Analysis of enablers for the digitalization of supply chain using an interpretive structural modelling approach

Abstract

Purpose

Over the years, technology development has rationalized supply chain processes. The demand economy is disrupting every sector causing the supply chain to be more innovative than ever before. The digitalization of the supply chain fulfils this demand. Several technologies such as blockchain, big data analytics, 3D printing, Internet of things (IoT), artificial intelligence (AI), augmented reality (AR), etc. have been innovated in recent years, which expedite the digitalization of the supply chain. The paper aims to analyse the applicability of these technological enablers in the digital transformation of the supply chain and to present an interpretive structural modelling (ISM) model, which presents a sequence in which enablers can be implemented in a sequential manner.

Design/methodology/approach

This paper employed the ISM approach to propose a various levelled model for the enablers of the digital supply chain. The enablers are also classified graphically based on their driving and dependence powers using matrix multiplication cross-impact applied to classification (MICMAC) analysis.

Findings

The study indicates that the enablers “big data analytics”, “IoT”, “blockchain” and “AI” are the most powerful enablers for the digitalization of the supply chain and actualizing these enablers should be a topmost concern for organizations, which want to exploit new opportunities created by these technologies.

Practical implications

This study presents a systematic approach to adopt new technologies for performing various supply chain activities and assists the policymakers better organize their assets and execution endeavours towards digitalization of the supply chain.

Originality/value

This is one of the initial research studies, which has analysed the enablers for the digitalization supply chain using the ISM approach.

Citation

Agrawal, P. and Narain, R. (2023), "Analysis of enablers for the digitalization of supply chain using an interpretive structural modelling approach", International Journal of Productivity and Performance Management, Vol. 72 No. 2, pp. 410-439. https://doi.org/10.1108/IJPPM-09-2020-0481