• Arvanpreet Singh
• Sourabh Sharma
• Ashwani Kumar

Supply Chain Management (SCM) is a critical discipline for ensuring the efficient flow of goods, services, and information across the global marketplace. The Supply Chain Operations Reference (SCOR) model provides a comprehensive framework for evaluating, designing, and optimizing supply chains. This paper investigates innovative optimization techniques in SCM under the SCOR framework, focusing on solutions that do not rely on evolutionary algorithms. Through the integration of mathematical models, heuristic methods, and data-driven approaches, this study addresses challenges such as routing, inventory management, and resource allocation. Using SCOR's key metrics—reliability, responsiveness, agility, cost, and asset management efficiency—the proposed methodologies demonstrate their ability to achieve balanced and efficient supply chain performance. The findings highlight how deterministic and heuristic approaches can be adapted to optimize supply chains in dynamic and uncertain environments. This study concludes with recommendations for future research in data-driven SCM optimization and hybrid methodologies.

Supply Chain Management, SCOR Model, Optimization Techniques, Heuristic Methods, Supply Chain Performance

"Optimizing Supply Chain Management with Mixed-Integer Programming: A Case Study on Distribution Network Efficiency", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.h943-h951, May-2025, Available :http://www.jetir.org/papers/JETIR2505897.pdf

"Optimizing Supply Chain Management with Mixed-Integer Programming: A Case Study on Distribution Network Efficiency", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. pph943-h951, May-2025, Available at : http://www.jetir.org/papers/JETIR2505897.pdf