1. Chapter 12 Forecasting

2. Lecture Outline Strategic Role of Forecasting in SCM Components of Forecasting Demand Time Series Methods Forecast Accuracy Regression Methods 12-2

3. Forecasting Predicting the future Qualitative forecast methods subjective Quantitative forecast methods based on mathematical formulas 12-3

4. Supply Chain Management Accurate forecasting determines inventory levels in the supply chain Continuous replenishment supplier & customer share continuously updated data typically managed by the supplier reduces inventory for the company speeds customer delivery Variations of continuous replenishment quick response JIT (just-in-time) VMI (vendor-managed inventory) stockless inventory 12-4

5. The Effect of Inaccurate Forecasting 12-5

6. Forecasting Quality Management Accurately forecasting customer demand is a key to providing good quality service Strategic Planning Successful strategic planning requires accurate forecasts of future products and markets 12-6

7. Types of Forecasting Methods Depend on time frame demand behavior causes of behavior 12-7

8. Time Frame Indicates how far into the future is forecast Short- to mid-range forecast typically encompasses the immediate future daily up to two years Long-range forecast usually encompasses a period of time longer than two years 12-8

9. Demand Behavior Trend a gradual, long-term up or down movement of demand Random variations movements in demand that do not follow a pattern Cycle an up-and-down repetitive movement in demand Seasonal pattern an up-and-down repetitive movement in demand occurring periodically 12-9

10. Forms of Forecast Movement 12-10 Time (a) Trend Time (d) Trend with seasonal pattern Time (c) Seasonal pattern Time (b) Cycle Demand Random movement

11. Forecasting Methods Time series statistical techniques that use historical demand data to predict future demand Regression methods attempt to develop a mathematical relationship between demand and factors that cause its behavior Qualitative use management judgment, expertise, and opinion to predict future demand 12-11

12. Qualitative Methods Management, marketing, purchasing, and engineering are sources for internal qualitative forecasts Delphi method involves soliciting forecasts about technological advances from experts 12-12

13. Forecasting Process 12-13 6. Check forecast accuracy with one or more measures 4. Select a forecast model that seems appropriate for data 5. Develop/compute forecast for period of historical data 8a. Forecast over planning horizon 9. Adjust forecast based on additional qualitative information and insight 10. Monitor results and measure forecast accuracy 8b. Select new forecast model or adjust parameters of existing model 7. Is accuracy of forecast acceptable? 1. Identify the purpose of forecast 3. Plot data and identify patterns 2. Collect historical data No Yes

14. Time Series Assume that what has occurred in the past will continue to occur in the future Relate the forecast to only one factor - time Include moving average exponential smoothing 12-14

15. Moving Average Naive forecast demand in current period is used as next period’s forecast Simple moving average uses average demand for a fixed sequence of periods stable demand with no pronounced behavioral patterns Weighted moving average weights are assigned to most recent data 12-15

16. Moving Average: Naïve Approach 12-16 Jan120 Feb90 Mar100 Apr75 May110 June50 July75 Aug130 Sept110 Oct90 ORDERS MONTHPER MONTH - 120 90 100 75 110 50 75 130 110 90 Nov - FORECAST

17. Simple Moving Average 12-17 MA n = n i = 1  DiDi n where n =number of periods in the moving average D i =demand in period i

18. 3-month Simple Moving Average 12-18 Jan120 Feb90 Mar100 Apr75 May110 June50 July75 Aug130 Sept110 Oct90 Nov- ORDERS MONTHPER MONTH MA 3 = 3 i = 1  DiDi 3 = 90 + 110 + 130 3 = 110 orders for Nov – 103.3 88.3 95.0 78.3 85.0 105.0 110.0 MOVING AVERAGE

19. 5-month Simple Moving Average 12-19 MA 5 = 5 i = 1  DiDi 5 = 90 + 110 + 130+75+50 5 = 91 orders for Nov Jan120 Feb90 Mar100 Apr75 May110 June50 July75 Aug130 Sept110 Oct90 Nov- ORDERS MONTHPER MONTH – 99.0 85.0 82.0 88.0 95.0 91.0 MOVING AVERAGE

20. Smoothing Effects 12-20 150 – 125 – 100 – 75 – 50 – 25 – 0 – ||||||||||| JanFebMarAprMayJuneJulyAugSeptOctNov Actual Orders Month 5-month 3-month

21. Weighted Moving Average 12-21 Adjusts moving average method to more closely reflect data fluctuations WMA n = i = 1  W i D i where W i = the weight for period i, between 0 and 100 percent  W i = 1.00 n

22. Weighted Moving Average Example 12-22 MONTH WEIGHT DATA August 17%130 September 33%110 October 50%90 WMA 3 = 3 i = 1  W i D i = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders November Forecast

23. Exponential Smoothing 12-23 Averaging method Weights most recent data more strongly Reacts more to recent changes Widely used, accurate method

24. Exponential Smoothing 12-24 F t +1 =  D t + (1 -  )F t where: F t +1 =forecast for next period D t =actual demand for present period F t =previously determined forecast for present period  =weighting factor, smoothing constant

25. 0.0  1.0 If  = 0.20, then F t +1 = 0.20  D t + 0.80 F t If  = 0, then F t +1 = 0  D t + 1 F t = F t Forecast does not reflect recent data If  = 1, then F t +1 = 1  D t + 0 F t =  D t Forecast based only on most recent data Effect of Smoothing Constant 12-25

26. Exponential Smoothing (α=0.30) 12-26 F 2 =  D 1 + (1 -  )F 1 = (0.30)(37) + (0.70)(37) = 37 F 3 =  D 2 + (1 -  )F 2 = (0.30)(40) + (0.70)(37) = 37.9 F 13 =  D 12 + (1 -  )F 12 = (0.30)(54) + (0.70)(50.84) = 51.79 PERIODMONTHDEMAND 1Jan37 2Feb40 3Mar41 4Apr37 5May 45 6Jun50 7Jul 43 8Aug 47 9Sep 56 10Oct52 11Nov55 12Dec 54

27. Exponential Smoothing 12-27 FORECAST, F t + 1 PERIODMONTHDEMAND(  = 0.3)(  = 0.5) 1Jan37–– 2Feb4037.0037.00 3Mar4137.9038.50 4Apr3738.8339.75 5May 4538.2838.37 6Jun5040.2941.68 7Jul 4343.2045.84 8Aug 4743.1444.42 9Sep 5644.3045.71 10Oct5247.8150.85 11Nov5549.0651.42 12Dec 5450.8453.21 13Jan–51.7953.61

28. Exponential Smoothing 12-28 70 – 60 – 50 – 40 – 30 – 20 – 10 – 0 – ||||||||||||| 12345678910111213 Actual Orders Month  = 0.50  = 0.30

29. Forecast Accuracy Forecast error difference between forecast and actual demand MAD mean absolute deviation MAPD mean absolute percent deviation Cumulative Forecasting Error (CFE) 12-29

30. Mean Absolute Deviation (MAD) 12-30 where t = period number D t = demand in period t F t = forecast for period t n = total number of periods  = absolute value  D t - F t  n MAD =

31. 12-31 MAD Example 13737.00–– 24037.003.003.00 34137.903.103.10 43738.83-1.831.83 54538.286.726.72 65040.299.699.69 74343.20-0.200.20 84743.143.863.86 95644.3011.7011.70 105247.814.194.19 115549.065.945.94 125450.843.153.15 55749.3153.39 PERIODDEMAND, D t F t (  =0.3)(D t - F t ) |D t - F t |

32. MAD Calculation 12-32  D t - F t  n MAD= = = 4.85 53.39 11

33. Other Accuracy Measures 12-33 Mean absolute percent deviation (MAPD) MAPD =  |D t - F t |  D t Cumulative error E =  e t Average error E = etnetn

34. Comparison of Forecasts 12-34 FORECASTMADMAPDE(E) Exponential smoothing (  = 0.30)4.859.6%49.314.48 Exponential smoothing (  = 0.50)4.048.5%33.213.02

35. Time Series Forecasting Using Excel Excel can be used to develop forecasts: Moving average Exponential smoothing Adjusted exponential smoothing 12-35

36. Exponentially Smoothed and Adjusted Exponentially Smoothed Forecasts Copyright 2011 John Wiley & Sons, Inc. 12-36 =B5*(C11-C10)+ (1-B5)*D10 =C10+D10 =ABS(B10-E10) =SUM(F10:F20) =G22/11

37. Demand and Exponentially Smoothed Forecast Copyright 2011 John Wiley & Sons, Inc. 12-37 Click on “Insert” then “Line”

38. Forecasting With OM Tools Copyright 2011 John Wiley & Sons, Inc. 12-38

39. Regression Methods Linear regression mathematical technique that relates a dependent variable to an independent variable in the form of a linear equation Correlation a measure of the strength of the relationship between independent and dependent variables 12-39

40. Linear Regression 12-40 y = a + bx a = y - b x b = where a =intercept b =slope of the line x == mean of the x data y == mean of the y data  xy - nxy  x 2 - nx 2  x n  y n

41. Linear Regression Example 12-41 xy (WINS)(ATTENDANCE) xyx 2 436.3145.216 640.1240.636 641.2247.236 853.0424.064 644.0264.036 745.6319.249 539.0195.025 747.5332.549 49346.72167.7311

42. Linear Regression Example 12-42 ||||||||||| 012345678910 60,000 – 50,000 – 40,000 – 30,000 – 20,000 – 10,000 – Linear regression line, y = 18.46 + 4.06 x Wins, x Attendance, y y = 18.46 + 4.06(7) = 46.88, or 46,880 Attendance forecast for 7 wins

43. Correlation and Coefficient of Determination Correlation, r Measure of strength of relationship Varies between -1.00 and +1.00 Coefficient of determination, r 2 Percentage of variation in dependent variable resulting from changes in the independent variable 12-43

44. Regression Analysis With Excel 12-44