A framework to achieve sustainability in manufacturing organisations of developing economies using industry 4.0 technologies’ enablers

Abstract

Sustainability has emerged as one of the most important issues in the international market. Ignorance of sustainability aspects has led many manufacturing organisations to face huge financial losses. It has been observed that developed nations have successfully achieved sustainability in their manufacturing sectors. However, the rate of sustainability adoption in developing nations is significantly poorer. The current business trend offers new technologies such as the Internet of Things, Big data analytics, Blockchain, Machine learning, etc. These technologies can be termed under the Industry 4.0 paradigm when considered within a manufacturing context. It is significant to notice that such new technologies directly or indirectly contribute to sustainability. So, it is necessary to explore the enablers that facilitate sustainability adoption. This study aims to develop a framework to improve sustainability adoption across manufacturing organisations of developing nations using Industry 4.0 technologies. Initially, the enablers that strongly influence sustainability adoption are identified through a literature review. Further, a large scale survey is conducted to finalise the Industry 4.0 technologies’ enablers to be included in the framework. Based on the empirical analysis, a framework is developed and tested across an Indian manufacturing case organisation. Finally, Robust Best Worst Method (RBWM) is utilised to identify the intensity of influence of each enabler included in the framework. The findings of the study reveal that managerial and economical, and environmental enablers possess a strong contribution toward sustainability adoption. The outcomes of the present study will be beneficial for researchers, practitioners, and policymakers.

Publication DOI: https://doi.org/10.1016/j.compind.2020.103280
Divisions: College of Business and Social Sciences > Aston Business School
Additional Information: © 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: Developing nations,Empirical study,Industry 4.0,Manufacturing supply chain,New technologies,Robust Best Worst Method (RBWM),Sustainability,Computer Science(all),Engineering(all)
Publication ISSN: 1872-6194
Full Text Link:
Related URLs: https://www.sci ... 166361520305145 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2020-11-01
Published Online Date: 2020-07-07
Accepted Date: 2020-06-25
Authors: Yadav, Gunjan
Kumar, Anil
Luthra, Sunil
Garza-Reyes, Jose Arturo
Kumar, Vikas
Batista, Luciano (ORCID Profile 0000-0002-0367-2975)

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 7 July 2022.

License: Creative Commons Attribution Non-commercial No Derivatives


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