Model development for assessing inhibitors impacting Industry 4.0 implementation in Indian manufacturing industries: an integrated ISM-Fuzzy MICMAC approach

Rimalini Gadekar (), Bijan Sarkar () and Ashish Gadekar ()
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Rimalini Gadekar: Government Polytechnic
Bijan Sarkar: Jadavpur University Kolkata
Ashish Gadekar: Amity Institute of Higher Education

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 2, No 7, 646-671

Abstract: Abstract Industry 4.0 (I4.0) adoption is becoming predominant in manufacturing industries due to its limitless opportunities. Even though companies are interested in adopting digitalization, several perceived barriers stymied them. However, in the interest of its smooth adoption, these perceived barriers must be addressed urgently. This research aims to analyze the broader spectrum of possible barriers that impede the implementation of I4.0 and converge them into the most prominent inhibitors, further assessing these inhibitors to develop contextual relationships among them. A comprehensive literature review and an empirical research-based survey considering a large sample size are used to address the study’s research objectives. Industry and academia experts’ inputs are considered to derive the I4.0 implementation barrier’s current prominence. The interrelationship among extracted twelve significant inhibitors through principle component analysis (PCA) is modeled using interpretive structural modeling (ISM) to manifest each inhibitor’s direct and indirect effect. Fuzzy matriced’ impacts croise’s multiplication applique’e a’ un classement (MICMAC) analysis is further considered to classify these inhibitors into drivers and dependents. The study depicts inadequate organizational strategies, uncertainty about financial decision making, limited employee readiness, inconsistent legal and government policies, Insufficient IT and automation infrastructure as the most prominent driver inhibitors of the I4.0 adoption. An integrated novel PCA-ISM Fuzzy MICMAC model developed in this research paper is unique and used for the first time to establish the hierarchical relationship among I4.0 implementation inhibitors considering the post-COVID-19 scenario. This study offers practical insights and outcomes that will help researchers, decision-makers, and practitioners in unlocking the potential of I4.0 by dealing with its inhibitors efficaciously.

Keywords: Industry 4.0; Sustainability; Inhibitors; Principal component analysis; Interpretive structural modeling; Fuzzy MICMAC; COVID-19 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s13198-022-01691-5

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