Abstract

Business analytics (BA) platforms have become critical tools in driving operational efficiency across transportation and supply chain sectors. This systematic review explores the landscape of contemporary BA platforms, analyzing their capabilities, adoption trends, and the extent to which they contribute to performance optimization, decision-making, and real-time responsiveness. The review synthesizes evidence from peer-reviewed journals, industry reports, and case studies published over the last decade, identifying key themes such as predictive analytics, real-time monitoring, machine learning integration, and data visualization. Findings highlight that BA platforms not only enhance logistics planning, demand forecasting, and inventory management but also support sustainability and risk mitigation strategies. The study underscores the growing importance of cloud-based and AI-powered analytics systems, noting their role in enabling agile and resilient supply chain operations. Challenges related to data quality, integration complexity, and organizational readiness are also discussed. This review provides actionable insights for practitioners, researchers, and policymakers aiming to leverage business analytics for strategic and operational improvements in transportation and supply chain networks.