@article { , title = {Assessing the key enablers for Industry 4.0 adoption using MICMAC analysis: A case study}, abstract = {Purpose: The aim of this research is to assess the key enablers of Industry 4.0 (I4.0) in the context of the Indian automobile industry. It is done to apprehend their comparative effect on executing I4.0 concepts and technology in manufacturing industries, in a developing country context. The progression to I4.0 grants the opportunity for manufacturers to harness the benefits of this industry generation. Design/methodology/approach: The literature related to I4.0 has been reviewed for the identification of key enablers of I4.0. The enablers were further verified by academic professionals. Additionally, key executive insights had been revealed by using interpretive structural modelling (ISM) model for the vital enablers unique to the Indian scenario. The authors have also applied MICMAC analysis to group the enablers of I4.0. Findings: The analysis of this study’s data from respondents using ISM provided us with seven levels of enabler framework. This study adds to the existing literature on I4.0 enablers and findings highlight the specificities of the territories in India context. The results show that top management is the major enabler to I4.0 implementation. Infact, it occupies the 7th layer of the ISM framework. Subsequently, government policies enable substantial support to develop smart factories in India. Practical implications: The findings of this work provide implementers of I4.0 in the automobile industry in the form of a robust framework. This framework can be followed by the automobile sector in enhancing their competency in the competitive market and ultimately provide a positive outcome for the Indian economic development led by these businesses. Furthermore, this work will guide decision-makers in enabling strategic integration of I4.0, opening doors for the development of new business opportunities as well. Originality/value: The study proposes a framework for Indian automobile industries. The automobile sector was chosen for this study as it covers a large percentage of the market share of the manufacturing industry in India. The existing literature does not address the broader picture of I4.0 and most papers do not provide validation of the data collected. This study thus addresses this research gap.}, doi = {10.1108/IJPPM-02-2020-0053}, issn = {1741-0401}, issue = {5}, journal = {International Journal of Productivity and Performance Management}, pages = {1049-1071}, publicationstatus = {Published}, publisher = {Emerald}, url = {https://uwe-repository.worktribe.com/output/7107099}, volume = {70}, keyword = {Business Administration, Innovation, Operations Management and Supply, Digital futures, Industry 40, Enablers, ISM, Multi-Criteria Decision Making, Automobile Industry, India}, year = {2021}, author = {Krishnan, Srijit and Gupta, Sumit and Kaliyan, Mathiyazhagan and Kumar, Vikas and Garza-Reyes, Jose Arturo} }