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Most organizational decisions are based upon a forecast. This is especially true in inventory control and scheduling, where forecasts are frequently generated for hundreds of items on a regular basis. Much of this process relies on computerized forecasts providing the advantage of computational efficiency and ease of use. Forecast accuracy is typically judged by standard forecast-error measures provided by most computer packages. The challenge for managers is to make good decisions based upon these forecasts and be able to expediently evaluate forecast performance.
Unfortunately, there is little consensus among forecasters as to the best and most reliable forecast error measures [1]. Complicating the issue is that different error measures often provide conflicting results [5]. Knowing which forecast-error measure to rely on can be difficult, yet extremely important. Different forecast-error measures provide unique information to the manager and each have their shortcomings. Knowing when to rely on which measure can be highly beneficial.
UNDERSTANDING FORECAST ERROR MEASURES
Common Forecast Error Measures Most forecast-error measures can be divided into two groups: standard and relative error measures [4]. Listed below are some of the more common forecast-error measures in these categories. Specific suggestions with regard to their use follow.
If Xt is the actual value for time period t and Ft is the forecast error for the period t, the forecast error for that period is the difference between the actual and the forecast:
et = Xt - Ft When evaluating performance for multiple observations, say n, there will be n error terms. We can define the following standard forecast-error measures. Standard versus Relative Forecast-Error Measures Standard error measures, such as mean error (ME) or mean square error (MSE), typically provide the error in the same units as the data. As such, the true magnitude of the error can be difficult for managers to comprehend. For example, a forecast error of 50 units has a completely different level of gravity if the actual value for that period was 500 versus 100 units. Also, a forecast error of $50 is vastly different from a forecast error of 50 cartons. In addition, having the error in actual units of measurement makes it difficult to compare accuracies across time series or different periods of time. In inventory control, for example,...