Grey Models


Journal of the Operational Research Society
December 2016, Volume 67, Issue 12, pp 1439–1445

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

Che-Jung Chang, Liping Yu, Peng Jin

This note introduces the concepts of this paper. To see the full paper, with the development of the model, go to the JORS edition, reference above. That is available to members only.

A grey model is one where there is only partial knowledge of model structure, and little data. In many cases a simple structure is chosen, with few data points. This can in some cases, be more useful than a fully structured model with lots of data. This case study demonstrates its advantages.

Accurate short-term demand forecasting is critical for developing effective production plans; however, if a short forecasting period indicates that the product demands are unstable, 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.

Uncertainty, which often exists in planning, is an unresolved problem for these managers during project drafting. Forecasting can ease uncertainty by providing information on future consumer demands, thereby supporting managers in developing an effective project plan. In production management, forecasting is fundamental in the decision-making process, and an accurate forecast can assist enterprises in comprehensively understanding market demands. When production rates are coordinated with market demand, balanced supply and demand enables effective operation management.

In production management, forecasting short-term demand is vital for preparing the daily production schedule. When the forecasting period is short, the variations in demand are typically high. Because of instabilities in the demand trends, the developing patterns of demand cannot be easily identified from a large amount of historical data. A forecasting model that is based on recent samples featuring the latest information can reflect the prevailing conditions precisely and therefore can be used to obtain accurate forecasts. An accurate short-term demand forecast not only enhances management efficiency, but also enables enterprises to maintain their competitive advantages.

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