After completing this chapter, students will be able to: At the end of this lecture, students should be able to know the importance of ICT in logistics management, know the ICT devices use in Logistics management. | 1-1 Logistics Management LSM 730 Dr. Khurrum S. Mughal Lecture 23 1 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 12-2 Exponential Smoothing 12-3 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 3 If = , then Ft +1 = Dt + Ft If = 0, then Ft +1 = 0 Dt + 1 Ft = Ft Forecast does not reflect recent data If = 1, then Ft +1 = 1 Dt + 0 Ft = Dt Forecast based only on most recent data Effect of Smoothing Constant 12-4 4 8-5 Classic Time Series Decomposition Model Basic formulation F = T S C R where F = forecast T = trend S = seasonal index C = cyclical index (usually 1) R = residual index (usually 1) CR (2004) Prentice Hall, Inc. CR (2004) Prentice Hall, Inc. Sales period (1) Time period, t (2) Sales (Dt ) ($000s) (3) Dt t (4) t2 (5) Trend value (Tt ) (6)= (2)/(5) Seasonal index Forecast ($000s) Summer 1 $9,458 9,458 1 $12,053 Trans-season 2 11,542 23,084 4 12,539 Fall 3 14,489 43,467 9 13,025 Holiday 4 15,754 63,016 16 13,512 Spring 5 17,269 86,345 25 13,998 Summer 6 11,514 69,084 36 14,484 Trans-season 7 12,623 88,361 49 14,970 Fall 8 16,086 128,688 64 15,456 Holiday 9 18,098 162,882 81 15,942 Spring 10 21,030 210,300 100 16,428 Summer 11 12,788 140,668 121 16,915 Trans-season 12 16,072 192,864 144 17,401 Fall 13 ? 17,887 * $18,602 Holiday 14 ? 18,373 * 20,945 Totals 78 176,723 1,218,217 650 Regression Forecasting Using Bobbie Brooks Sales Data N = 12 å Dt ´ t = 1,218,217 å t2 = 650 = = ( , / ) , . 176 723 12 14 726 92 = = 78 12 6 5 / . Regression equation is: Tt = 11, + *Forecasted values 8-35 8-7 CR (2004) Prentice Hall, Inc. Regression Analysis Basic formulation F = o 1X1 2X2 nXn Example Bobbie Brooks, a manufacturer of teenage women’s clothes, was able to forecast seasonal sales from the following relationship F = constant 1(Time) 2(consumer debt ratio) + 3(no. nonvendor accounts) 8-8 Combined Model Forecasting Combines the results of several models to improve overall accuracy. Consider the seasonal forecasting problem of Bobbie Brooks. Four models were used. Three of them were two forms of exponential smoothing and a regression model. The fourth was managerial judgement used by a vice president of marketing using experience. Each forecast is then weighted according to its respective error as shown below. Calculation of forecast weights Model type (1) Forecast error (2) Percent of total error (3)= (2) Inverse of error proportion (4)= (3)/ Model weights MJ R ES 1 ES 2 Total CR (2004) Prentice Hall, Inc. 8-9 Combined Model Forecasting (Cont’d) Weighted Average Fall Season Forecast Using Multiple Forecasting Techniques Forecast type (1) Model forecast (2) Weighting factor (3)= (1) ´ (2) Weighted proportion Regression model (R) $20,367,000 $11,813,000 Exponential Smoothing ES 1 20,400,000 6,732,000 Combined exponential smoothing-- regression model (ES 2 ) 17,660,000 883,000 Managerial judgment (MJ) 19,500,000 780,000 Weighted average forecast $20,208,000 CR (2004) Prentice Hall, Inc. CR (2004) Prentice Hall, Inc. Multiple Model Errors 8-38
