MSME's are the foundation of many economies, making substantial contributions to industrial output, job creation, and economic expansion. However, because of their limited infrastructure, resources, and access to cutting-edge technologies, MSME's frequently struggle to manage supply chain and logistics operations. This research focuses on developing an innovative mathematical model that enhances MSME’s competitiveness by optimizing supply chain and logistics operations using industrial engineering principles. The proposed model adopts a multi-objective optimization framework, addressing critical aspects of supply chain efficiency, including cost minimization, service level enhancement, and resource utilization. By integrating supplier, facility, customer, transportation, and inventory data, the model provides actionable insights for MSME’s to streamline operations, reduce costs, and improve service delivery. This research focuses on developing a mathematical model to optimize supply chain and logistics operations for MSME’s, addressing challenges related to limited resources and infrastructure. The model employs a multi-objective optimization framework, aiming to minimize costs, enhance service levels, and improve resource utilization. It integrates data on suppliers, facilities, customers, transportation, and inventory, providing actionable insights for MSME’s to streamline operations and reduce costs. A hypothetical dataset, representing suppliers, facilities, and customers across different locations with varying demands, is used to demonstrate the model's applicability. Decision variables in the model represent transportation flows, facility operations, and inventory levels, while constraints ensure demand fulfilment and operational feasibility. The objectives of the model include minimizing procurement, transportation, and inventory holding costs, while maximizing service levels and resource utilization. The optimization process considers factors like transportation lead times, operational costs, and stockout penalties, making it highly relevant to MSME’s contexts. A realistic dataset simulates MSME’s supply chain scenarios, including supplier capacities, facility costs, customer demands, and transportation dynamics, reflecting logistical challenges in India. Transportation data captures cost-per-unit distance, lead times, and distances between nodes, while resource data provides insights into labor availability and equipment utilization. Preliminary results show that the model significantly reduces supply chain costs while maintaining high service levels and improving resource efficiency. It identifies optimal transportation routes, balances inventory levels at facilities, and suggests ways to enhance labor and equipment utilization. Overall, the model contributes to operational efficiency and competitiveness for MSME’s by optimizing logistics and supply chain operations.