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 first-period demand is dropped and the latest-period 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 + Dt-1 + Dt-2 + ... + Dt-N+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 short-term fluctuations

Example

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