Equal weights are assigned to all periods in the computation of simple moving average. The weighted moving average assigns more weight to some demand values (usually more recent ones) the Table 2.1 Shows the computation for three months weighted moving average with a weight of 0, 5 assigned to the most recent demand value, a weight of 0, 3 assigned to the next most recent value and a weight of 0, 2 assigned to the oldest of the demand value included in the average
Table 2.1 Three months weighted moving average
| Time | Months (t) | Demand (Dt) | Moving average Forecast (Mt) |
| 1 | 120 | – | – |
| 2 | 130 | 118 | – |
| 3 | 110 | – | – |
| 4 | 140 | 129 | 1 |
| 5 | 110 | 119 | 1 |
| 6 | 130 | 126 | 9 |
Weighted MA3 = 0, 2 * 120 + 0, 3 * 130+o, 5*110 =110
0, 2+0, 3+0, 5
Weight MAi = $WtDt
$Wt
Where I =1, 2, 3 if we use these periods moving average, i=3 corresponds to the most recent times period and i=1 correspond to oldest time period Wt=Weight for the time period t In the example, Wi=0, 2 W2=0, 3 and so on
An advantage of this model is that it allows you to compensate for some trend in seasonality. If you want to, you can weight recent months more heavily and still dampen somewhat the effect of noise by placing small weightings on older demands. Of course the modeler or manager still has to choose the coefficients and this choice is critical to model success or failure.
