"Optimization techniques for supply chain decision making" concentrates on the key factor of optimization techniques which is used in the improvement of the effectiveness of supply chain management. The abstract represents a summary of the major ideas and methods addressed in the article. The article focuses on the growing complexity of supply chain networks and highlights the necessity of the right decision-making strategies to deal with issues like demand variability, inventory management and the transportation logistics that complicated these networks. Several optimization methods such as mathematical modeling, simulation, and heuristic strategies are compared to determine their suitability to various sections of the supply chain management. The abstract stresses the need to merge these techniques into decision support systems for timely decisions and further enhances total supply chain performance. In addition to this, the article deliberates on how methods like artificial intelligence, machine learning, and big data analytics are incorporated into the optimization model in order to identify and solve the emerging trends and the challenges in the supply chain management system. Case studies and examples of practical cases are presented to demonstrate the use and effectiveness of optimization in the wide range of supply chain contexts. This section furnishes the conclusion by stressing the potential role of an ongoing research and innovation process in the optimization methods to handle the complex market conditions and the processes involved in sc. . Therefore, this article presents a complete guideline for researchers, practitioners, and policymakers who want to exploit optimization approaches for better decision making and more efficient performance when managing supply chains.