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.
Logistics Management LSM 730 Lecture 23 Dr Khurrum S Mughal 1-1 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 Ft +1 = ∆ t + (1 ) Φt where: Ft +1 = forecast for next period Dt = actual demand for present period Ft = previously determined forecast for present period = ωειγ ητινγ φαχτορ, σµοοτηινγ χονσταντ 12-3 Effect of Smoothing Constant 0.0 1.0 = 0.20, τηεν Φt +1 = 0.20 ∆ t + 0.80 Ft Ιφ If = 0, τηεν Φt +1 = 0 ∆ t + 1 Ft = Ft Forecast does not reflect recent data If = 1, τηεν Φt +1 = 1 ∆ t + 0 Ft = ∆ t Forecast based only on most recent data 12-4 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 8-5 Regression Forecasting Using Bobbie Brooks Sales Data (1) Sales period Time period, t Summer Trans-season Fall Holiday Spring Summer Trans-season Fall Holiday Spring 10 Summer Trans-season Fall Holiday Totals 11 12 13 14 78 (2) (3) (4) (5) (6)= (2)/(5) Seasonal Forecast index ($000s) 0.78 0.92 1.11 1.17 1.23 Sales (Dt ) ($000s) $9,458 11,542 14,489 15,754 17,269 Dt t 9,45 23,084 43,467 63,016 86,345 t2 16 25 Trend value (Tt) $12,053 12,539 13,025 13,512 13,998 11,514 12,623 16,086 18,098 21,030 69,084 88,361 128,688 162,882 210,300 36 49 64 81 100 14,484 14,970 15,456 15,942 16,428 0.79 0.84 1.04 1.14 1.28 12,788 140,668 16,072 192,864 ? ? 176,723 1,218,217 121 144 16,915 17,401 17,887* 18,373* 0.76 0.92 $18,602 20,945 650 N = 12 ∑Dt×t = 1,218,217∑t2 = 650 D = ( 176 ,723 / 12 ) = 14 ,726 92 t = 78 / 12 = Regression equation is: Tt = 11,567.08 + 486.13t *Forecasted values CR (2004) Prentice Hall, Inc 8-35 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) CR (2004) Prentice Hall, Inc 8-7 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 (1) (3)= (4)= 1.0/(2) (3)/48.09 Percent Inverse of Model Forecast of total error Model type error error proportion weights MJ R ES1 ES2 Total 9.0 0.7 1.2 8.4 19.3 CR (2004) Prentice Hall, Inc (2) 0.466 0.036 0.063 0.435 1.000 2.15 27.77 15.87 2.30 48.09 0.04 0.58 0.33 0.05 1.00 8-8 Forecast type (1) (2) Model forecast Weighting factor Regression model (R) $20,367,000 0.58 Exponential Smoothing ES1 20,400,000 0.33 Combined exponential smoothing-regression model 17,660,000 0.05 (ES2) Managerial judgment (MJ) 19,500,000 0.04 Weighted average forecast CR (2004) Prentice Hall, Inc (3)= (1) × (2) Weighted proportion $11,813,000 6,732,000 883,000 780,000 $20,208,000 8-9 Weighted Average Fall Season Forecast Using Multiple Forecasting Techniques Combined Model Forecasting (Cont’d) Multiple Model Errors CR (2004) Prentice Hall, Inc 8-38 ... Inc 8-5 Regression Forecasting Using Bobbie Brooks Sales Data (1) Sales period Time period, t Summer Trans-season Fall Holiday Spring Summer Trans-season Fall Holiday Spring 10 Summer Trans-season... 1.11 1.17 1 .23 Sales (Dt ) ($000s) $9,458 11,542 14,489 15,754 17,269 Dt t 9,45 23, 084 43,467 63,016 86,345 t2 16 25 Trend value (Tt) $12,053 12,539 13,025 13,512 13,998 11,514 12, 623 16,086 18,098... 140,668 16,072 192,864 ? ? 176, 723 1,218,217 121 144 16,915 17,401 17,887* 18,373* 0.76 0.92 $18,602 20,945 650 N = 12 ∑Dt×t = 1,218,217∑t2 = 650 D = ( 176 , 723 / 12 ) = 14 ,726 92 t = 78