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Modelling the enablers of industry 4.0 in the Indian manufacturing industry

Modelling the enablers of industry 4.0 in the Indian manufacturing industry The vision of Industry 4.0 concept is to create smart factories that will change the current processes of production and manufacturing system using smart machines to produce smart and intelligent products. The main aim of this research is to explore the enablers with regard to Industry 4.0 application in manufacturing industry in India as the available literature shows that manufacturing sector is still doubtful about the implementation of Industry 4.0.Design/methodology/approachSeventeen enablers that can affect the adoption of Industry 4.0 in the manufacturing industry in India have been explored through an extensive review of available literature and viewpoints of industry and academic experts. Total Interpretive Structural Modelling methodology (TISM) has been used to evaluate the interrelationships among these factors. A TISM model has been developed to extract the key enablers influencing Industry 4.0 adoption.FindingsThe result shows that Internet facility from government at reduced price, financial support and continued specialized skills training are the major enablers as they have strong driving power.Practical implicationsProper understanding of these enablers will help the managers and policymakers to explore the impact of each enabler on other enablers as well as the degree of relationships among them and to take concrete steps so that Industry 4.0 can be implemented successfully in the manufacturing sector in India.Originality/valueThis study is pioneer in exploring the enablers Industry 4.0 which is the most advanced concept that has the capability to change the future of Indian manufacturing sector if implemented judiciously and cautiously. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Productivity and Performance Management Emerald Publishing

Modelling the enablers of industry 4.0 in the Indian manufacturing industry

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1741-0401
DOI
10.1108/ijppm-07-2019-0317
Publisher site
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Abstract

The vision of Industry 4.0 concept is to create smart factories that will change the current processes of production and manufacturing system using smart machines to produce smart and intelligent products. The main aim of this research is to explore the enablers with regard to Industry 4.0 application in manufacturing industry in India as the available literature shows that manufacturing sector is still doubtful about the implementation of Industry 4.0.Design/methodology/approachSeventeen enablers that can affect the adoption of Industry 4.0 in the manufacturing industry in India have been explored through an extensive review of available literature and viewpoints of industry and academic experts. Total Interpretive Structural Modelling methodology (TISM) has been used to evaluate the interrelationships among these factors. A TISM model has been developed to extract the key enablers influencing Industry 4.0 adoption.FindingsThe result shows that Internet facility from government at reduced price, financial support and continued specialized skills training are the major enablers as they have strong driving power.Practical implicationsProper understanding of these enablers will help the managers and policymakers to explore the impact of each enabler on other enablers as well as the degree of relationships among them and to take concrete steps so that Industry 4.0 can be implemented successfully in the manufacturing sector in India.Originality/valueThis study is pioneer in exploring the enablers Industry 4.0 which is the most advanced concept that has the capability to change the future of Indian manufacturing sector if implemented judiciously and cautiously.

Journal

International Journal of Productivity and Performance Management – Emerald Publishing

Published: Jun 25, 2021

Keywords: Industry 4.0; Industry 4.0 enablers; Manufacturing industry; Total interpretive structural modeling; MICMAC analysis; Cross-impact matrix multiplication applied to classification; Fuzzy MICMAC

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