Best Practices in Historical Forecasting Techniques for Inventory Management

Explore Dr. Tony Wild's insights on historical forecasting techniques in inventory management, including moving averages, exponential forecasting, and sensitivity analysis. These techniques help businesses make informed decisions based on past data, contributing to efficient inventory management practices.

• Inventory
• Forecasting
• Historical
• Techniques
• Management

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1. Best Practice in Inventory Management CHAPTER 12 HISTORICAL FORECASTING TECHNIQUES Dr Tony Wild Best Practice in Inventory Management

2. Figure 12.1 Moving Averages b) Variable Demand a) Smooth Demand c) Increasing Demand 200 200 200 180 180 180 160 160 160 140 140 140 Demand 120 120 120 100 100 100 6 week average 80 80 80 60 60 60 12 week average 40 40 40 20 20 20 26 week average 0 0 0 1 2 3 4 5 6 7 8 9 1011 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 Dr Tony Wild Best Practice in Inventory Management

3. Figure 12.2 Contribution from Each Period to the Average 0.45 0.4 0.35 Contributions to the Average 0.3 Alpha =0.4 0.25 Alpha =0.3 0.2 Alpha =0.2 Alpha =0.1 0.15 0.1 0.05 0 Dr Tony Wild 1 3 5 7 9 11 13 15 Best Practice in Inventory Management Age of Information

4. Figure 12.3 Exponential Forecast Sensitivity Forecast Demand a = 0.1 a = 0.2 99 95 89 97 92 89 99 100 94 87 92 100 Period Demand a = 0.4 99 85 73 129 89 80 120 100 76 74 100 127 1 2 3 4 5 6 7 8 9 10 11 12 57 35 171 49 62 189 109 36 30 131 178 113 102 88 105 94 87 108 108 94 81 91 108 Forecast Best Practice in Inventory Management

5. Figure 12.4 Exponential Forecasting Dr Tony Wild Best Practice in Inventory Management

6. Figure 12.5 Contributions of Historical Data to the Forecast 1 2 3 4 5 6 7 8 9 10 11 12 13 1 0.9 0.8 Demand 30% of result 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Dr Tony Wild Recent Old History in Periods Best Practice in Inventory Management

7. Figure 12.6 Exponentially Weighted MAD Forecast alpha = 0.2 Absolute Error e error in MAD Week Demand MAD Initial 1 2 3 4 5 6 7 8 9 10 11 Beta = 113 102 88 105 94 87 108 108 94 81 91 108 60 59.4 60.5 63.8 62.6 58.0 64.5 55.0 57.6 58.5 57.2 61.7 57 35 171 49 62 189 109 36 30 131 178 56 67 83 56 32 102 1 72 64 50 87 -4 7 22 -8 -31 44 -63 17 6 -8 30 0.15 Best Practice in Inventory Management Dr Tony Wild

8. Figure 12.7 Focus Forecasting Alternative forecasts Model Type Moving Average Period 1 2 3 4 5 6 7 8 9 10 11 12 Demand 28 45 33 39 17 27 22 24 15 18 28 25 Parameters Forecast Demand 26.8 22.0 28.3 24.7 23.8 23.9 21.9 21.8 21.4 MAD 12 month 6 month = = = = = = = 5.9 7.4 9.5 6.2 5.7 5.2 5.4 5.0 5.7 Exponential Smoothing Double Exponential Smoothing For Double Exponential Smoothing see next chapter Best Practice in Inventory Management Dr Tony Wild

9. Figure 12.8 Tracking Signals Variable demand Rising Demand Exp Error Tracking signal Exp Error Tracking signal Exp Error Tracking signal Exp Error Tracking signal Demand Demand Period a = 0.2 a = 0.4 a = 0.1 a = 0.3 Initial 1 2 3 4 5 6 7 8 9 10 11 0.00 -9.96 -20.15 -6.74 -11.63 -16.32 0.42 3.60 -7.70 -18.24 -9.89 6.17 0.0 -0.2 -0.4 -0.1 -0.2 -0.3 0.0 0.1 -0.1 -0.3 -0.2 0.1 0.00 -10.97 -18.92 -3.12 -6.31 -15.39 1.88 5.99 -7.52 -16.88 -6.08 10.49 0.0 -0.2 -0.3 -0.1 -0.1 -0.3 0.0 0.1 -0.1 -0.3 -0.1 0.2 47 72 67 82 104 31 90 91 105 92 113 100 0.00 -0.81 2.33 3.95 7.38 13.19 6.49 10.12 12.89 16.92 17.80 21.35 0.0 0.0 0.1 0.2 0.3 0.5 0.2 0.4 0.5 0.6 0.6 0.7 0.00 -0.81 1.97 2.21 4.73 8.79 -0.06 5.37 6.77 9.35 8.47 11.09 0.0 0.0 0.1 0.1 0.2 0.4 0.0 0.2 0.3 0.4 0.4 0.5 57 35 171 49 62 189 109 36 30 131 178 Unacceptable forecasts are shaded Best Practice in Inventory Management Dr Tony Wild

10. Figure 12.9 Triggs Forecasting Method Chart Title 45 40 Demand & Forecast values 35 30 Demand 25 Exponential Forecast a = 0.2 Trigg's Exponential Forecast Trigg s method is very elegant but not very useful in itself 20 15 10 5 0 1 2 3 4 5 6 7 8 9 101112131415 Best Practice in Inventory Management Dr Tony Wild

11. Figure 12.10 Tracking Signals 12.10a Exponential Forecasting Normal Exponential Forecast Error in Forecast 12.10b Trigg's Exponential Forecast Trigg's Method Smoothed Error Smoothe d Error Smoothed M.A.D. Tracking Signal Error in Forecast Smoothed M.A.D. Tracking Signal Period Demand = 0.1 = 0.1 = = a = 0.2 16 15.4 12.52 12.02 13.01 12.61 13.89 14.31 14.45 16.76 21.01 23.01 26.4 28.12 28.9 = = 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 13 1 10 17 11 19 16 15 26 38 31 40 35 32 -3 4 0 0 16 16 -3 4 0 0 -14.4 -2.52 4.98 -2.01 6.39 2.11 0.69 11.55 21.24 9.99 16.99 8.6 3.88 5.04 4.79 4.81 4.53 4.71 4.45 4.08 4.82 6.47 6.82 7.84 7.91 7.51 -1.44 -1.55 -0.89 -1.01 -0.27 -0.03 0.04 1.19 3.2 3.88 5.19 5.53 5.36 -0.29 -0.32 -0.19 -0.22 -0.06 -0.01 0.01 0.25 0.49 0.57 0.66 0.7 0.71 -15 5.1 4.75 4.87 4.48 4.75 4.69 4.5 5.39 6.91 6.44 6.8 6.25 5.98 -1.5 -1.51 -0.77 -0.79 -0.29 -0.32 -0.16 -0.18 11.59 11.08 12.02 11.84 11.84 12.21 12.62 17.41 28.72 30.01 36.3 35.52 33.76 -1.59 5.92 -1.02 7.16 4.16 2.79 13.38 20.59 2.28 9.99 -1.3 -3.52 0 0 0.42 0.66 1.93 3.79 3.64 4.28 3.72 0.09 0.15 0.36 0.55 0.57 0.63 0.6 0.5 3 Best Practice in Inventory Management Dr Tony Wild