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Managing production of high-tech products with high production quality variability

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  • Sandra Transchel
  • Saurabh Bansal
  • Mrinmay Deb

Abstract

We consider production systems in technology industries where output quality of a single production run has a large variance. Firms operating such systems classify products into different quality bins and sell units in one bin at the same tagged quality level and the same price. Consumers have heterogeneous quality preferences and choose that quality that maximises their net utility. We examine firms’ assortment, production and pricing problem. We present a three-stage solution procedure that optimises the production quantity, quality specification and number of bins. In that regard, we show that for a manufacturing technology with known quality distribution and known distribution of customers’ quality preference, the optimal assortment and production quantity are set such that on average, the demand of each bin is exactly fulfilled. We examine the impact of an improved manufacturing technology, variation in consumer preferences and changing price premium on the optimal assortment, lot size, market share, yield loss and the overall profitability. We further show that when the quality distribution of the manufacturing process is unknown, downward substitution leads to product offering of higher quality and higher prices. Finally, we discuss practical considerations for pricing, technology and optimal product offerings, and explain the proliferation of bins witnessed in the last decade in the processor industry.

Suggested Citation

  • Sandra Transchel & Saurabh Bansal & Mrinmay Deb, 2016. "Managing production of high-tech products with high production quality variability," International Journal of Production Research, Taylor & Francis Journals, vol. 54(6), pages 1689-1707, March.
  • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:6:p:1689-1707
    DOI: 10.1080/00207543.2015.1053579
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    Cited by:

    1. Ying-Ju Chen & Brian Tomlin & Yimin Wang, 2017. "Dual Coproduct Technologies: Implications for Process Development and Adoption," Manufacturing & Service Operations Management, INFORMS, vol. 19(4), pages 692-712, October.
    2. Meijer, Mirjam S. & van Jaarsveld, Willem & de Kok, Ton & Tang, Christopher S., 2022. "Direct versus indirect penalties for supply contracts in high-tech industry," European Journal of Operational Research, Elsevier, vol. 301(1), pages 203-216.
    3. Zhou, Pin & Xu, He & Wang, Hongwei, 2020. "Value of by-product synergy: A supply chain perspective," European Journal of Operational Research, Elsevier, vol. 285(3), pages 941-954.
    4. Prim, Alexandre Luis & Freitas, Kenyth Alves de & Paiva, Ely Laureano & Kumar, Maneesh, 2023. "The development of quality capabilities in Brazilian breweries: A Co-evolutionary approach," International Journal of Production Economics, Elsevier, vol. 256(C).
    5. Hsieh, Chung-Chi & Lai, Hsing-Hua, 2020. "Pricing and ordering decisions in a supply chain with downward substitution and imperfect process yield," Omega, Elsevier, vol. 95(C).
    6. Yangyang Peng & Xiaolin Xu & Xue Liang & Weili Xue, 2020. "Mismatch risk allocation in a coproduct supply chain," Annals of Operations Research, Springer, vol. 291(1), pages 707-730, August.
    7. Tao Lu & Ying‐Ju Chen & Brian Tomlin & Yimin Wang, 2019. "Selling Co‐Products through a Distributor: The Impact on Product Line Design," Production and Operations Management, Production and Operations Management Society, vol. 28(4), pages 1010-1032, April.

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