Answer to Question 1
The first type of forecast error measure is called the cumulative sum of forecast errors (CFE). It calculates the total forecast error for a set of data, taking into consideration both negative and positive errors. This is also referred to as bias. This gives an overall measure of forecast error. However, taking into consideration both negative and positive errors, this method can produce an overall low error total although individual period forecasts can either be much higher or much lower than actual demand.
The second measure of forecast error is mean squared error (MSE), which squares each period error so the negative and positive errors do not cancel each other out. MSE also provides a good indication of the average error per period over a set of demand data.
The third type is Mean Absolute Deviation (MAD) and is closely related to MSE. By taking the absolute value of each error, the negative and positive signs are removed and a good indication of average error per period is calculated. This measure is popular because it is easy to understand and provides a good indication of the accuracy of the forecast.
The final measure of forecast error is mean absolute percent error (MAPE), and it relates the forecast error to the level of demand so different types of forecasts can be compared.
Answer to Question 2
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