Demand forecasts are essential for managing supply chain activities but are difficult to create when collaborative information is absent. Many traditional and advanced forecasting tools are available, but applying them to a large number of customers is not manageable. In our research, we use data mining techniques to identify segments of customers with similar demand behaviors. Historical usage is used to cluster customers with similar demands. Once customer segments are identified, a manageable number of forecasting models can be built to represent the customers within the segments.
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