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.

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