Exam 7: Demand Management

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Two types of demand exist: (1) independent demand, which is the demand for the primary item, and (2) dependent demand, which is directly influenced by the demand for the independent item. For example, the demand for bicycles would be called independent. It is the demand for the primary, or finished, product and is directly created by the customer. The demand for bicycle tires would be called dependent, because the number of tires demanded is determined by the number of bicycles demanded. Most forecasting techniques focus on independent demand. For example, a bicycle manufacturer will forecast the demand for bicycles during a given period. Given that level of demand, the manufacturer knows that two tires will be required for each bicycle demanded. As such, there is no need for the bicycle manufacturer to forecast the demand for tires. From a different perspective, the tire manufacturer will need to forecast the demand for tires, because these are its independent demand items. However, the tire manufacturer will not need to forecast the demand for rims since each tire requires one rim. So, each organization in a particular supply chain will have different definitions for independent and dependent demand items. Forecasting, however, will still usually be done at the independent demand item level.

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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.