Deployment of Interpretive Structural Modeling in Barriers to Industry 4.0: A Case of Small and Medium Enterprises

Abstract

Small and medium enterprises (SMEs) are vital contributors and significant drivers of any manufacturing sector. The Industry 4.0 (I 4.0) revolution has made the global economy highly competitive and automated, requiring Indian SMEs to adapt more quickly. Therefore, this study aimed to identify the barriers to implementing I 4.0, simplifying the complex interrelationship among such barriers with the help of a suitable model, categorizing them as independent and dependent ones, and, ultimately, leveling the same drivers, autonomous linkages, and dependent forces. The present investigation thoroughly examined the existing literature and summarized the list of barriers into fifteen significant barriers to the smooth establishment of Industry 4.0 in India. The identified barriers were analyzed with the help of Interpretive Structural Modeling (ISM) Diagraph and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis. This study was able to explore the interrelationship among these barriers. The study has found found a lack of support from stakeholders, and insufficient managerial support emerged as a major factor neglected by Indian SMEs. However, uncertainty in the predicted demand for products, the lack of an alternate solution to the technological breakdown, and doubt about the sustainability of Industry 4.0 (relating to its potential to lead to unemployment in society, etc.) are significant contingent barriers. These barriers can impact the other strategic choices related to the successful implementation of Industry 4.0. This study’s observations can help decision-makers make strategic decisions to manage the barriers affecting Industry 4.0 in Indian SMEs. This research revealed a scope that can be extended to other South Asian and developing nations. The results of the present work can be further studied with structural equation modeling (SEM) and multiple regression analysis (MRA).