obtained would lag behind this trend. Small value of ‘n’ should be taken when demand of product is not stable (i.e. there is a component of trend in demand time series or the demand pattern is changing). Small value of ‘n’ gives a more oscillating response (less smooth) but there is a closer following of the trend by the forecast (e.g. if the value of ‘n’ is 1, it means only one past period i.e. most recent demand value is taken and it is the forecast for future). Thus, if the demand time series is stable, select a high value of ‘n’. If the demand time series shows a trend, a low value of ‘n’ should be considered. The effect of high and low value of ‘n’ is shown for a time series with trend in the following figure.
Effect of Sample Size on Moving Average Forecast
The graph in Figure 2.11 shows actual demand of product over a period of time, the forecast calculated using a three month average (n = 3) and the forecast using a nine month moving average (n = 3). It can be noticed that forecast based on nine month moving average gives a smoother output (as it eliminates random variations) but lag behinds the actual demand ( of
76