1. Chapter 13 Improved forecasting methods
- Better forecasting for rising or falling demand
- Coping with seasonal demand
- Alternative techniques

2. Figure 13.1 Forecasting Trend

3. Figure 13.2 Double exponential model
Y=bx + d
Y Demand b a
Time Periods x

4. Figure 13.3 Double exponential smoothing process

5. Figure 13.4 Double Exponential Calculations
Week
Customer Demand
Error
Demand Level recalculated, d
Trend Recalculated, t
Forecast for next week, Dt-1 9
61.34
2.57
63.91 10 91
-27.1
73.66
3.65
77.31 11 40
37.3
63.88
2.16
66.04 12 56
10.0
62.42
1.76
64.18 13
8.2
61.23
1.43
62.66 14 35
27.7
52.70
0.32
53.02 15 80
-27.0
62.74
1.40
64.14 16 85
-20.9
71.65
2.23
73.88 17 43
30.9
62.76
1.00
63.76

6. Figure 13.5 Double Exponential Smoothing Forecasts
Demand
Weeks

7. Figure 13.6 Effects of Sudden Demand Changes2 1

8. Seasonal Demand History1 2 3 4 5 6 7 8 9 10 11 12
Year -3
Year -2
Year -1 50
100
150
200
250
300
Month
Demand

9. Fig 13.7 Creating the Base Series
Demand History
Seasonal Factors
Base Series
Month
Year 1
Year 2
Year 3 1 40 60
0.421
0.600
0.571
0.531 2 20
0.211
0.200
0.381
0.264 3 80 70
0.842
0.700
0.705 4
200
150
180
2.105
1.500
1.714
1.773 5
230
240
1.579
2.300
2.286
2.055 6
210
270
2.211
2.400
2.571
2.394 7
140
160
1.474
1.600
1.905
1.659 8
0.737
0.503 9 30
0.316
0.300
0.000
0.205 10
0.105
0.095
0.134 11
100 50
1.000
0.476
0.738 12
120 90
1.263
0.857
1.040
Total 95
105

10. Figure 13.8 Forecasting with Base Series
Seasonal
Month
Forecast Sales
Actual Sales
Base Series from figure 13.7
Equivalent Average Demand
New Smoothed Average
 Initial
Forecast  =
130 1
68.90 50
0.53
94.3
122.87 2
31.95 30
0.26
115.4
121.37 3
84.96 70
0.7
100.0
117.10 4
207.26
260
1.77
146.9
123.06 5
252.27
220
2.05
107.3
119.91 6
286.58
2.39 7
1.66 8
0.5
 Normal
 Exponential 9
0.21
 Smoothing
  alpha = 0.2 10
0.13 11
0.74 12
1.04
Calculation
Base Series X New Smoothed Average
Actual Sales Recorded
From Historical Base Series
Actual Sales divided by Base Series Value
Exponential Calculation from Forecast & Equivalent Actual

11. Figure 13.9 Baysian Approach
Causes of forecast errors

12. Figure 13.10 Forecasting Models
Application
Constraints
Moving Average
Static demand level, irregular pattern
Usually Unsuitable for major products
Regression Analysis
Continuous trend, irregular pattern
Oversensitive to outliers
Exponential Smoothing
Variable demand, gradual changing level, and slow moving spares items
Good Basic Workhorse
Base Series
Seasonal demand, mature products with low variability
Basis for most seasonal forecasting
Double Exponential Smoothing
consistent increasing or decreasing demand, product phase-in and obsolescence
Apply where variability does not mask trend
Fourier Analysis
Cyclic established demand e.g. commodities
Product with long consistent history
Baysian Forecasting
General products
Mathematics needs developing
Fuzzy Logic
Sophisticated modelling
Not yet available
More sophisticated techniques
Improved exponential forecasts with more complex formulae
Much history often needed, for longer term forecasting
Specific models
Based on market knowledge, potentially most accurate
Unreliable due to forecasters bias