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An empirical study of demand forecasting of non-volatile memory for smart production of semiconductor manufacturing

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  • Ying-Jen Chen
  • Chen-Fu Chien

Abstract

As high-speed computing is crucial to empower intelligent manufacturing for Industry 4.0, non-volatile memory (NVM) is critical semiconductor component of the cloud and data centre for the infrastructures. The NVM manufacturing is capital intensive, in which capacity utilisation significantly affects the capital effectiveness and profitability of semiconductor companies. Since capacity migration and expansion involve long lead times, demand forecasting plays a critical role for smart production of NVM manufacturers for revenue management. However, the shortening product life cycles of integrated circuits (IC), the fluctuations of semiconductor supply chains, and uncertainty involved in demand forecasting make the present problem increasingly difficult in the consumer electronics era. Focusing on the realistic needs of NVM demand forecasting, this study aims to develop a decision framework that integrates an improved technology diffusion model and a proposed adjustment mechanism to incorporate domain insights. An empirical study was conducted in a leading semiconductor company for validation. A comparison of alternative approaches is also provided. The results have shown the practical viability of the proposed approach.

Suggested Citation

  • Ying-Jen Chen & Chen-Fu Chien, 2018. "An empirical study of demand forecasting of non-volatile memory for smart production of semiconductor manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 56(13), pages 4629-4643, July.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:13:p:4629-4643
    DOI: 10.1080/00207543.2017.1421783
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    Cited by:

    1. Omar, Yamila M. & Minoufekr, Meysam & Plapper, Peter, 2019. "Business analytics in manufacturing: Current trends, challenges and pathway to market leadership," Operations Research Perspectives, Elsevier, vol. 6(C).
    2. Hsieh, Chung-Chi & Lathifah, Artya, 2022. "Ordering and waste reuse decisions in a make-to-order system under demand uncertainty," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1290-1303.
    3. Shui Ming Li & Felix T. S. Chan & Yung Po Tsang & Hoi Yan Lam, 2021. "New Product Idea Selection in the Fuzzy Front End of Innovation: A Fuzzy Best-Worst Method and Group Decision-Making Process," Mathematics, MDPI, vol. 9(4), pages 1-18, February.
    4. Wang, Mengyue & Huang, Hongxuan, 2019. "The design of a flexible capital-constrained global supply chain by integrating operational and financial strategies," Omega, Elsevier, vol. 88(C), pages 40-62.
    5. Marketa Kubickova, 2022. "Revenue management in manufacturing: systematic review of literature," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 21(2), pages 147-152, April.

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