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Sustainable Quality Management Based on Metrological Sampling Scheme Design: A Case Study of Food Processor

Author

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  • Mingquan Wang

    (School of Mathematical Sciences, Nanjing Normal University, Nanjing 210023, China)

  • Fengping Yang

    (School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Bin Zhang

    (School of Mathematics and Statistics, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Zhisong Chen

    (Business School, Nanjing Normal University, Nanjing 210023, China
    Stern School of Business, New York University, 44 West Fourth Street, New York, NY 10012, USA)

Abstract

The optimization of the sampling scheme is particularly important in order to achieve sustainability in quality management. This paper discusses the problem of optimizing the design of metrological sampling scheme when the mean is a random variable. Assuming that the prior distribution of product means is known, a Bayesian posterior probability is calculated by using the likelihood function with decision variables to measure the sampling risk, and the expected cost of the sampling scheme is calculated based on the protection of producer and user risk in combination with the Taguchi quality loss function. The influence of model parameters on the selection of the optimal sampling scheme is investigated through sensitivity analysis. The model constructed in this paper solves the problem of sampling design in the case of food processing enterprises, quantifies the quality loss of products in the sampling process, facilitates sustainable quality management of enterprises, and has important theoretical significance and application value for sustainable business management of food processing enterprises.

Suggested Citation

  • Mingquan Wang & Fengping Yang & Bin Zhang & Zhisong Chen, 2023. "Sustainable Quality Management Based on Metrological Sampling Scheme Design: A Case Study of Food Processor," Sustainability, MDPI, vol. 15(6), pages 1-13, March.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:6:p:5283-:d:1099108
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    References listed on IDEAS

    as
    1. Allen, D.E. & Powell, R.J. & Singh, A.K., 2016. "Take it to the limit: Innovative CVaR applications to extreme credit risk measurement," European Journal of Operational Research, Elsevier, vol. 249(2), pages 465-475.
    2. Lam, Yeh & Li, Kim-Hung & Ip, Wai-Cheung & Wong, Heung, 2006. "Sequential variable sampling plan for normal distribution," European Journal of Operational Research, Elsevier, vol. 172(1), pages 127-145, July.
    3. Gumataw Kifle Abebe, 2020. "Effects of institutional pressures on the governance of food safety in emerging food supply chains: a case of Lebanese food processors," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 37(4), pages 1125-1138, December.
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