Interpretive Structural Modelling to assess the enablers of blockchain technology in supply chain

Abstract

The purpose of this paper is to identify, analyze the enablers for blockchain technology in supply chain. We have proposed Interpretive Structural Modeling (ISM) to analyze the different types of enablers like autonomous enablers, dependent enablers, linkage enablers and driver enablers from SSIM (Structural Self-Interaction Matrix) in MATLAB software by using partition level and iterations for the prioritization of enablers. We have prioritized the different enablers and proposed cluster diagram of enablers for blockchain technology in supply chain. On the basis of our analysis, we have formed five clusters and found that traceability transparency, seamless connectivity, verifiability of transaction enablers are highly driven and dependent on the other input variables included in the supply chain system. The present work suggested the platform for both academicians and researchers to understand the relationship between enablers of blockchain technology in supply chain. This paper also provides the future direction to the practitioners for optimally assign the efforts and available resources to increase the current performance of supply chain system. This article prioritizes the enablers of block chain in clusters according to their level of impact.