This method uses the Moving Average formula to average the specified number of periods to project the next period. You should recalculate it often (monthly, or at least quarterly) to reflect changing demand level.
To forecast demand, this method requires the number of periods best fit plus the number of periods of sales order history. This method is useful to forecast demand for mature products without a trend.
Moving Average (MA) is a popular method for averaging the results of recent sales history to determine a projection for the short term. The MA forecast method lags behind trends. Forecast bias and systematic errors occur when the product sales history exhibits strong trend or seasonal patterns. This method works better for short range forecasts of mature products than for products that are in the growth or obsolescence stages of the life cycle.
The Weighted Moving Average formula is similar to Method 4, Moving Average formula, because it averages the previous month’s sales history to project the next month’s sales history. However, with this formula you can assign weights for each of the prior periods.
This method requires the number of weighted periods selected plus the number of periods best fit data. Similar to Moving Average, this method lags behind demand trends, so this method is not recommended for products with strong trends or seasonality. This method is useful to forecast demand for mature products with demand that is relatively level.
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.
