Dynamic Data Mining for Improved Forecasting in Logistics and Supply Chain Management

Abstract

Supply Chain Management relies heavily on forecasts, e.g. of futuredemand or future prices. Most applications, however, use staticforecasting models in the sense that past data is used for modelconstruction and evaluation without being updated adequately when newdata becomes available. We propose a dynamic forecasting methodology andshow its effectiveness in a real-world application.