A mega-trend-diffusion grey forecasting model for short-term manufacturing demand

Abstract

Accurate short-term demand forecasting is critical for developing effective production plans; however, a short forecasting period indicates that the product demands are unstable, rendering tracking of product development trends difficult. Determining the actual developing data patterns by using forecasting models generated using historical observations is difficult, and the forecasting performance of such models is unfavourable, whereas using the latest limited data for forecasting can improve management efficiency and maintain the competitive advantages of an enterprise. To solve forecasting problems related to a small data set, this study applied an adaptive grey model for forecasting short-term manufacturing demand. Experiments involving the monthly demand data for thin film transistor liquid crystal display panels and wafer-level chip-scale packaging process data showed that the proposed grey model produced favourable forecasting results, indicating its appropriateness as a short-term forecasting tool for small data sets.

Keywords:

• small data set
• grey theory
• forecasting
• short-term demand
• process data

Acknowledgements

This research is partially sponsored by K. C. Wong Magna Fund in Ningbo University; Natural Science Foundation of Zhejiang Province (China) under grant LY16G010002; Ningbo University under grant XKW15D201.