A hybrid framework to prioritize the performance metrics for Blockchain technology adoption in manufacturing industries

Abstract

Purpose Blockchain technology (BCT) can play a vital role in manufacturing industries by providing visibility and real-time transparency. With BCT adoption, manufacturers can achieve higher productivity, better quality, flexibility and cost-effectiveness. The current study aims to prioritize the performance metrics and ranking of enablers that may influence the adoption of BCT in manufacturing industries through a hybrid framework. Design/methodology/approach Through an extensive literature review, 4 major criteria with 26 enablers were identified. Pythagorean fuzzy analytical hierarchy process (AHP) method was used to compute the weights of the enablers and the Pythagorean fuzzy combined compromise solution (Co-Co-So) method was used to prioritize the 17-performance metrics. Sensitivity analysis was then carried out to check the robustness of the developed framework. Findings According to the results, data security enablers were the most significant among the major criteria, followed by technology-oriented enablers, sustainability and human resources and quality-related enablers. Further, the ranking of performance metrics shows that data hacking complaints per year, data storage capacity and number of advanced technologies available for BCT are the top three important performance metrics. Framework robustness was confirmed by sensitivity analysis. Practical implications The developed framework will contribute to understanding and simplifying the BCT implementation process in manufacturing industries to a significant level. Practitioners and managers may use the developed framework to facilitate BCT adoption and evaluate the performance of the manufacturing system. Originality/value This study can be considered as the first attempt to the best of the author’s knowledge as no such hybrid framework combining enablers and performance indicators was developed earlier.