Entropy as a Measurement for the Quality of Demand Forecasting
Production planning and control is a highly complex process influenced by many factors. An important part of this broad task is demand forecasting, for which many methods already have been developed. But due to the occurring dynamics in the used data, the prediction may differ strongly from the optimum and thus errors leading to rising costs are inevitable. In this paper we will propose the entropy as a measurement for the quality of order forecasting respectively as relative estimation for the forecasting error. In general, entropy is a measurement for disorder and thus also for information content. Since lack of information leads to inaccuracy of forecasting, the entropy can be identified with the quality of order prediction. First results on the basis of time-series obtained from mathematical functions, discrete-event simulations of a production network scenario and a real shop-floor system will show the successful transfer of this method.
KeywordsForecast Error Word Length Forecast Method Demand Forecast Symbol Sequence
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