Instructor:
Cecil Bozarth , PhD
North Carolina State University
Author of "Introduction to Operations and Supply Chain Management," 2nd edition, Pearson, Prentice-Hall
SECTION Index
2. Double Exponential Smoothing
Advanced Techniques
Forecasting Strategies
Approaches to Forecasting : A Tutorial
Measuring Forecast Accuracy
How Do We Measure Forecast Accuracy?
How Do We Measure Forecast Accuracy?
- Used to measure:
- Forecast model bias
- Absolute size of the forecast errors
- Can be used to:
- Compare alternative forecasting models
- Identify forecast models that need adjustment (management by exception)
Measures of Forecast Accuracy
Error = Actual demand - Forecast
OR
et = At - Ft
Mean Forecast Error (MFE)
For n time periods where we have actual demand and forecast values:
Ideal value = 0;
MFE > 0, model tends to under-forecast
MFE < 0, model tends to over-forecast
Mean Absolute Deviation (MAD)
For n time periods where we have actual demand and forecast values:
While MFE is a measure of forecast model bias, MAD indicates the absolute size of the errors
Example
Period |
Demand |
Forecast |
Error |
Absolute |
3 |
11 |
13.5 |
-2.5 |
2.5 |
4 |
9 |
13 |
-4.0 |
4.0 |
5 |
10 |
10 |
0 |
0.0 |
6 |
8 |
9.5 |
-1.5 |
1.5 |
7 |
14 |
9 |
5.0 |
5.0 |
8 |
12 |
11 |
1.0 |
1.0 |
Period |
Demand |
Forecast |
Error |
Absolute |
|
3 |
11 |
13.5 |
-2.5 |
2.5 |
|
4 |
9 |
13 |
-4.0 |
4.0 |
|
| n = 6 | 5 |
10 |
10 |
0 |
0.0 |
| observations | 6 |
8 |
9.5 |
-1.5 |
1.5 |
7 |
14 |
9 |
5.0 |
5.0 |
|
8 |
12 |
11 |
1.0 |
1.0 |
|
-2 |
14 |
MFE = -2/6 = -0.33
MAD = 14/6 = 2.33
Conclusion: Model tends to slightly over-forecast, with an average absolute error of 2.33 units.
Tracking Signal
Used to pinpoint forecasting models that need adjustment
Rule of Thumb:
As long as the tracking signal is between –4 and 4, assume the model is working correctly
Other Measures
