Real-Time Analytics and Supply Chain Transformation in the Digital Era - Salwa , Nisrina (Sinergi International Journal of Logistics, 2023)
Big Data Analytics (BDA) has emerged as a transformative tool in optimizing supply chain management by enabling real-time insights, predictive forecasting, and operational efficiency. This study presents a comprehensive narrative review to evaluate the strategic application of BDA across key supply chain domains. Literature was collected from Scopus, Google Scholar, and related databases using Boolean search strings to identify relevant peer-reviewed studies published between 2018 and 2024. The review synthesized findings across four thematic areas: demand forecasting, inventory and logistics management, supply chain resilience, and technology integration. Results indicate that BDA significantly improves forecasting accuracy, enhances inventory efficiency, supports risk mitigation, and enables agile responses to market changes. BDA-integrated systems such as ERP and IoT provide strategic visibility and faster decision-making. Case studies from various sectors, including retail, healthcare, and agribusiness, demonstrate measurable cost reductions and increased responsiveness. However, challenges such as legacy IT systems, data security concerns, and workforce capability gaps limit implementation. This study discusses the systemic implications of BDA, proposing policies and managerial strategies to overcome integration barriers. It also outlines future research directions in adaptive analytics, sustainable operations, and digital infrastructure. Ultimately, this review underscores BDA’s potential to enable dynamic and resilient supply chains, aligning operational goals with long-term sustainability.