A Web-based DSS Architecture and its Forecasting Core in Supply Chain Management

In a competitive market environment, supply chain management (SCM) has been critical for companies to survive. Demand planning plays an important role in SCM, for it provides accurate demand forecasts which may achieve customer satisfaction by offering benefits such as low inventory level, short lead time, efficient resource allocation, and quick response. To obtain more accurate forecasts, this study presents a web-based Decision Support System (DSS) architecture and its forecasting core. The forecasting core, named Panel Function, contains three modules: Segmentation Module, Forecasting Module, and Coordination Module. The Segmentation Module categorizes customers into three segments: Loyal Customer Segment, Potential Customer Segment, and Switcher Segment. Based on the three segments, the Forecasting Module employs different forecasting and analysis technologies to make an integrated forecast estimate: time-series forecasting to capture the loyal customer demand trend, Bayesian inference to estimate the predicted value of switcher purchase quantity, and questionnaire analysis and brand choice models to unearth potential customers. The results from these three processes are then synthesized to obtain the integrated forecast, which is then used in the Coordination Module as the base of distribution planning, and provides a minimal system-wide total cost solution for all parties in the supply chain. As a whole, this DSS architecture has been shown to provide an efficient mechanism for collaborative demand planning and help create the maximum profit for the supply chain.