Optimizing Procurement Strategies for Diverse Product Segments: A Case Study in Pharmaceutical Supply Chain Management

Selecting the most suitable procurement strategy is crucial to the efficient management of supply chain operations and the prevention of stock shortages. Nevertheless, when dealing with a wide variety of products, this task becomes an intricate challenge. While traditional and advanced procurement tools are available, applying them across such diverse product ranges is often impractical. This research is dedicated to determining distinct procurement strategies tailored to each product cluster. These strategies will be designed to accommodate the technical and financial constraints specific to each cluster. To address the optimization challenges associated with clustering algorithms, especially within complex search spaces, metaheuristic algorithms are considered as promising solutions. In this paper, Accelerated Particle Swarm Optimization (APSO) is harnessed for its exploratory capabilities, and Teaching Learning Based Algorithms (TLBO) are leveraged for their high exploitation competence. This innovative approach effectively combines the strengths of both algorithms, ensuring optimal clustering solutions in an efficient manner. The suggested approach outperforms the accuracy of the well-known metaheuristics including Grey Wolf Optimizer and the Whale Optimization Algorithm. This methodology successfully identifies five major clusters and assigns the appropriate procurement strategy to each cluster. The selection of a suitable procurement strategy for each product cluster significantly enhances overall procurement performance. This study introduces a powerful approach to assist managers in adapting procurement strategies for different product clusters. This approach has been implemented within organizations specializing in pharmaceutical freight and holds potential applicability across various product types. This innovation has the capacity to significantly impact and enhance global procurement performance.