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An integrated Bayesian best–worst method and Taguchi loss function for measuring quality in supply chain management

Author

Listed:
  • Azam Modares

    (Ferdowsi University of Mashhad)

  • Nasser Motahari Farimani

    (Ferdowsi University of Mashhad)

  • Kimia Abdari

    (Ferdowsi University of Mashhad)

Abstract

Product quality and reliability play a critical role in the profitability of supply chains, as consumers are more inclined to purchase from suppliers offering higher-quality and more reliable products. Given the direct impact of quality on product pricing, a quantitative assessment of quality is essential. This study proposes an integrated approach that combines the Bayesian Best–worst method (BWM) with a hybrid quality function based on the Taguchi loss function and fuzzy logic. Since different quality attributes hold varying levels of importance, the BWM is used to prioritize them based on expert opinions. A quality function is then defined for each attribute. These scores are used to compute a final product quality index. To ensure data integrity, traceability, and transparency in quality evaluations, blockchain technology is incorporated into the framework. The quality-based price is then included in a mathematical model aimed at optimizing supplier order quantities. Results show that the quality scores for milk, cream, yogurt, and cheese were 0.76, 0.78, 0.87, and 0.85, respectively. Sensitivity analysis further demonstrated that price fluctuations, driven by quality variations, significantly influence profit outcomes.

Suggested Citation

  • Azam Modares & Nasser Motahari Farimani & Kimia Abdari, 2025. "An integrated Bayesian best–worst method and Taguchi loss function for measuring quality in supply chain management," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 16(11), pages 3758-3783, November.
    • Handle: RePEc:spr:ijsaem:v:16:y:2025:i:11:d:10.1007_s13198-025-02900-7
    DOI: 10.1007/s13198-025-02900-7
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