Bridging theoretical advancements and practical implementation of analytics in data-driven supply chain management: a review

Supply Chain Management (SCM) has evolved from a logistical function to a strategic pillar, driven by the need for efficiency, cost reduction, and resilience in complex global networks. As supply chains grow more interconnected, companies face pressure to optimize operations while mitigating disruptions. Data-driven approaches, leveraging advanced analytics, AI, and operations research, have emerged as key tools for enhancing decision-making. However, a significant gap persists between theoretical advancements in SCM analytics and their practical implementation. Previous literature points out that 40% of analytical methods proposed in academia lack real-world application, highlighting discrepancies between theoretical assumptions and practical realities. This study aims to address two objectives: i) analyze the evolution of analytical methods (descriptive, diagnostic, predictive, and prescriptive) in SCM, focusing on their role in datadriven decision-making across industries; ii) identify and categorize limitations hindering their adoption, distinguishing between theoretical and practical challenges. Using a systematic literature review from the Scopus database, the research introduces the SCM-Data analytics Constraints Matrix, a framework organizing limitations from both academia and industry. Future steps include industrial surveys to validate findings and develop a strategic roadmap for mitigating constraints, fostering greater adoption of data-driven methods in SC.