A STRATIFIED MARKOVIAN MULTI-PREFERENCE DECISION SUPPORT SYSTEM TO ASSESS SUPPLY CHAIN BLOCKCHAIN PLATFORMS

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 study develops a novel and hybrid decision support system using the concept of stratification and multi-preference group decision making. To analyse blockchain technology platforms for a supply chain network, further enhancements are made to the developed model by utilising the principles of complex system behaviour, target-based normalisation, Markov chains and best-worst method. This research 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 paper 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 study exposes the efficiency of the proposed approach to address such complex problems.