1. 3-1Forecasting William J. Stevenson Operations Management 8 th edition

2. 3-2Forecasting CHAPTER 3 Forecasting McGraw-Hill/Irwin Operations Management, Eighth Edition, by William J. Stevenson Copyright © 2005 by The McGraw-Hill Companies, Inc. All rights reserved.

3. 3-3Forecasting FORECAST:  A statement about the future value of a variable of interest such as demand.  Forecasts affect decisions and activities throughout an organization  Accounting, finance  Human resources  Marketing  MIS  Operations  Product / service design

4. 3-4Forecasting AccountingCost/profit estimates FinanceCash flow and funding Human ResourcesHiring/recruiting/training MarketingPricing, promotion, strategy MISIT/IS systems, services OperationsSchedules, MRP, workloads Product/service designNew products and services Uses of Forecasts

5. 3-5Forecasting  Assumes causal system past ==> future  Forecasts rarely perfect because of randomness  Forecasts more accurate for groups vs. individuals  Forecast accuracy decreases as time horizon increases I see that you will get an A this semester.

6. 3-6Forecasting Elements of a Good Forecast Timely Accurate Reliable Meaningful Written Easy to use

7. 3-7Forecasting Steps in the Forecasting Process Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Gather and analyze data Step 5 Prepare the forecast Step 6 Monitor the forecast “The forecast”

8. 3-8Forecasting Types of Forecasts  Judgmental - uses subjective inputs  Time series - uses historical data assuming the future will be like the past  Associative models - uses explanatory variables to predict the future

9. 3-9Forecasting Judgmental Forecasts  Executive opinions  Sales force opinions  Consumer surveys  Outside opinion  Delphi method  Opinions of managers and staff  Achieves a consensus forecast

10. 3-10Forecasting Time Series Forecasts  Trend - long-term movement in data  Seasonality - short-term regular variations in data  Cycle – wavelike variations of more than one year’s duration  Irregular variations - caused by unusual circumstances  Random variations - caused by chance

11. 3-11Forecasting Forecast Variations Trend Irregular variatio n Seasonal variations 90 89 88 Figure 3.1 Cycles

12. 3-12Forecasting Naive Forecasts Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.... The forecast for any period equals the previous period’s actual value.

13. 3-13Forecasting  Simple to use  Virtually no cost  Quick and easy to prepare  Data analysis is nonexistent  Easily understandable  Cannot provide high accuracy  Can be a standard for accuracy Naïve Forecasts

14. 3-14Forecasting  Stable time series data  F(t) = A(t-1)  Seasonal variations  F(t) = A(t-n)  Data with trends  F(t) = A(t-1) + (A(t-1) – A(t-2)) Uses for Naïve Forecasts

15. 3-15Forecasting Techniques for Averaging  Moving average  Weighted moving average  Exponential smoothing

16. 3-16Forecasting Moving Averages  Moving average – A technique that averages a number of recent actual values, updated as new values become available.  Weighted moving average – More recent values in a series are given more weight in computing the forecast. MA n = n AiAi i = 1  n

17. 3-17Forecasting Moving Averages  Moving average – MA n = n AiAi i = 1  n

18. 3-18Forecasting Moving Averages  Moving average – MA n = n AiAi i = 1  n

19. 3-19Forecasting Moving Averages  Moving average – MA n = n AiAi i = 1  n

20. 3-20Forecasting Moving Averages  Moving average – MA n = n AiAi i = 1  n

21. 3-21Forecasting Moving Averages  Moving average – MA n = n AiAi i = 1  n

22. 3-22Forecasting Simple Moving Average MA n = n AiAi i = 1  n Actual MA3 MA5

23. 3-23Forecasting Weighted Moving Averages  Weighted Moving average – WMA n = A*w i i = 1  n

24. 3-24Forecasting Weighted Moving Averages  Weighted Moving average – WMA n = A*w i i = 1  n

25. 3-25Forecasting Weighted Moving Averages  Weighted Moving average – WMA n = A*w i i = 1  n

26. 3-26Forecasting Weighted Moving Averages  Weighted Moving average – WMA n = A*w i i = 1  n

27. 3-27Forecasting Weighted Moving Averages  Weighted Moving average – WMA n = A*w i i = 1  n

28. 3-28Forecasting Exponential Smoothing 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 )

29. 3-29Forecasting Exponential Smoothing  Weighted averaging method based on previous forecast plus a percentage of the forecast error  A-F is the error term,  is the % feedback F t = F t-1 +  ( A t-1 - F t-1 )

30. 3-30Forecasting Exponential Smoothing  F t = Next Period  F t-1 = Previous Period   Smoothing Constant  A t-1 = Actual Result Previous Period F t = F t-1 +  ( A t-1 - F t-1 )

31. 3-31Forecasting Exponential Smoothing - Problem  F t = Result of formula  F t-1 = 42   Smoothing =.10  A t-1 = 44 F t = F t-1 +  ( A t-1 - F t-1 )

32. 3-32Forecasting Exponential Smoothing - Problem  F t = 42 +.10(44-42)  F t = 42 +.10(2)  F t = 42 +.20  F t = 42.20 F t = F t-1 +  ( A t-1 - F t-1 )

33. 3-33Forecasting Exponential Smoothing - Problem  F t = 43 +.10(42.20-43)  F t = 43 +.10(-.80)  F t = 43 + -.080  F t = 42.92 F t = F t-1 +  ( A t-1 - F t-1 )

34. 3-34Forecasting Example 3 - Exponential Smoothing

35. 3-35Forecasting Picking a Smoothing Constant .1 .4 Actual

36. 3-36Forecasting Common Nonlinear Trends Parabolic Exponential Growth Figure 3.5

37. 3-37Forecasting Linear Trend Equation  F t = Forecast for period t  t = Specified number of time periods  a = Value of F t at t = 0  b = Slope of the line F t = a + bt 0 1 2 3 4 5 t FtFt

38. 3-38Forecasting Calculating a and b b = n(ty) - ty nt 2 - ( t) 2 a = y - bt n   

39. 3-39Forecasting Linear Trend Equation Example

40. 3-40Forecasting Linear Trend Calculation y = 143.5 + 6.3t a= 812- 6.3(15) 5 = b= 5 (2499)- 15(812) 5(55)- 225 = 12495-12180 275-225 = 6.3 143.5

41. 3-41Forecasting Associative Forecasting  Predictor variables - used to predict values of variable interest  Regression - technique for fitting a line to a set of points  Least squares line - minimizes sum of squared deviations around the line

42. 3-42Forecasting Linear Model Seems Reasonable A straight line is fitted to a set of sample points. Computed relationship

43. 3-43Forecasting Forecast Accuracy  Error - difference between actual value and predicted value  Mean Absolute Deviation (MAD)  Average absolute error  Mean Squared Error (MSE)  Average of squared error  Mean Absolute Percent Error (MAPE)  Average absolute percent error

44. 3-44Forecasting MAD, MSE, and MAPE MAD = Actualforecast   n MSE = Actualforecast ) - 1 2   n ( MAPE = Actualforecas t  n / Actual*100) 

45. 3-45Forecasting Example 10

46. 3-46Forecasting Controlling the Forecast  Control chart  A visual tool for monitoring forecast errors  Used to detect non-randomness in errors  Forecasting errors are in control if  All errors are within the control limits  No patterns, such as trends or cycles, are present

47. 3-47Forecasting Sources of Forecast errors  Model may be inadequate  Irregular variations  Incorrect use of forecasting technique

48. 3-48Forecasting Tracking Signal Tracking signal = ( Actual - forecast ) MAD  Tracking signal –Ratio of cumulative error to MAD Bias – Persistent tendency for forecasts to be Greater or less than actual values.

49. 3-49Forecasting Choosing a Forecasting Technique  No single technique works in every situation  Two most important factors  Cost  Accuracy  Other factors include the availability of:  Historical data  Computers  Time needed to gather and analyze the data  Forecast horizon

50. 3-50Forecasting Exponential Smoothing

51. 3-51Forecasting Linear Trend Equation

52. 3-52Forecasting Simple Linear Regression

53. 3-53Forecasting Workload/Scheduling SSU9 United Airlines example