Real-time Decision Support Systems in Supply Chain Management: Leveraging Machine Learning for Agility and Responsiveness

Abstract

Real-time decision support systems (DSS) powered by machine learning (ML) are revolutionizing supply chain management, enabling organizations to adapt swiftly to dynamic market conditions and customer demands. This paper explores the role of ML-driven DSS in enhancing agility and responsiveness within supply chain networks. Leveraging historical and real-time data, ML algorithms analyze complex patterns, predict future trends, and recommend optimal actions across various supply chain functions. From demand forecasting and inventory optimization to supplier selection and logistics planning, ML-enabled DSS provides actionable insights that drive operational efficiency and strategic decision-making. This abstract examines key components of ML-driven DSS, including data integration, predictive analytics, and prescriptive recommendations, highlighting their impact on supply chain agility and responsiveness. Case studies and examples demonstrate how organizations leverage ML-driven DSS to mitigate risks, optimize resources, and capitalize on opportunities in today's dynamic business landscape. By harnessing the power of ML technologies, organizations can build agile and responsive supply chains capable of meeting evolving customer expectations and achieving competitive advantage in the marketplace.