| Example |
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| Jan forecast is given as 410 |
0.3 |
| Month |
Mileage |
Forecast |
0.3 |
| Jan |
450 |
410 |
0.3 |
| Feb |
495 |
422.0 |
0.3 |
| Mar |
518 |
443.9 |
0.3 |
| April |
563 |
466.1 |
0.3 |
| May |
584 |
495.2 |
0.3 |
| june |
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Exponential Smoothing Example |
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| Ft |
new forecast |
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| Ft-1 |
previous period’s forecast |
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| a |
smoothing (weighting) constant |
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| At-1 |
previous period actual or demand |
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| At-1 minus Ft-1 is the forecast error |
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| Smoothing constant |
.05 to .5) |
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| higher constant – more weight on recent data |
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| lower constant – more weight on past data |
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| constant of 1 = naïve forecast |
(current forecast equals previous period demand) |
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| Formula: |
Ft=Ft-1+alpha(At-1 – Ft-1) |
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Current Forecast = previous period forecast + alpha (forecast error) |
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Forecast error is the actual demand minus the previous forecast |
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The post Example a
Jan forecast is given as 410 0.3
Month Mileage Forecast 0.3 appeared first on GET HELP WITH PAPERLINQ.
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