Digital and Data-Driven Optimization of Integrated Decision Processes to Improve Operational Efficiency and Sustainability in Multiproduct Retail Systems

Abstract

The increasing complexity of multiproduct retail systems, driven by fluctuating consumer demand, resource constraints, and sustainability requirements, necessitates advanced decision-making approaches that integrate operational efficiency with environmental considerations. This study proposes a digital and data-driven optimization framework for integrated decision processes in multiproduct retail systems to enhance operational performance while supporting sustainability objectives. By leveraging real-time data analytics, machine learning, and optimization techniques, the proposed framework integrates key retail decision areas, including inventory management, demand forecasting, pricing strategies, and resource allocation. The integration enables retailers to minimize operational inefficiencies, reduce energy consumption, and lower environmental impacts associated with logistics and inventory operations. The results demonstrate that data-driven integration of decision processes can significantly improve system responsiveness, reduce waste, and enhance overall efficiency while aligning retail operations with sustainability and low-carbon transition goals. This study highlights the potential of digital transformation and data-driven optimization as strategic enablers for sustainable retail systems and provides insights for practitioners and policymakers seeking to balance economic performance with environmental responsibility.