Artificial intelligence-driven internet of things-based green supply chain for carbon reduction in sustainable manufacturing.

Journal: Journal of environmental management
Published Date:

Abstract

Recent advancements in sustainable business practices and information technology trends have led to the rise of green smart supply chains. These green sustainable supply chains are a novel approach that involves information technology to enhance the operational standards throughout various industries. The integration of artificial intelligence and the Internet of Things has transformed these sectors by utilizing intelligent supply chain practices, improving efficiency and significantly reducing environmental impacts. Based on this observation, the present study proposed a framework that integrates artificial intelligence-assisted internet of things-driven green supply chains in the manufacturing sector, a major contributor of carbon emissions. To succeed in the competitive industry 4.0, manufacturers must focus on developing sustainable products and processes. The framework proposed in this study aims to achieve net-zero carbon emissions by leveraging artificial intelligence in carbon emission reduction. By utilizing the advanced intelligence technology of improved support vector regression, the study demonstrates a significant decrease in carbon emissions. Also, green management, green products, and green technological innovations are crucial factors in the framework that help to increase the effectiveness of these solutions in achieving sustainability goals. Further, this research highlights the importance of intelligent manufacturing for a sustainable future and significantly contributes to future projects that primarily rely on these domains.

Authors

  • Luyao Zhang
    State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Nisreen Innab
    Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Diriyah, Riyadh, Saudi Arabia.
  • Shuhaida Mohamed Shuhidan
    Centre for Research in Data Science, Computer and Information Sciences Department, Faculty of Science and Information Technology, Universiti Teknologi PETRONAS, Malaysia.
  • Yiting Pan
    Department of Administrative Affairs, Wenzhou Business College, Wenzhou, 325035, China. Electronic address: yitingpanreas@outlook.com.
  • Ying Zhang
    Department of Nephrology, Nanchong Central Hospital Affiliated to North Sichuan Medical College, Nanchong, China.
  • Hafizan Mat Som
    Computer and Information Sciences Department, Faculty of Science and Information Technology Universiti Teknologi PETRONAS, Malaysia.
  • Nada Alasbali
    Department of Informatics and Computer Systems, College of Computer Science, King Khalid University, Abha, 61421, Saudi Arabia.