Leveraging Big Data-Driven Strategies for Sustainable Supply Chain Management in Manufacturing firms

In the last decade, the Sustainable Supply Chain Management (SSCM) in manufacturing firms has been greatly improved with the integration of Big Data-Driven Strategies (BDS). Considering manufacturing sustainability goals, its application in supply chain operations is underexplored, but it provides transformational insights into supply chain operations that allow firms to optimally utilize resources, predict disruptions and minimize environmental footprint. The objective of this paper is to determine the integration of the big data driven strategies to make the sustainable supply chain management (SSCM) within manufacturing companies more effective as the need for environmentally and economically efficient operation is increasing due to rapid technological progress. As SSCM is becoming more vital to the manufacturing industry, this research explores the utilization of big data analytics (BDA) for resource usage optimization, disruption prediction and reduced environmental impact, in line with global sustainability objectives. Five main criteria and 16 sub criteria important for BDA implementation on SSCM have been considered for the purpose of enhancing operational efficiency, resource optimization and transparency in supply chain. The research also identifies key gaps in the literature, including in the use of BDA to meet specific manufacturing sustainability goals such as carbon reduction, waste management and resource efficiency. The techniques of these gaps are needed to fill, to suggest BDA frameworks tailored to the industry in accordance with the sustainability targets. The findings of the research propose a comprehensive framework for manufacturing firms to make BDA-driven SSCM strategy environmentally and economically competitive. Nevertheless, empirical research is still required to confirm how these frameworks work in actual industry applications and to further develop solutions adapted to the requirements of individual industries. Future research should include development of data governance protocol, exploring advanced AI and ML technologies, and assessing long term implication of BDA for sustainability by longitudinal studies. This paper offers a foundation for use of big data to enhance SSCM practices as well as overlooked gaps and pathways to accelerate future sustainable manufacturing practices.