1. Chapter 12 Historical forecasting techniques
Best Practice in Inventory Management
Chapter Historical forecasting techniques
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2. Figure 12.1 Moving Averages
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3. Figure 12.2 Contribution from Each Period to the Average
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45 1 3 5 7 9 11 13 15
Age of Information
Contributions to the Average
Alpha =0.4
Alpha =0.3
Alpha =0.2
Alpha =0.1
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4. Figure 12.3 Exponential Forecast Sensitivity
Period
Demand
Forecast Demand
a = 0.1
a = 0.2
a = 0.4 1 57 99
113 2 35 95
102 85 3
171 89 88 73 4 49 97
105
129 5 62 92 94 6
189 87 80 7
109
108
120 8 36
100 9 30 76 10
131 81 74 11
178 91 12
Forecast
127

5. Figure 12.4 Exponential Forecasting
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6. Figure 12.5 Contributions of Historical Data to the Forecast
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Recent Old
History in Periods

7. Figure 12.6 Exponentially Weighted MAD
Week
Demand
Forecast alpha = 0.2
Absolute Error
e error in MAD
MAD
Initial
113 60 1 57
102 56 -4
59.4 2 35 88 67 7
60.5 3
171
105 83 22
63.8 4 49 94 -8
62.6 5 62 87 32
-31
58.0 6
189
108 44
64.5
109
-63
55.0 8 36 72 17
57.6 9 30 81 64
58.5 10
131 91 50
57.2 11
178
61.7
Beta =
0.15
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8. Figure 12.7 Focus Forecasting
Period
Demand
Alternative forecasts 1 28
Model
Parameters
Forecast
MAD 2 45
Type 3 33
Moving
12 month
26.8
5.9 4 39
Average
6 month
22.0
7.4 5 17
a =0 .1
28.3
9.5 6 27
Exponential
a =0 .2
24.7
6.2 7 22
Smoothing
a =0 .3
23.8
5.7 8 24
a =0 .4
23.9
5.2 9 15
Double
21.9
5.4 10 18
21.8
5.0 11
21.4 12 25
For Double Exponential Smoothing see next chapter
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9. Figure 12.8 Tracking Signals
Variable demand
Rising Demand
Period
Demand
Exp Error
Tracking signal
a = 0.2
a = 0.4
a = 0.1
a = 0.3
Initial
0.00
0.0 47 1 57
-9.96
-0.2
-10.97 72
-0.81 2 35
-20.15
-0.4
-18.92
-0.3 67
2.33
0.1
1.97 3
171
-6.74
-0.1
-3.12 82
3.95
0.2
2.21 4 49
-11.63
-6.31
104
7.38
0.3
4.73 5 62
-16.32
-15.39 31
13.19
0.5
8.79
0.4 6
189
0.42
1.88 90
6.49
-0.06 7
109
3.60
5.99 91
10.12
5.37 8 36
-7.70
-7.52
105
12.89
6.77 9 30
-18.24
-16.88 92
16.92
0.6
9.35 10
131
-9.89
-6.08
113
17.80
8.47 11
178
6.17
10.49
100
21.35
0.7
11.09
Unacceptable forecasts are shaded
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10. Figure 12.9 Trigg’s Forecasting Method
Trigg’s method is very elegant but not very useful in itself
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11. Figure 12.10 Tracking Signals
Exponential Forecasting
12.10b
Trigg's Method
Period
Demand
Normal Exponential Forecast
Error in Forecast
Smoothed M.A.D.
Smoothed Error
Tracking Signal
Trigg's Exponential Forecast
a = 0.2
β = 0.1
d = 0.1 1 13 16 -3 4 2
15.4
-14.4
5.04
-1.44
-0.29
-15
5.1
-1.5 3 10
12.52
-2.52
4.79
-1.55
-0.32
11.59
-1.59
4.75
-1.51 17
12.02
4.98
4.81
-0.89
-0.19
11.08
5.92
4.87
-0.77
-0.16 5 11
13.01
-2.01
4.53
-1.01
-0.22
-1.02
4.48
-0.79
-0.18 6 19
12.61
6.39
4.71
-0.27
-0.06
11.84
7.16 7
13.89
2.11
4.45
-0.03
-0.01
4.16
4.69
0.42
0.09 8 15
14.31
0.69
4.08
0.04
0.01
12.21
2.79
4.5
0.66
0.15 9 26
14.45
11.55
4.82
1.19
0.25
12.62
13.38
5.39
1.93
0.36 38
16.76
21.24
6.47
3.2
0.49
17.41
20.59
6.91
3.79
0.55 31
21.01
9.99
6.82
3.88
0.57
28.72
2.28
6.44
3.64 12 40
23.01
16.99
7.84
5.19
30.01
6.8
4.28
0.63 35
26.4
8.6
7.91
5.53
0.7
36.3
-1.3
6.25
3.72
0.6 14 32
28.12
7.51
5.36
0.71
35.52
-3.52
5.98
0.5
28.9
33.76
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