Example: Method 4: Moving Average

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.

Forecast specifications: n equals the number of periods of sales history 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. It results in a stable forecast, but is slow to recognize shifts in the level of sales. Conversely, a small value for n (such as 3) is quicker to respond to shifts in the level of sales, but the forecast might fluctuate so widely that production cannot respond to the variations.

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

Calculation of Moving Average, given n = 4

(131 + 114 + 119 + 137) / 4 = 125.25 rounded to 125.

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

Jan	Feb	Mar	Apr	May	Jun	Jul	Aug	Sep	Oct	Nov	Dec
125	124	126	128	126	126	127	127	126	126	126	126

January forecast equals (131 + 114 + 119 + 137) / 4 = 125.25 rounded to 125.

February forecast equals (114 + 119 + 137 + 125) / 4 = 123.75 rounded to 124.

March forecast equals (119 + 137 + 125 + 124) / 4 = 126.25 rounded to 126.