Demand Forecasting of Short Life span Products: Issues, Challenges, and use of Soft Computing techniques

We briefly review forecasting features of typical data mining software, and we give the salient features of SIMForecaster, the existing forecasting system developed at Singapore Institute of Manufacturing Technology, Singapore. SIMForecaster has successfully been used for many important forecasting problems in industry. Demand forecasting of Short life span products involves unique issues and challenges that cannot be fully tackled in existing software systems like SIMForecaster. To introduce there problems, we give three case studies for short life span products, and identify the issues and problems for demand forecasting of short life span products. We identify specific soft computing techniques, namely (i) Small World Theory, (ii) Memes Theory, (iii) Neural networks with special structures, such as (a) binary neural networks (BNNs), (b) bidirectional segmented memory (BSM) recurrent neural networks, and, (c) Long-Short-Term-Memory (LSTM) networks for solving these problems. We suggest that, in addition to these neural network techniques, integrated demand forecasting system for handling optimization problems involved in short life span products would also need some techniques in evolutionary computing as well as genetic algorithms.