A mega-trend-diffusion grey forecasting model for short-term manufacturing demand. Issue 12 (December 2016)

• Record Type: Journal Article
• Authors: Chang, Che-Jung; Yu, Liping; Jin, Peng
• Is Part Of: Journal of the Operational Research Society. Volume 67:Issue 12(2016)
• Page Start: 1439
• Page End: 1445
• Publication Date: 2016-12
• Subjects: small data set -- grey theory -- forecasting -- short-term demand -- process data; Operations research -- Periodicals; 658.4034
• DOI: 10.1057/jors.2016.31
• Languages: English
• ISSNs: 0160-5682

Abstract

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.