A Case-Based Reasoning System for Individual Demand Forecasting

In recent years, much attention has been focused by practitioners as well as academics on a new manufacturing paradigm called ´instant customerisation´ (IC). One of critical operation tactics is finalise-to-individual demand forecasting to realize IC. It attaches more importance to individual demand forecasting, especially short-term forecasts. However, there is scarcely any relevant research. According to the character of individual demand, an oriented-attribute forecasting approach is proposed. We forecast individual demand indirectly after transforming it into a time series. Then we propose a Case-based Reasoning (CBR) system for universal individual demand forecasting. The forecast process is divided into three stages: case representation, similarity search, and case forecasting and adjustment. In particular, we proposed a distance-based formula that broadens our view by regarding the slope and length of a segment as changing characteristics of a time series. The performance of our system is evaluated using real data from a restaurant. The proposed approach is compared with other traditional time series forecasting methods. Because of particularity of the data, a special error measure, MASE, is employed. The empirical results show that our method is able to produce a forecast with a high degree of accuracy.