A SYSTEMATIC REVIEW OF ERP-INTEGRATED DECISION SUPPORT SYSTEMS FOR FINANCIAL AND OPERATIONAL OPTIMIZATION IN GLOBAL RETAILS BUSINESS

Abstract

Enterprise Resource Planning (ERP) systems and Decision Support Systems (DSS) have become critical components in driving digital transformation, operational efficiency, and strategic decision-making in the global retail industry. The integration of these systems enables organizations to unify transactional workflows with advanced analytics, allowing for real-time visibility, financial control, inventory optimization, and data-driven decision automation. This systematic review examines the convergence of ERP and DSS technologies with a particular focus on their role in enhancing financial and operational performance in multinational retail enterprises. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines, a total of 230 peer-reviewed articles published between 2012 and 2024 were identified, screened, and analyzed. The review synthesizes findings across multiple themes, including business intelligence, predictive analytics, inventory management, financial forecasting, and executive decision automation. The results indicate that ERP-DSS integration significantly improves forecasting accuracy, cross-border financial consolidation, operational agility, and strategic responsiveness in retail environments. Additionally, organizations with higher ERP-DSS maturity demonstrated greater resilience, performance consistency, and innovation capacity, especially when supported by robust data governance and localized implementation strategies. However, the review also identifies critical challenges, such as cultural resistance, integration complexity, and regulatory diversity, particularly in multinational contexts. Overall, the study underscores ERP-DSS integration as a foundational enabler of enterprise intelligence and strategic value creation in the modern retail sector, offering insights for researchers, practitioners, and policymakers seeking to advance performance and adaptability in data-intensive business environments.