One of the most common and successful approaches to integrated supply chain management (SCM) is vendor-managed inventory (VMI). One of the technologies that has been widely used recently to share information in the VMI is blockchain technology (BT). Given that many factors, such as scalability and adoption cost, play a role in obtaining the optimal number of transactions, it is of vital importance to account for them in the VMI. Since the VMI strategy aims for a long-term relationship between the vendor and the retailer and highly affects the supply chain’s total cost, the vendor must pay more attention in selecting retailers. Another contribution of this study is to consider the issue of environmental pollution generated by inventory holding, ordering, set-up, and transportation operations, which has not been thoroughly investigated in the existing literature. This study uses a green two-echelon multi-product, multiple-vendor, and multipleretailer supply chain with a hybrid of multi-criteria decision-making (MCDM) methods, and a multi-objective programming under the VMI policy is developed. This paper examines BT for supply chain management by accounting for themost impactful criteria of BT implementation in retailer selection and optimization. For this purpose, this paper applies the Bayesian best–worst approach (BWM) as one of the MCDM techniques. The obtained weights are then plugged into the model as the inputs of the proposed model. Finally, the efficiency of the presented method is verified through a case study with actual data collected from the electronic supply chain.

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