AI-ENABLED REVERSE LOGISTICS AND BIG DATA FOR ENHANCED WASTE AND RESOURCE MANAGEMENT

Abstract

The primary objective of this research is to investigate the impact of AI-powered resource and waste management systems on reverse logistics and big data analytics within the manufacturing industry of Saudi Arabia. The present study has also investigated the role of Firm Performance as a mediator in Circular Economy Supply Chains, as well as the moderating effect of Environmental Process Integration. The research study has employed a quantitative research methodology and utilised a survey instrument to gather data from employees working in the manufacturing sector of Saudi Arabia. The data underwent analysis using the Statistical Package for the Social Sciences (SPSS).The findings of the study indicated that Big Data Driven (BDD) supply chain did not have a significant impact on Waste Management (WM). However, it was observed that Big Data Analytics (BDA), Circular Economy Human Resource (CEHR), and Reverse Logistics (RI) were found to have a significant influence on Waste Management (WM). Moreover, our analysis reveals that the sole significant factor mediating the relationship between the successful execution of reverse logistics and the reduction of waste in the supply chain is firm performance. Additionally, it has been observed that the integration of environmental processes plays a significant role in moderating the association between waste minimization and firm performance within the context of circular economy supply. This research adds to the current corpus of knowledge by investigating and analysing observed constructs and offering recommendations to the manufacturing sector regarding waste reduction, sustainability enhancement, and firm performance through the utilisation of big data and artificial intelligence. To conclude, this section will discuss the study's constraints and put forward potential directions for future research.