Example: Method 9: Weighted Moving Average

The Weighted Moving Average (WMA) method is similar to Method 4, Moving Average (MA). However, you can assign unequal weights to the historical data when using WMA. The method calculates a weighted average of recent sales history to arrive at a projection for the short term. More recent data is usually assigned a greater weight than older data, so WMA is more responsive to shifts in the level of sales. However, forecast bias and systematic errors occur when the product sales history exhibits strong trends or seasonal patterns. This method works better for short range forecasts of mature products than for products in the growth or obsolescence stages of the life cycle.

Forecast specifications:

• The number of periods of sales history (n) to use in the forecast calculation.

  For example, specify n = 4 in the processing option to use the most recent four periods as the basis for the projection into the next time period. A large value for n (such as 12) requires more sales history. Such a value results in a stable forecast, but it is slow to recognize shifts in the level of sales. Conversely, a small value for n (such as 3) responds more quickly to shifts in the level of sales, but the forecast might fluctuate so widely that production cannot respond to the variations.

  Note: The total number of periods for the processing option “14 - periods to include" should not exceed 12 months.

• The weight that is assigned to each of the historical data periods.

  The assigned weights must total 1.00. For example, when n = 4, assign weights of 0.50, 0.25, 0.15, and 0.10 with the most recent data receiving the greatest weight.

Minimum required sales history: n plus the number of time periods that are required for evaluating the forecast performance (periods of best fit).

This table is history used in the forecast calculation:

Past Year	Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
1	None	None	None	None	None	None	None	None	131	114	119	137

This is the calculation of Moving Average, given n = 4:

[(131 × 0.10) + (114 × 0.15) + (119 × 0.25) + (137 × 0.50)] / (0.10 + 0.15 + 0.25 + 0.50) = 128.45 rounded to 128

This is the Weighted Moving Average forecast for next year, given n = 4:

Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
128	128	128	129	129	129	129	129	129	129	129	129

January forecast equals [(131 × 0.10) + (114 × 0.15) + (119 × 0.25) + (137 × 0.50)] / (0.10 + 0.15+ 0.25 + 0.50) = 128.45 rounded to 128.

February forecast equals [(114 × 0.10) + (119 × 0.15) + (137 × 0.25) + (128 × 0.50)] / 1 = 127.5 rounded to 128.

March forecast equals [(119 × 0.10) + (137 × 0.15) + (128 × 0.25) + (128 × 0.50)] / 1 = 128.45 rounded to 128.