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Capacity Allocation for Dynamic Process Improvement with Quality and Demand Considerations

Author

Listed:
  • Suresh Chand

    (Purdue University, West Lafayette, Indiana)

  • Herbert Moskowitz

    (Purdue University, West Lafayette, Indiana)

  • Andreas Novak

    (University of Vienna, Vienna, Austria)

  • Ishpal Rekhi

    (Purdue University, West Lafayette, Indiana)

  • Gerhard Sorger

    (University of Vienna, Vienna, Austria)

Abstract

Management of process improvement activities is an essential part of the manufacturing strategy of a firm to remain globally competitive in the long run. This paper considers a manufacturing environment where process improvement activities require use of the productive capacity of the firm in addition to other investments. Thus the firm must allocate its productive capacity between production activities and improvement activities. The output of production activities is used to meet customer demand. Process improvement activities improve the quality of the output, which in turn leads to lower quality related costs (both internal and external) and possibly lower per-unit production cost. It is assumed that the demand function is downward sloping and that revenue is a concave function of output. A continuous-time, finite-horizon, profit maximization, resource allocation model is developed to find an optimal time path for process improvement activities and production activities. Computational results are provided to study the effect of various problem parameters on the optimal decisions.

Suggested Citation

  • Suresh Chand & Herbert Moskowitz & Andreas Novak & Ishpal Rekhi & Gerhard Sorger, 1996. "Capacity Allocation for Dynamic Process Improvement with Quality and Demand Considerations," Operations Research, INFORMS, vol. 44(6), pages 964-975, December.
    • Handle: RePEc:inm:oropre:v:44:y:1996:i:6:p:964-975
    DOI: 10.1287/opre.44.6.964
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    Citations

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    Cited by:

    1. Voros, Jozsef, 2006. "The dynamics of price, quality and productivity improvement decisions," European Journal of Operational Research, Elsevier, vol. 170(3), pages 809-823, May.
    2. Lu, Lijue & Navas, Jorge, 2021. "Advertising and quality improving strategies in a supply chain when facing potential crises," European Journal of Operational Research, Elsevier, vol. 288(3), pages 839-851.
    3. De Giovanni, Pietro & Zaccour, Georges, 2023. "A survey of dynamic models of product quality," European Journal of Operational Research, Elsevier, vol. 307(3), pages 991-1007.
    4. Fouad El Ouardighi & Gustav Feichtinger & Dieter Grass & Richard F. Hartl & Peter M. Kort, 2016. "Advertising and Quality-Dependent Word-of-Mouth in a Contagion Sales Model," Journal of Optimization Theory and Applications, Springer, vol. 170(1), pages 323-342, July.
    5. Manda, A.B. & Uzsoy, Reha, 2021. "Managing product transitions with learning and congestion effects," International Journal of Production Economics, Elsevier, vol. 239(C).
    6. Yuan, Xuchuan & Brian Hwarng, H., 2023. "Examining the dynamics of reactive capacity allocation through a chaos lens," European Journal of Operational Research, Elsevier, vol. 308(2), pages 912-928.
    7. Voros, Jozsef, 2019. "An analysis of the dynamic price-quality relationship," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1037-1045.
    8. De Giovanni, Pietro, 2011. "Quality improvement vs. advertising support: Which strategy works better for a manufacturer?," European Journal of Operational Research, Elsevier, vol. 208(2), pages 119-130, January.
    9. Vörös, József, 2013. "Multi-period models for analyzing the dynamics of process improvement activities," European Journal of Operational Research, Elsevier, vol. 230(3), pages 615-623.
    10. El Ouardighi, Fouad & Pasin, Federico, 2006. "Quality improvement and goodwill accumulation in a dynamic duopoly," European Journal of Operational Research, Elsevier, vol. 175(2), pages 1021-1032, December.
    11. Voros, Jozsef, 2002. "Product balancing under conditions of quality inflation, cost pressures and growth strategies," European Journal of Operational Research, Elsevier, vol. 141(1), pages 153-166, August.
    12. Fouad El Ouardighi & Gustav Feichtinger & Gila E. Fruchter, 2018. "Accelerating the diffusion of innovations under mixed word of mouth through marketing–operations interaction," Annals of Operations Research, Springer, vol. 264(1), pages 435-458, May.
    13. Yimin Wang & Wendell Gilland & Brian Tomlin, 2010. "Mitigating Supply Risk: Dual Sourcing or Process Improvement?," Manufacturing & Service Operations Management, INFORMS, vol. 12(3), pages 489-510, September.
    14. Carrillo, Janice E. & Franza, Richard M., 2006. "Investing in product development and production capabilities: The crucial linkage between time-to-market and ramp-up time," European Journal of Operational Research, Elsevier, vol. 171(2), pages 536-556, June.
    15. Pietro De Giovanni, 2020. "An optimal control model with defective products and goodwill damages," Annals of Operations Research, Springer, vol. 289(2), pages 419-430, June.
    16. Balázs Szabó & Sándor Danka, 2022. "State aid and the learning‐by‐doing effect on product pricing," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(1), pages 228-240, January.
    17. Fouad El Ouardighi & Konstantin Kogan, 2013. "Dynamic conformance and design quality in a supply chain: an assessment of contracts’ coordinating power," Annals of Operations Research, Springer, vol. 211(1), pages 137-166, December.
    18. Mukherjee, Arka & Carvalho, Margarida & Zaccour, Georges, 2023. "Managing quality and pricing during a product recall: An analysis of pre-crisis, crisis and post-crisis regimes," European Journal of Operational Research, Elsevier, vol. 307(1), pages 406-420.

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