Question

Choose one of the following forecasting methods discussed in this chapter: last-value, averaging, moving-average, or exponential smoothing. Identify the conditions when the method is most appropriate to use and give an example of an application of this method.

Answer 1

Answer-

The exponential smoothing forecasting method takes into account the sales or data from only the most recent months and weighs the data in the last month most heavily while progressively giving smaller weights to the older months. This forecasting method is most appropriate to use when conditions tend to change occasionally, but not extremely rapidly.

Formula:

Forecast = a(Last value) + (1 – a)(Last forecast); Where a = smoothing constant between 0 and 1

Higher values of a mean the forecast is greatly influenced by the last value while smaller values for a mean the forecast is less influenced by the last value.

Example:

This model could be used to forecast the hiring needs for a start-up company that is just beginning to establish itself. It hasn’t experienced any cyclical needs or any rapidly changing trends. Let’s say a start-up company predicted a hiring need of 34 new workers and had an actual demand of 29 workers with a smoothing constant of 0.3. The exponential forecasting method would be used as follows:

New Forecast = (0.3 * 29) + (1- 0.3) (34) = 8.7 + 23.8 = 32.5 workers

Based on this forecast, the predicted hiring need for the following month would be 33 workers.