Multi objective Machine Learning for Real-Time Decision Making in Smart Supply Chains
Keywords:
Smart supply chains, Multiobjective optimization, Machine learning, Sustainability, Resilience, Real-time decision-making, Emerging technologiesAbstract
In today’s globalized world, optimizing smart supply chains is essential for maintaining competitiveness, resilience, and sustainability. This study presents a multiobjective framework integrating machine learning (ML) to enable real-time decision-making in smart supply chains. The proposed framework addresses key trade-offs, such as minimizing costs, reducing environmental impacts, and enhancing service levels. Utilizing advanced ML techniques, the framework predicts demand fluctuations, mitigates disruptions, and dynamically optimizes inventory and routing decisions. Furthermore, it integrates emerging technologies like the Internet of Things (IoT), blockchain, and digital twins to enhance transparency and operational efficiency. Case studies and simulations demonstrate the framework's effectiveness in addressing modern supply chain challenges while balancing sustainability and resilience goals. This research provides a practical roadmap for businesses and policymakers to adopt cutting-edge technologies for supply chain optimization, ensuring adaptability to ever-changing market dynamics.
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