Moderating Role of Data Driven Decision Making on the Relationship between Reverse Logistics and Firm Performance - A Development of Conceptual Framework

The growing emphasis on sustainability in Supply Chain Management (SCM) has highlighted reverse logistics (RL) as a critical strategy for achieving both environmental and economic objectives. However, existing studies often overlook the moderating role of Data-Driven Decision-Making (DDDM) in enhancing the relationship between RL and firm performance. This study addresses this gap by proposing a conceptual framework that integrates RL, sustainability, profitability, and DDDM to optimize reverse logistics operations and bolster firm performance.

Drawing on a synthesis of existing literature, the study identifies key challenges in RL, including inefficiencies in recovery and recycling processes, and proposes data-centric strategies to address these issues. The conceptual framework illustrates how leveraging DDDM enables firms to analyze large datasets, streamline RL processes, and ensure alignment with sustainability goals. By integrating data-driven insights, organizations can improve operational agility, reduce costs, and achieve compliance with environmental regulations, ultimately enhancing profitability and competitive positioning.

The methodology focuses on developing a theoretically grounded framework that lays the foundation for future empirical validation. Key results suggest that firms employing DDDM in RL processes can significantly reduce waste, lower carbon footprints, and create value through efficient resource use. This research provides actionable insights for managers and policymakers, advocating for the integration of DDDM to transform RL into a strategic driver of sustainability and profitability.

This article holds significant importance for the scientific community as it addresses the critical intersection of reverse logistics, sustainability, and profitability, a growing area of interest in both academia and industry. By integrating the role of data-driven decision-making, the study provides a novel conceptual framework that can enhance operational efficiency and promote sustainable business practices. This study not only contributes to the theoretical advancement of reverse logistics and sustainability but also offers practical insights for organizations striving to balance economic and environmental objectives. Furthermore, it highlights the transformative potential of data analytics in driving sustainable supply chain strategies, making it a valuable resource for researchers and practitioners alike.