The growing demand for bottled beverages has led to the search for optimal configurations that represent the lowest costs. Using crossdocking techniques reduces storage costs, these costs being the main ones in the logistics of distribution of products to the consumer. However, it is vitally important to consider the ergonomics of the workers who are subjected to the loading and unloading of products to meet the demands. Various ailments have been reported to the authorities, and it is imperative to address them for decision making. Likewise, the best arrangement of the products within these fast warehouses is associated with the relationship between the number of times a worker travels to pick up the product from the place where it is located to the loading area and the distance. In this work, the distribution from the production plants and the crossdocking to the distribution centers are proposed jointly and in each distribution center the best arrangement of the products is determined, as well as the ergonomics of those involved, considering the best scheme that represents the lowest cost. The results show the best distribution of products as well as the crossdocking that must be installed to meet the demands of the distribution centers.

This study formulates the sequencing of pharmaceutical products on packaging lines as a prescriptive analytics problem to enhance operational efficiency in healthcare manufacturing systems. A two-stage optimization framework is developed that integrates descriptive analytics, through similarity-based product clustering, with prescriptive optimization based on a Traveling Salesman Problem (TSP) sequencing model to minimize setup times and related inefficiencies. The problem is solved using both an exact optimization approach implemented in Gurobi and a metaheuristic ant system algorithm. The methodology is evaluated on four real-world production lines in a pharmaceutical company, where results show that as product complexity increases, the Ant System achieves solutions comparable to or better than those obtained by the exact solver for full-set sequencing. Clustering products into homogeneous families prior to sequencing yields additional reductions in setup times, underscoring the value of structured segmentation in operational decision-making. Numerical and graphical analyses further reveal stable transition patterns within product families, enabling more standardized and predictable sequencing strategies. Compared with the company’s existing manual scheduling practices, the proposed analytics-driven framework reduces setup times by up to 19.3% and is readily adaptable to other healthcare and life sciences manufacturing environments where setup minimization is critical to operational performance.