Big Supply Chain Analytics Enhances Decision-Making: A Literature Review Approach

Abstract

The way organizations function in today's dynamic market landscape has been changed by the integration of data analytics inside supply chain management. Within the supply chain domain, data analytics pertains to be methodical procedure of gathering, analyzing, and interpreting diverse data kinds produced along the chain in order to extract significant understandings and practical intelligence. It entails applying methods from the fields of descriptive, predictive, and prescriptive analytics to boost operational effectiveness, optimize decision-making, and raise overall performance in the ecosystem of the supply chain. Organizations may make well-informed decisions, spot trends, and proactively handle problems by analyzing data from a variety of sources, including suppliers, manufacturers, distributors, and retailers. This results in more efficient resource allocation and streamlined procedures. Data analytics is playing a more and more important role as businesses look to improve operational efficiency, cut costs, and successfully satisfy customer needs. This study examines the role that data analytics plays in supply chain optimization, helping firms to increase the precision of their forecasts, simplify their logistical procedures, and make well-informed decisions through literature review. Most of the literature that is currently available agrees that big data analytics goes far beyond simply reimagining the supply chain. It may help the next generation of multinational corporations function more adaptably in a setting that is becoming more demanding and unpredictable. This conceptual paper applies  relevant review literature and draw conclusions. DOI: https://doi.org/10.52783/pst.1315