2. Chapter 4 Forecasting

3. Production Planning

4. Overview  What is forecasting?  Types of forecasts  7 steps of forecasting  Qualitative forecasting

5. Overview  Quantitative forecasting  Time-series forecasting  Naïve  Moving average  Exponential smoothing  Seasonal variations  Associative methods  Monitoring and Controlling Forecasts

6. What is forecasting? Sales will be $200 Million! Could be a prediction based on historical data and mathematical models Could be a prediction based on expertise and intuition Forecasting - Is the art and science of predicting the future Could be a prediction based on both a model and a manager’s expertise

7. 7 Steps to a Forecast  Determine the use of the forecast  Select the items to be forecast  Determine the time horizon of the forecast  Select the forecasting model(s)  Gather the data  Make the forecast  Validate and implement results

8. Realities of Forecasting  Forecasts never perfect and seldom correct.  Most forecasting methods assume that there is some underlying stability in the system  Both product family and aggregated product forecasts are more accurate than individual product forecasts

9. Demand Forecasts  OM manager is primarily interested in demand forecasts (as opposed to economic forecasts and technological forecasts)  Underlying basis of all business decisions  Production  Inventory  Personnel  Facilities

10. Demand Forecast Applications Time Horizon Medium TermLong Term Short Term (3 months–(more than Application(0–3 months) 3 years) 3 years) Forecast quantity Decision area Forecasting technique

11. Demand Forecast Applications Time Horizon Medium TermLong Term Short Term (3 months–(more than Application(0–3 months) 2 years) 2 years) Forecast quantityIndividual products or services Decision areaInventory management Final assembly scheduling Workforce scheduling Master production scheduling ForecastingTime series techniqueAssociative

12. Demand Forecast Applications Time Horizon Medium TermLong Term Short Term (3 months–(more than Application(0–3 months) 2 years) 2 years) Forecast quantityIndividualTotal sales products orGroups or families servicesof products or services Decision areaInventoryStaff planning managementProduction Final assemblyplanning schedulingMaster production Workforcescheduling schedulingPurchasing Master productionDistribution scheduling ForecastingTime seriesAssociative techniqueAssociative

13. Demand Forecast Applications Time Horizon Medium TermLong Term Short Term (3 months–(more than Application(0–3 months) 2 years) 2 years) Forecast quantityIndividualTotal salesTotal sales products orGroups or families servicesof products or services Decision areaInventoryStaff planningFacility location managementProductionCapacity Final assemblyplanningplanning schedulingMaster productionProcess Workforceschedulingmanagement schedulingPurchasing Master productionDistribution scheduling ForecastingTime seriesAssociativeAssociative techniqueAssociative

14. Overview of Qualitative Methods READ in TEXT (p. 81-82)  Jury of executive opinion  Pool opinions of high-level executives, sometimes augment by statistical models  Sales force composite  Estimates from individual salespersons are reviewed for reasonableness, then aggregated  Delphi method  Panel of experts, queried iteratively  Consumer Market Survey  Ask the customer

15. Patterns of Demand

16. Quantity Time

17. Patterns of Demand Quantity Time (a) Random: Data cluster about a horizontal line.

18. Patterns of Demand Quantity Time (b) Trend: Data consistently increase or decrease over a period of time.

19. Patterns of Demand Quantity |||||||||||| JFMAMJJASOND Months (c) Seasonal: Data consistently show peaks and valleys at the same time each year. Year 1

20. Patterns of Demand Quantity |||||||||||| JFMAMJJASOND Months Year 1 Year 2 (c) Seasonal: Data consistently show peaks and valleys at the same time each year.

21. Patterns of Demand Quantity |||||| 123456 Years (c) Cyclical: Data reveal gradual increases and decreases over extended periods.

22. Overview of Quantitative Methods  Naïve approach  Moving averages  Exponential smoothing  Linear regression Time-series Models – no trend, seasonal, or cyclical fluctuations Associative models

23.  Set of evenly spaced numerical data  Obtained by observing response variable at regular time periods  Forecast based only on past values  Assumes that factors influencing past and present will continue influence in future  Example Year:19931994199519961997 Sales:78.763.589.793.292.1 What is a Time Series?

24. Naïve Approach  Assumes demand in next period is the same as demand in most recent period  e.g., If May sales were 48, then June sales will be 48  Sometimes cost effective & efficient

25. Moving Average Approach  MA is a series of arithmetic means  Used if little or no trend  Used often for smoothing  Provides overall impression of data over time MA n n  Demand in Previous Periods Periods

26. Time-Series Methods Simple Moving Averages  Patient arrivals have been recorded at a medical clinic over the past 28 weeks.  Want to predict the number of patient arrivals for the 29 th week.

27. Time-Series Methods Simple Moving Averages Week 450 — 430 — 410 — 390 — 370 — Patient arrivals |||||| 051015202530 Patient arrivals

28. Time-Series Methods Simple Moving Averages Week 450 — 430 — 410 — 390 — 370 — |||||| 051015202530 Actual patient arrivals Patient arrivals

29. Time-Series Methods Simple Moving Averages Week 450 — 430 — 410 — 390 — 370 — |||||| 051015202530 Actual patient arrivals Patient arrivals -No trend -No seasonal variation -No cycle

30. Time-Series Methods Simple Moving Averages Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week |||||| 051015202530 Patient arrivals

31. Time-Series Methods Simple Moving Averages Actual patient arrivals Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week |||||| 051015202530 Patient WeekArrivals 1400 2380 3411 Patient arrivals

32. Time-Series Methods Simple Moving Averages Actual patient arrivals Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week |||||| 051015202530 Patient WeekArrivals 1400 2380 3411 Patient arrivals

33. Time-Series Methods Simple Moving Averages Actual patient arrivals Week 450 — 430 — 410 — 390 — 370 — |||||| 051015202530 Patient WeekArrivals 1400 2380 3411 F 4 = 411 + 380 + 400 3 Patient arrivals F 4 = 397.0

34. Time-Series Methods Simple Moving Averages Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week |||||| 051015202530 Patient WeekArrivals 1400 2380 3411 F 4 = 397.0 Patient arrivals

35. Time-Series Methods Simple Moving Averages Actual patient arrivals Week 450 — 430 — 410 — 390 — 370 — |||||| 051015202530 Patient WeekArrivals 2380 3411 4415 F 5 = 415 + 411 + 380 3 Patient arrivals

36. Time-Series Methods Simple Moving Averages Actual patient arrivals 450 — 430 — 410 — 390 — 370 — Week |||||| 051015202530 Patient WeekArrivals 2380 3411 4415 F 5 = 402.0 Patient arrivals

37. Time-Series Methods Simple Moving Averages 450 — 430 — 410 — 390 — 370 — Week |||||| 051015202530 Actual patient arrivals Patient arrivals Go To Excel

38. Time-Series Methods Simple Moving Averages 450 — 430 — 410 — 390 — 370 — Week |||||| 051015202530 Actual patient arrivals 3-week MA forecast Patient arrivals

39. Time-Series Methods Simple Moving Averages Week 450 — 430 — 410 — 390 — 370 — |||||| 051015202530 Actual patient arrivals 3-week MA forecast 6-week MA forecast Patient arrivals

40. WEIGHTED Moving Averages  SKIP

41.  Increasing n makes forecast less sensitive to changes  Do not forecast trend well  Require much historical data © 1984-1994 T/Maker Co. Disadvantages of Moving Average Methods

42.  Form of weighted moving average  Weights decline exponentially  Most recent data weighted most  Requires smoothing constant (  )  Ranges from 0 to 1  Subjectively chosen  Involves little record keeping of past data Exponential Smoothing Method

43. Time-Series Methods Exponential Smoothing 450 — 430 — 410 — 390 — 370 — Week |||||| 051015202530 Exponential Smoothing  = 0.10 F t +1 = F t +  (D t – F t ) Patient arrivals

44. Time-Series Methods Exponential Smoothing 450 — 430 — 410 — 390 — 370 — Week |||||| 051015202530 Exponential Smoothing  = 0.10 F 3 = 390 (Given) D 3 = 411 F t +1 = F t +  (D t – F t ) Patient arrivals F 4 = 390 + 0.10(411-390)

45. Time-Series Methods Exponential Smoothing 450 — 430 — 410 — 390 — 370 — Week |||||| 051015202530 F 4 = 392.1 Exponential Smoothing  = 0.10 F 3 = 390 (Given) D 3 = 411 F t +1 = F t +  (D t – F t ) Patient arrivals

46. Time-Series Methods Exponential Smoothing Week 450 — 430 — 410 — 390 — 370 — |||||| 051015202530 F 4 = 392.1 D 4 = 415 Exponential Smoothing  = 0.10 F 4 = 392.1 F 5 = 394.4 F t +1 = F t +  (D t – F t ) Patient arrivals

47. Time-Series Methods Exponential Smoothing Week 450 — 430 — 410 — 390 — 370 — |||||| 051015202530 Patient arrivals

48. Time-Series Methods Exponential Smoothing 450 — 430 — 410 — 390 — 370 — Patient arrivals Week |||||| 051015202530 Exponential smoothing  = 0.10 Go To Excel

49. Time-Series Methods Exponential Smoothing 450 — 430 — 410 — 390 — 370 — Patient arrivals Week |||||| 051015202530 3-week MA forecast Exponential smoothing  = 0.10

50.  Exponential smoothing with trend adjustment  SKIP  Trend projection (p. 93-96)  Regression analysis

51. Time-Series Methods Seasonal Influences

52. QuarterYear 1Year 2Year 3Year 4 14570100100 2335370585725 35205908301160 4100170285215 Total1000120018002200 Average250300450550 Time-Series Methods Seasonal Influences

53. QuarterYear 1Year 2Year 3Year 4 14570100100 2335370585725 35205908301160 4100170285215 Total1000120018002200 Average250300450550 Seasonal Index = Actual Demand Average Demand Time-Series Methods Seasonal Influences Projected Annual Demand = 2600 Average Quarterly Demand = 2600/4 = 650

54. QuarterYear 1Year 2Year 3Year 4 14570100100 2335370585725 35205908301160 4100170285215 Total1000120018002200 Average250300450550 Seasonal Index = Actual Demand Average Demand Time-Series Methods Seasonal Influences

55. QuarterYear 1Year 2Year 3Year 4 14570100100 2335370585725 35205908301160 4100170285215 Total1000120018002200 Average250300450550 Seasonal Index = = 0.18 45 250 Time-Series Methods Seasonal Influences

56. Quarter Year 1 Year 2 Year 3 Year 4 145/250 = 0.1870/300 = 0.23100/450 = 0.22100/550 = 0.18 2335/250 = 1.34370/300 = 1.23585/450 = 1.30725/550 = 1.32 3520/250 = 2.08590/300 = 1.97830/450 = 1.841160/550 = 2.11 4100/250 = 0.40170/300 = 0.57285/450 = 0.63215/550 = 0.39 Time-Series Methods Seasonal Influences

57. Quarter Year 1 Year 2 Year 3 Year 4 145/250 = 0.1870/300 = 0.23100/450 = 0.22100/550 = 0.18 2335/250 = 1.34370/300 = 1.23585/450 = 1.30725/550 = 1.32 3520/250 = 2.08590/300 = 1.97830/450 = 1.841160/550 = 2.11 4100/250 = 0.40170/300 = 0.57285/450 = 0.63215/550 = 0.39 QuarterAverage Seasonal Index 1(0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20 2 3 4 Time-Series Methods Seasonal Influences

58. Quarter Year 1 Year 2 Year 3 Year 4 145/250 = 0.1870/300 = 0.23100/450 = 0.22100/550 = 0.18 2335/250 = 1.34370/300 = 1.23585/450 = 1.30725/550 = 1.32 3520/250 = 2.08590/300 = 1.97830/450 = 1.841160/550 = 2.11 4100/250 = 0.40170/300 = 0.57285/450 = 0.63215/550 = 0.39 QuarterAverage Seasonal Index 1(0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20 2(1.34 + 1.23 + 1.30 + 1.32)/4 = 1.30 3(2.08 + 1.97 + 1.84 + 2.11)/4 = 2.00 4(0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50 Time-Series Methods Seasonal Influences

59. Quarter Year 1 Year 2 Year 3 Year 4 145/250 = 0.1870/300 = 0.23100/450 = 0.22100/550 = 0.18 2335/250 = 1.34370/300 = 1.23585/450 = 1.30725/550 = 1.32 3520/250 = 2.08590/300 = 1.97830/450 = 1.841160/550 = 2.11 4100/250 = 0.40170/300 = 0.57285/450 = 0.63215/550 = 0.39 QuarterAverage Seasonal IndexForecast 1(0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20 2(1.34 + 1.23 + 1.30 + 1.32)/4 = 1.30 3(2.08 + 1.97 + 1.84 + 2.11)/4 = 2.00 4(0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50 Projected Annual Demand = 2600 Average Quarterly Demand = 2600/4 = 650 Time-Series Methods Seasonal Influences

60. Quarter Year 1 Year 2 Year 3 Year 4 145/250 = 0.1870/300 = 0.23100/450 = 0.22100/550 = 0.18 2335/250 = 1.34370/300 = 1.23585/450 = 1.30725/550 = 1.32 3520/250 = 2.08590/300 = 1.97830/450 = 1.841160/550 = 2.11 4100/250 = 0.40170/300 = 0.57285/450 = 0.63215/550 = 0.39 QuarterAverage Seasonal IndexForecast 1(0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20650(0.20) =130 2(1.34 + 1.23 + 1.30 + 1.32)/4 = 1.30 3(2.08 + 1.97 + 1.84 + 2.11)/4 = 2.00 4(0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50 Projected Annual Demand = 2600 Average Quarterly Demand = 2600/4 = 650 Time-Series Methods Seasonal Influences

61. Quarter Year 1 Year 2 Year 3 Year 4 145/250 = 0.1870/300 = 0.23100/450 = 0.22100/550 = 0.18 2335/250 = 1.34370/300 = 1.23585/450 = 1.30725/550 = 1.32 3520/250 = 2.08590/300 = 1.97830/450 = 1.841160/550 = 2.11 4100/250 = 0.40170/300 = 0.57285/450 = 0.63215/550 = 0.39 QuarterAverage Seasonal IndexForecast 1(0.18 + 0.23 + 0.22 + 0.18)/4 = 0.20650(0.20) =130 2(1.34 + 1.23 + 1.30 + 1.32)/4 = 1.30650(1.30) =845 3(2.08 + 1.97 + 1.84 + 2.11)/4 = 2.00650(2.00) =1300 4(0.40 + 0.57 + 0.63 + 0.39)/4 = 0.50650(0.50) =325 Time-Series Methods Seasonal Influences

62. Remember Regression Analysis?

63. Dependent variable Independent variable X Y

64. Remember Regression Analysis? Dependent variable Independent variable X Y

65. Remember Regression Analysis? Dependent variable Independent variable X Y Regression equation: Y = a + bX

66. Remember Regression Analysis? Dependent variable Independent variable X Y Actual value of Y Value of X used to estimate Y Regression equation: Y = a + bX

67. Remember Regression Analysis? Dependent variable Independent variable X Y Actual value of Y Estimate of Y from regression equation Value of X used to estimate Y Regression equation: Y = a + bX

68. Remember Regression Analysis? Dependent variable Independent variable X Y Actual value of Y Estimate of Y from regression equation Value of X used to estimate Y Deviation, or error { Regression equation: Y = a + bX

69. Regression analysis in forecasting  Two applications of regressions analysis in forecasting  Time-series data  Independent variable is time  Dependent variable is the variable that you want to forecast (i.e. demand)  Data is not time-series  Independent variable is a known variable that can be used to predict (i.e. advertising dollars, customer population)  Dependent variable is the variable that you want to forecast (i.e. demand)  Regression analysis is the same in both applications

70. Armand, Inc.: Regression Analysis Armand, Inc. is a chain of Italian restaurants located in a five-state area.  The most successful locations have been near college campuses.  Prior to opening a new restaurant, management requires a forecast of the yearly sales revenues.  Such an estimate is used  in planning the restaurant capacity,  personnel requirements, and  to see if the operations costs are smaller than the predicted revenue.

71. Armand, Inc.

73. Go To Excel

74. Armand, Inc. Intercept Coefficient for Student Population

75. Armand, Inc.  Forecast the Annual Sales if the student population is 20,000.

76. Armand, Inc.  Forecast the Annual Sales if the student population is 20,000. Forecast is : $160,000

77. Forecasting accuracy “I think there is a world market for about FIVE computers.” —Thomas J. Watson, chairman of IBM, 1943

78. Forecast accuracy  IBM 1994  $700 million inventory of OBSOLETE PCs that took 6 months to unload.  Reaction: too conservative when releasing the new Aptiva home PCs. New models sold out before the holiday season had begun.

79. Measuring the quality of forecasting  MAD – mean absolute deviation  MSE – mean square error

80. Your Turn  Demand for April-September is given.  Determine the exponential smoothing forecasts for those April.  Forecast for Mar was 58  Demand for Mar was 60.  Determine the regression equation forecasts for those April.  X is the number of months in the future (for April, X = 1)

81. Your Turn Demand Exponential Smoothing alpha = 0.2 Regression Y = 54 + 3.9X April60 May55 June75 July60 August80 September75 Calculate for APRIL: Exponential smoothing forecast Regression forecast Forecast for Mar was 58 Demand for Mar was 60. X is the number of months in the future (for April, X = 1)

82. Your Turn Demand Exponential Smoothing alpha = 0.20 Regression Y = 54 + 3.9X April6058.41.657.92.1 May June July August September

83. Your Turn Demand Exponential Smoothing alpha = 0.20 Exp Smooth abs(forecast error) Regression Y = 54 + 3.9X Regression abs(forecast error) April6058.457.9 May5558.761.8 June7558.065.7 July6061.469.6 August8061.173.5 September7564.977.4 MAD = Calculate abs(forecast error) for April

84. Your Turn Demand Exponential Smoothing alpha = 0.20 Regression Y = 54 + 3.9X April6058.41.657.92.1 May5558.761.8 June7558.065.7 July6061.469.6 August8061.173.5 September7564.977.4 MAD =

85. Your Turn Demand Exponential Smoothing alpha = 0.20 Regression Y = 54 + 3.9X April6058.41.657.92.1 May5558.73.761.86.8 June7558.017.065.79.3 July6061.41.469.69.6 August8061.118.973.56.5 September7564.910.177.42.4 MAD = Calculate MAD for each.

86. Your Turn Demand Exponential Smoothing alpha = 0.20 Regression Y = 54 + 3.9X April6058.41.657.92.1 May5558.73.761.86.8 June7558.017.065.79.3 July6061.41.469.69.6 August8061.118.973.56.5 September7564.910.177.42.4 MAD =8.8MAD =6.12

87. Third Wave Research Group - offers marketing software and databases - Forecasts sales for specific -Market areas -Products -segments

88. Tracking Signals

89. Tracking signal = RSFE MAD +2.0 — +1.5 — +1.0 — +0.5 — 0 — –0.5 — –1.0 — –1.5 — ||||| 0510152025 Observation number Tracking signal Control limit

90. Tracking Signals Tracking signal = RSFE MAD +2.0 — +1.5 — +1.0 — +0.5 — 0 — –0.5 — –1.0 — –1.5 — ||||| 0510152025 Observation number Tracking signal Control limit Out of control

91. Tracking Signal Computation

104. Tracking Signals Tracking signal = RSFE MAD +2.0 — +1.5 — +1.0 — +0.5 — 0 — –0.5 — –1.0 — –1.5 — ||||| 0510152025 Observation number Tracking signal Control limit Out of control

105. Demand Forecast Applications  Time horizons Short term0 – 3 monthsDay to day More accurate Medium term3 months – 3 yearsSales & Production planning Budgeting Long termOver 3 yearsLong term projects Facility planning New products