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Forecasting

Forecasting. Chapter 11. Operations Management - 5 th Edition. Roberta Russell & Bernard W. Taylor, III. Forecasting. Predicting the Future Qualitative forecast methods subjective Quantitative forecast methods based on mathematical formulas. Forecasting and Supply Chain Management.

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Forecasting

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  1. Forecasting Chapter 11 Operations Management - 5th Edition Roberta Russell & Bernard W. Taylor, III

  2. Forecasting • Predicting the Future • Qualitative forecast methods • subjective • Quantitative forecast methods • based on mathematical formulas

  3. Forecasting and Supply Chain Management • Accurate forecasting determines how much inventory a company must keep at various points along its supply chain • Continuous replenishment • supplier and 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

  4. Forecasting and TQM • Accurate forecasting customer demand is a key to providing good quality service • Continuous replenishment and JIT complement TQM • eliminates the need for buffer inventory, which, in turn, reduces both waste and inventory costs, a primary goal of TQM • smoothes process flow with no defective items • meets expectations about on-time delivery, which is perceived as good-quality service

  5. Types of Forecasting Methods • Depend on • time frame • demand behavior • causes of behavior

  6. 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

  7. 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

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

  9. Forecasting Methods • Qualitative • use management judgment, expertise, and opinion to predict future demand • 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

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

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

  12. 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 • linear trend line

  13. Moving Average • Naive forecast • demand the current period is used as next period’s forecast • Simple moving average • stable demand with no pronounced behavioral patterns • Weighted moving average • weights are assigned to most recent data

  14. ORDERS MONTH PER MONTH Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 FORECAST - 120 90 100 75 110 50 75 130 110 90 Nov - Moving Average:Naïve Approach

  15. n i= 1  Di MAn = n where n = number of periods in the moving average Di= demand in period i Simple Moving Average

  16. 3 i= 1 ORDERS MONTH PER MONTH MOVING AVERAGE  Di MA3 = Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 Nov - – – – 103.3 88.3 95.0 78.3 78.3 85.0 105.0 110.0 3 = 110 orders for Nov 90 + 110 + 130 3 = 3-month Simple Moving Average

  17. 5 i= 1 ORDERS MONTH PER MONTH MOVING AVERAGE  Di MA5 = Jan 120 Feb 90 Mar 100 Apr 75 May 110 June 50 July 75 Aug 130 Sept 110 Oct 90 Nov - – – – – – 99.0 85.0 82.0 88.0 95.0 91.0 5 90 + 110 + 130+75+50 5 = = 91 orders for Nov 5-month Simple Moving Average

  18. 150 – 125 – 100 – 75 – 50 – 25 – 0 – 5-month Orders 3-month Actual | | | | | | | | | | | Jan Feb Mar Apr May June July Aug Sept Oct Nov Month Smoothing Effects

  19. Wi Di WMAn = i = 1 where Wi = the weight for period i, between 0 and 100 percent  Wi= 1.00 Weighted Moving Average Adjusts moving average method to more closely reflect data fluctuations

  20. 3 i = 1  WMA3 = Wi Di November Forecast = (0.50)(90) + (0.33)(110) + (0.17)(130) = 103.4 orders Weighted Moving Average Example MONTH WEIGHT DATA August 17% 130 September 33% 110 October 50% 90

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

  22. Exponential Smoothing (cont.) Ft +1 = Dt + (1 - )Ft where: Ft +1 = forecast for next period Dt= actual demand for present period Ft= previously determined forecast for present period = weighting factor, smoothing constant

  23. Effect of Smoothing Constant 0.0  1.0If = 0.20, then Ft +1 = 0.20Dt + 0.80 Ft If = 0, then Ft+1 = 0Dt + 1 Ft0 = FtForecast does not reflect recent data If = 1, then Ft +1 = 1Dt + 0 Ft=DtForecast based only on most recent data

  24. PERIOD MONTH DEMAND 1 Jan 120 2 Feb 90 3 Mar 100 4 Apr 75 5 May 110 6 Jun 50 7 Jul 75 8 Aug 130 9 Sep 110 10 Oct 90 Exponential Smoothing (α=0.10) F2 = D1 + (1 - )F1 = (0.10)(120) + (0.90)(120) = 120 F3 = D2 + (1 - )F2 = (0.10)(90) + (0.90)(120) = 117 F11 = D10 + (1 - )F10 = (0.10)(90) + (0.90)(105.34) = 103.81

  25. Forecast Accuracy • Forecast error • difference between forecast and actual demand • MAD • mean absolute deviation • MAPD • mean absolute percent deviation • Cumulative error • Average error or bias

  26. Dt - Ft n MAD = Mean Absolute Deviation (MAD) where t = period number Dt = demand in period t Ft = forecast for period t n = total number of periods  = absolute value

  27. Mean absolute percent deviation (MAPD) MAPD = |Dt - Ft| Dt Cumulative error E = et et n Average error E = Other Accuracy Measures

  28. Regression Methods • Linear regression • a 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

  29. 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 x2- nx2 x n y n Linear Regression

  30. Linear Regression Example x y (WINS) (ATTENDANCE) xyx2 4 36.3 145.2 16 6 40.1 240.6 36 6 41.2 247.2 36 8 53.0 424.0 64 6 44.0 264.0 36 7 45.6 319.2 49 5 39.0 195.0 25 7 47.5 332.5 49 49 346.7 2167.7 311

  31. x = = 6.125 y = = 43.36 b= = = 4.06 a= y - bx = 43.36 - (4.06)(6.125) = 18.46 49 8 xy - nxy2 x2 - nx2 346.9 8 (2,167.7) - (8)(6.125)(43.36) (311) - (8)(6.125)2 Linear Regression Example (cont.)

  32. Regression equation Attendance forecast for 7 wins 60,000 – 50,000 – 40,000 – 30,000 – 20,000 – 10,000 – y = 18.46 + 4.06x y = 18.46 + 4.06(7) = 46.88, or 46,880 Attendance, y Linear regression line, y = 18.46 + 4.06x | | | | | | | | | | | 0 1 2 3 4 5 6 7 8 9 10 Wins, x Linear Regression Example (cont.)

  33. Correlation and Coefficient of Determination • Correlation, r • Measure of strength of relationship • Varies between -1.00 and +1.00 • Coefficient of determination, r2 • Percentage of variation in dependent variable resulting from changes in the independent variable

  34. n xy -  x y [n x2 - ( x)2] [n y2 - ( y)2] r = (8)(2,167.7) - (49)(346.9) [(8)(311) - (49)2] [(8)(15,224.7) - (346.9)2] r = r = 0.947 Coefficient of determination r2 = (0.947)2 = 0.897 Computing Correlation

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