A Stratified Markovian Multipreference Decision Support System to Assess Supply Chain Blockchain Platforms

Abstract

To cope with the advancements in blockchain technologies, novel platforms are rapidly evolving. This creates new business and financial opportunities for supply chain networks. Despite the extensive literature on blockchain technologies, few studies have focused on selecting the most suitable platforms for supply chain networks. Furthermore, such decisions may be influenced by the occurrence of future events causing system dynamics. The literature also fails to integrate uncertainty related to such system dynamics into this decision-making process. To address this gap, this article develops a novel and hybrid decision support system using the concept of stratification and multipreference group decision-making. To analyze blockchain technology platforms for a supply chain network, further enhancements are made to the developed model by utilizing the principles of complex system behavior, target-based normalization, Markov chains, and best–worst method. This article is the first to examine how such methods can work together to integrate dynamics of a complex system into the decision-making process. Moreover, the article analyses a supply chain network blockchain platform technology with complex systems transitions. To validate the reliability of the method, a real-world problem is addressed, which is a blockchain platform technology selection problem in one of largest multinational and professional services networks in New Zealand. The article exposes the efficiency of the proposed approach to address such complex problems.