Analysis of the driving and dependence power of barriers to adopt industry 4.0 in Indian manufacturing industry
By Sachin S. Kamble, Angappa Gunasekaran, Rohit Sharma. Computers in industry (Print), 2018
610 citations recorded by the source corpus.
Abstract The aim of this paper is the analysis of potential barriers which would hinder the manufacturing organizations from embracing Industry 4.0. This paper establishes relationships among the barriers using interpretive structural modeling (ISM) and finds out driving and dependence power of barriers, using fuzzy MICMAC (Matriced’ Impacts Croise´s Multiplication Applique´e a´ un Classement) analysis. A group of experts from industry and academia was consulted and ISM methodology was used to develop the contextual relationship among the identified barriers to Industry 4.0 adoption. The results of ISM were used as input to fuzzy MICMAC analysis for determining the driving and dependence power of the Industry 4.0 adoption barriers. The results are helpful in identifying and classifying the significant barriers, revealing the direct and indirect effects of each identified barrier on the Industry 4.0 adoption. The findings will help the practitioners and the policymakers for a detailed understanding of the Industry 4.0 adoption process and the barriers hindering its implementation. They may utilize the framework and the driving-dependence power of the barriers to understanding the inter-relationship between the barriers to building a valid and operative digital manufacturing platform. The study is first of its kind to identify industry 4.0 adoption barriers and develop hierarchical relationships between them using ISM and fuzzy MICMAC methodology in the Indian manufacturing context.
Subfields: Industrial and Manufacturing Engineering, Management Information Systems
View Paper: https://www.semanticscholar.org/paper/480b1fa54f45fdde11c44d25309c2c407276a5e9
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In Industrial and Manufacturing Engineering
In Management Information Systems
Citations in the corpus
- Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective (2020, background): “0 remains largely unexplored in the extant literature and merits further investigation (de Sousa Jabbour et al., 2018a, b; Kamble et al., 2018; Horváth and Szabó, 2019; Oesterreich and Teuteberg, 2016; Xu et al., 2018).”
- Industry 4.0 and lean manufacturing practices for sustainable organisational performance in Indian manufacturing companies (2019, background): “The adoption of I4 T in Indian manufacturing companies is still in its initial stage and slowly gathering momentum (Kamble, Gunasekaran, and Sharma 2018).”
- A performance measurement system for industry 4.0 enabled smart manufacturing system in SMMEs- A review and empirical investigation (2020, background): “…manufacturing systems to Smart Manufacturing Systems (SMS) through the support of automation, data exchanges, cyber-physical systems (CPS), cloud computing, robotics, big data, artificial intelligence, internet of things (IoT) and semi-autonomous industrial techniques (Kamble et al., 2018a).”