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

Yadav, Gunjan; Kumar, Anil; Luthra, Sunil; Garza-Reyes, Jose Arturo; Kumar, Vikas; Batista, Luciano

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.

• Journal Article Type: Article
• Acceptance Date: Jun 25, 2020
• Online Publication Date: Jul 7, 2020
• Publication Date: Nov 1, 2020
• Deposit Date: Jun 26, 2020
• Publicly Available Date: Jul 8, 2022
• Journal: Computers in Industry
• Print ISSN: 0166-3615
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 122
• Article Number: 103280
• DOI: https://doi.org/10.1016/j.compind.2020.103280
• Keywords: Sustainability; Manufacturing supply chain; Industry 4.0; Developing nations; New technologies; Empirical study; Robust Best Worst Method (RBWM)
• Public URL: https://uwe-repository.worktribe.com/output/6056843