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Models for Measuring and Accounting for Cost of Conformance Quality

Author

Listed:
  • P. Nandakumar

    (Fuqua School of Business, Duke University, Durham, North Carolina 27706)

  • S. M. Datar

    (Graduate School of Business, Stanford University, Stanford, California 94305)

  • R. Akella

    (Graduate School of Industrial Administration, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

We build a model to measure and account for the cost of quality. We incorporate the impact of quality on lead time variance and on service reliability and demand. Quality costs are a joint and nonlinear function of various parameters of the manufacturing process. Our model shows that it may not be optimal for quality improvement efforts to target products that have the highest defective levels, largest direct costs or consume the maximum capital resources. A stochastic dynamic programming model is developed to evaluate the optimal trade-offs across prevention and appraisal costs, and the costs of failure.

Suggested Citation

  • P. Nandakumar & S. M. Datar & R. Akella, 1993. "Models for Measuring and Accounting for Cost of Conformance Quality," Management Science, INFORMS, vol. 39(1), pages 1-16, January.
  • Handle: RePEc:inm:ormnsc:v:39:y:1993:i:1:p:1-16
    DOI: 10.1287/mnsc.39.1.1
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    Citations

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    Cited by:

    1. Paulson Gjerde, Kathy A. & Slotnick, Susan A., 2004. "Quality and reputation: The effects of external and internal factors over time," International Journal of Production Economics, Elsevier, vol. 89(1), pages 1-20, May.
    2. Donald E. Harter & Mayuram S. Krishnan & Sandra A. Slaughter, 2000. "Effects of Process Maturity on Quality, Cycle Time, and Effort in Software Product Development," Management Science, INFORMS, vol. 46(4), pages 451-466, April.
    3. Lin, Z. Jun & Johnson, Stev, 2004. "An exploratory study on accounting for quality management in China," Journal of Business Research, Elsevier, vol. 57(6), pages 620-632, June.
    4. Clemons, Rebecca & Slotnick, Susan A., 2016. "The effect of supply-chain disruption, quality and knowledge transfer on firm strategy," International Journal of Production Economics, Elsevier, vol. 178(C), pages 169-186.
    5. Yue, Jinfeng & Wang, Min-Chiang & Chen, Bintong, 2007. "Mean-range based distribution-free procedures to minimize "overage" and "underage" costs," European Journal of Operational Research, Elsevier, vol. 176(2), pages 1103-1116, January.
    6. Meng Zhang & Yongxi Yi & Chunyan Fu & Yuqiong Li, 2023. "Green supply chain coordination model under environmental impact and conformance quality sensitive consumer demand," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(3), pages 1410-1435, April.
    7. So, Kut C. & Song, Jing-Sheng, 1998. "Price, delivery time guarantees and capacity selection," European Journal of Operational Research, Elsevier, vol. 111(1), pages 28-49, November.
    8. Wright, CM & Mehrez, A, 1998. "An Overview of Representative Research of the Relationships Between Quality and Inventory," Omega, Elsevier, vol. 26(1), pages 29-47, February.
    9. Kaijie Zhu & Rachel Q. Zhang & Fugee Tsung, 2007. "Pushing Quality Improvement Along Supply Chains," Management Science, INFORMS, vol. 53(3), pages 421-436, March.
    10. Rath, Sambit Brata & Basu, Preetam & Mandal, Prasenjit & Paul, Samit, 2021. "Financing models for an online seller with performance risk in an E-commerce marketplace," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 155(C).
    11. Quigley, John & Walls, Lesley & Demirel, Güven & MacCarthy, Bart L. & Parsa, Mahdi, 2018. "Supplier quality improvement: The value of information under uncertainty," European Journal of Operational Research, Elsevier, vol. 264(3), pages 932-947.
    12. Wiersma, Eelke, 2008. "An exploratory study of relative and incremental information content of two non-financial performance measures: Field study evidence on absence frequency and on-time delivery," Accounting, Organizations and Society, Elsevier, vol. 33(2-3), pages 249-265.

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