An ISM-MICMAC approach for analyzing dependencies among barriers of supply chain digitalization

Abstract

Purpose Supply chain digitalization (SCD) facilitates a competitive advantage and is equally challenging. A literature analysis reveals that research related to SCD is available, but analysis of key barriers and their interrelations for effective SCD is limited. This paper aims to identify and find out interrelationships among the key barriers affecting SCD in the electronic supply chain (SC). Design/methodology/approach Interpretive structural modeling (ISM) is used to identify the contextual relationship among the SCD barriers. The model is validated using MICMAC analysis. Findings The findings of this study have implications at both theoretical and practical levels. The proposed ISM model can help organizations analyze and interpret interrelationships among barriers to SCD. It can lead to better insights and understanding for minimizing the dominant barriers to effective SCD. Originality/value This study tries to bridge the gap in identifying and finding the interrelationships among the key barriers to effective SCD in an electronic SC. The result assists practicing managers and academicians in effective decision-making on the SCD process.

Fostering supply chain agility by prominent enablers’ identification and developing conceptual modeling based on the ISM-MICMAC approach

“…The critical factor falls into either an independent or a linkage factor classification ( Ali et al ., 2020). In terms of their driving and dependence power, the four clusters of variables identified are as follows (Ali et al , 2020; Azadnia et al , 2021; Deepu and Ravi, 2023): Independent variables – These variables are strong in driving power but weak in dependence power. Dependent variables – These variables are weak in driving power but strong in dependence power. Linkage variables – These variables are strong in both driving and dependence power. Any actions taken by these variables can affect other factors as well as generate a feedback effect.…”

“…In the literature on supply chain management, many researchers used ISM-MICMAC in their studies. For example, Deepu and Ravi (2023) investigated the dependencies among barriers to supply chain digitalization with the help of the ISM-MICMAC approach. Singh et al (2019) investigate the barriers to Lean Six Sigma by modeling ISM and MICMAC.…”

“…The critical factor falls into either an independent or a linkage factor classification (Ali et al, 2020). In terms of their driving and dependence power, the four clusters of variables identified are as follows (Ali et al, 2020;Azadnia et al, 2021;Deepu and Ravi, 2023):…”

“…Linkage enablers are depicted in Quadrant III. These enablers are generally deemed volatile, and any actions involving alteration to these enablers will presumably lead to an equivalent reaction, affecting itself and other enablers (Deepu and Ravi, 2023). Minimizing resistance to change (E1), trust development (E2), external process connectivity (E4), internal process connectivity (E5), supply and demand side competence (E6), supply chain risk management (E7), supply chain flexibility (E8), supply chain integration (E9), supply chain orientation (E10), strategic flexibility (E11), logistics and distribution competency (E13), strategic sourcing (E16) and market sensitivity (E17) fall under this category.…”

The analysis of critical success factors for successful kaizen implementation during the COVID-19 pandemic: a textile industry case study

“…The MICMAC analysis is carried out depending on the powers of driving and dependent of the variables. In accordance with their relative powers, the variables will be divided into four groups (Deepu & Ravi, 2023). These four clusters are identified in the system: autonomous, dependent, linkage, and independent factors (see Figure 3).…”