in Exponential Smoothing • An trend in data causes the exponential forecast to always lag the actual data • Can be corrected somewhat by adding in a trend adjustment • To correct the trend, we need two smoothing constants • Smoothing constant alpha () • Trend smoothing constant delta (δ) LO 5

37. Exponential Forecasts versusActual Demand over Time Showing the Forecast Lag LO 5

38. Trend Effects Equations LO 5

39. Forecast Error • Sources of errors • Projecting the past into the future • Wrong relationships • Wrong information (data) • Errors outside of our control • Goal is to minimize the errors

40. Forecast Error • Bias errors: when a consistent mistake is made • Random errors: errors that cannot be explained by the forecast model being used • Measures of error • Mean absolute deviation (MAD) • Mean absolute percent error (MAPE) • Tracking signal LO 5

41. The MAD Statistic to Determine Forecasting Error • The ideal MAD is zero which would mean there is no forecasting error • The larger the MAD, the less the accurate the resulting model LO 5

42. Example: Find the MAD — — 5 5 20 10 Total = 40 Note that by itself, the MAD only lets us know the mean error in a set of forecasts

43. Tracking Signal • The tracking signal (TS) is a measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand • Depending on the number of MAD’s selected, the TS can be used like a quality control chart indicating when the model is generating too much error in its forecasts LO 5

44. Computing the MAD, the RSFE, and the TS from Forecast and Actual Data LO 5

45. Example: Tracking Signal - 1 1 250 200 - 50 50 - 50 50 50.0 - 2 2 325 250 125 62.5 - 75 75 - 125 - 75 75 - 200 - 3 3 400 325 200 66.7 - 4 4 350 300 - 50 50 - 250 250 62.5 - 50 50 - 300 5 375 325 300 60.0 - 5 6 - 50 50 - 350 350 58.3 450 - 6 400 • + 4 • 3 • 2 • 1 • 0 • - 1 • - 2 • - 3 • 4 • 5 • 6 TS Out of Control 1 2 3 4 5 6 Period

46. Causal Relationship Forecasting • Causal relationship forecasting: using independent variables other than time to predict future demand • The independent variable must be a leading indicator • Must find those occurrences that are really the causes LO 5

47. Qualitative Techniques in Forecasting • Qualitative forecasting techniques take advantage of the knowledge of experts • Most useful when the product is new or there is little experience with selling into a new region • The following are samples of qualitative forecasting techniques • Executive judgment • Grass roots • Market research • Panel consensus • Historical analogy • Delphi method LO 4

48. Qualitative Methods Executive Judgment Grass Roots • Used for new products introduction • Decisions are broader and at a higher level • Builds forecast by adding successively from bottom • Those closest to customer know better Market Research Historical analogy Qualitative Methods • Consumer surveys and interviews • Used to improve existing products • Existing product used as model for another • Example: buying CDs on Internet put you on mailing list for related products Panel Consensus Delphi Method • Open meetings with free exchange of ideas • Power play possibilities • Based on expert opinion • Experts asked question anonymously • Goes thru several rounds of questioning • Results tabulated, iterated until a consensus is reached

49. Web-Based Forecasting: (CPFR) • Collaborative planning, forecasting, and replenishment (CPFR): a Web-based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners • Used to integrate the multi-tier or n-Tier supply chain • Objective is to exchange selected internal information to provide for a reliable, longer term future views of demand • CPFR uses a cyclic and iterative approach to derive consensus forecasts LO 5

50. Web-Based Forecasting: Steps in CPFR • Creation of a front-end partnership agreement • Joint business planning • Development of demand forecasts • Sharing forecasts • Inventory replenishment LO 5