Moving averages
One simple way to forecast is to take the average demand for the last three or six periods and use that figure as the forecast for the next period. At the end of the next period, the firstperiod demand is dropped and the latestperiod demand added to determine a new average to be used as a forecast. If a longer period is used, the forecast does not react as quickly. The fewer months included in the moving average, the more weight is given to the latest information and the faster the forecast reacts to trends. However, the forecast will always lag behind a trend.
Moving averages are best used for forecasting products with stable demand where there is a little trend or seasonality. They are also useful to filter out random fluctuation. One drawback to using moving averages is the need to retain several
periods of history for each item to be forecast.
Simple moving average: At = (Dt + Dt1 + Dt2 + ... + DtN+1 )/N
where N = total number of periods in the average forecast for period t+1: Ft+1 = At
Key Decision: N  How many periods should be considered in the forecast
Higher value of N  greater smoothing, lower responsiveness
Lower value of N  less smoothing, more responsiveness
The more periods (N) over which the moving average is calculated, the less susceptible the forecast is to random variations, but the less responsive it is to changes
A large value of N is appropriate if the underlying pattern of demand is stable
A smaller value of N is appropriate if the underlying pattern is changing or if it is important to identify shortterm fluctuations
Example
January 92 July 84 February 83 August 81 March 66 September 75 April 74 October 63 May 75 November 91 June 84 December 84


period, the firstperiod demand is dropped and the latestperiod demand added to determine a new average to be used as a forecast. This forecast would always be based on the average of the actual demand over the specified period.

For example, suppose it was decided to use a threemonth moving average on the data shown in Figure 8.4. Our forecast for January, based on the demand in October, November, and December, would be:
63 + 91 + 84 3 

Now suppose that January demand turned out to be 90 instead of 79. The forecast for February would be calculated as:
91 + 84 + 90 3 
Weighted moving average
A weighted moving average adjusts the moving average method to reflect fluctuations more closely by assigning weights to the most recent data, meaning, that the older data is usually less important. The weights are based on intuition and lie between 0 and 1 for a total of 1.0