1. McGraw-Hill/Irwin © 2011 The McGraw-Hill Companies, All Rights Reserved Chapter 15 Demand Management and Forecasting

2. 15-2 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. 15-3   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 Characteristics of Forecasts

4. 15-4 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. 15-5 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. 15-6 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. 15-7 Components of Demand  Average demand for a period of time  Trend  Seasonal element  Cyclical elements  Random variation  Autocorrelation LO 3

8. 15-8 Common Types of Trends LO 3

9. 15-9 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. 15-10 A Guide to Selecting an Appropriate Forecasting Method LO 5

11. 15-11 Pick Forecasting Model Based On  Time horizon to forecast  Data availability  Accuracy required  Size of forecasting budget  Availability of qualified personnel LO 5

12. 15-12 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. 15-13 Example 15.1: The Data and Least Squares Regression Line LO 5

14. 15-14 Example 15.1: Equations and Calculating Totals LO 5

15. 15-15 Example 15.1: Calculating the Forecast LO 5

16. 15-16 Y = 143.5 + 6.3x What is forecast for x=100? Calculating the Forecast Y = 143.5 + 6.3(100) = 774

17. 15-17 Decomposition of 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. 15-18 Additive and Multiplicative Seasonal Variation Superimposed on Changing Trend LO 6

19. 15-19 Example 15.3: The Data and Hand Fitting LO 6

20. 15-20 Example 15.3: Computing Seasonal Factors and Computing Forecast LO 5

21. 15-21 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. 15-22 Steps 1-3 Deseasonalized Demand LO 6

23. 15-23 Steps 4 – 5 LO 6

24. 15-24 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. 15-25 Simple Moving Average Formula LO 5

26. 15-26 Forecast Demand Based on a Three- and a Nine-Week Simple Moving Average LO 5

27. 15-27 Forecast Demand Based on a Three- and a Nine-Week Simple Moving Average

28. 15-28 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: LO 5   F t =

29. 15-29 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 5 th period or week? F 5 = (0.6)(655)+(0.1)(680)+(0.3)(755)= 688

30. 15-30 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. 15-31 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. 15-32 Exponential Smoothing Model LO 5   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 F t = F t-1 + (A t-1 - F t-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

33. 15-33 Exponential Smoothing Example (  =0.20) LO 5

34. 15-34 ES Example (  =0.10, 0.60)

35. 15-35 Note how the smaller alpha results in a smoother line in this example ES Example (  =0.10, 0.60)

36. 15-36 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. 15-37 Exponential Forecasts versus Actual Demand over Time Showing the Forecast Lag LO 5

38. 15-38 Trend Effects Equations LO 5

39. 15-39   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 Forecast Error

40. 15-40 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. 15-41 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. 15-42 MonthSalesForecastAbs Error 1220 2250 255 3210 205 4300 320 5325 315 5 20 10 5 —— 40 Note that by itself, the MAD only lets us know the mean error in a set of forecasts Total = Example: Find the MAD

43. 15-43 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. 15-44 Computing the MAD, the RSFE, and the TS from Forecast and Actual Data LO 5

45. 15-45 PeriodForecastDemandError |E|RSFE Sum |E|MADTS +43210-1-2-3--4--5--6+43210-1-2-3--4--5--6 123456Period 1250200- 5050- 505050.0 - 1 2325250 - 7575- 125 12562.5 - 2 3400325 - 7575- 200 20066.7 - 3 4350300- 5050- 25025062.5 - 4 5375325 - 5050- 300 30060.0- 5 6 450 400 - 5050- 35035058.3 - 6 Out of Control Example: Tracking Signal

46. 15-46 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. 15-47 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. 15-48 Qualitative Methods Grass Roots Market Research Panel Consensus Executive Judgment Historical analogy Delphi Method Qualitative Methods Existing product used as model for another Example: buying CDs on Internet put you on mailing list for related products Based on expert opinion Experts asked question anonymously Goes thru several rounds of questioning Results tabulated, iterated until a consensus is reached 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 Consumer surveys and interviews Used to improve existing products Open meetings with free exchange of ideas Power play possibilities

49. 15-49 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. 15-50 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