Quantitative Analysis for Management, 13/e

5.4 Forecasting Models—Random Variations Only

If all variations in a time series are due to random variations, with no trend, seasonal, or cyclical component, some type of averaging or smoothing model would be appropriate. The averaging techniques in this chapter are moving average, weighted moving average, and exponential smoothing. These methods will smooth out the forecasts and not be too heavily influenced by random variations. However, if there is a trend or seasonal pattern present in the data, then a technique that incorporates that particular component into the forecast should be used.

Moving Averages

Moving averages smooth out variations when forecasting demands that are fairly steady.

Moving averages are useful if we can assume that ...