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

Author

Listed:
  • Che-Jung Chang

    (Ningbo University)

  • Liping Yu

    (Ningbo University)

  • Peng Jin

    (Ningbo University)

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.

Suggested Citation

  • Che-Jung Chang & Liping Yu & Peng Jin, 2016. "A mega-trend-diffusion grey forecasting model for short-term manufacturing demand," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(12), pages 1439-1445, December.
    • Handle: RePEc:pal:jorsoc:v:67:y:2016:i:12:d:10.1057_jors.2016.31
    DOI: 10.1057/jors.2016.31
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    References listed on IDEAS

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    1. Lin, Yao-San & Li, Der-Chiang, 2010. "The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems," European Journal of Operational Research, Elsevier, vol. 207(1), pages 121-130, November.
    2. Naiming Xie & Alan Pearman, 2014. "Forecasting energy consumption in China following instigation of an energy-saving policy," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 639-659, November.
    3. Yokuma, J. Thomas & Armstrong, J. Scott, 1995. "Beyond accuracy: Comparison of criteria used to select forecasting methods," International Journal of Forecasting, Elsevier, vol. 11(4), pages 591-597, December.
    4. Che-Jung Chang & Wen-Li Dai & Chien-Chih Chen, 2015. "A novel procedure for multimodel development using the grey silhouette coefficient for small-data-set forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 66(11), pages 1887-1894, November.
    5. Der-Chiang Li & Wen-Chih Chen & Che-Jung Chang & Chien-Chih Chen & I-Hsiang Wen, 2015. "Practical information diffusion techniques to accelerate new product pilot runs," International Journal of Production Research, Taylor & Francis Journals, vol. 53(17), pages 5310-5319, September.
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    Cited by:

    1. Yi-Chung Hu, 2017. "Electricity consumption prediction using a neural-network-based grey forecasting approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1259-1264, October.
    2. Yi-Chung Hu, 2021. "Forecasting tourism demand using fractional grey prediction models with Fourier series," Annals of Operations Research, Springer, vol. 300(2), pages 467-491, May.
    3. Yi-Chung Hu, 2021. "Developing grey prediction with Fourier series using genetic algorithms for tourism demand forecasting," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(1), pages 315-331, February.

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