| dc.description.abstract | This research proposes an optimization model to support operational decision-making and efficiency learning in the context of multiproduct retail. The main objective of this study is to develop an approach that can assist retail companies in optimizing their decisions related to inventory, product allocation, and operational management efficiently. The optimization model is proposed based on the integration of product selection analysis, inventory management, and resource allocation. This approach combines mathematical optimization techniques, predictive modeling, and machine learning to achieve efficiency goals. Historical sales data, demand trends, as well as external factors influencing retail performance are utilized to formulate an accurate predictive model. Furthermore, the aspect of efficiency learning is also considered in this research. The proposed model can periodically learn from the implemented operational decisions, taking into account the impact of actual demand variations on operational performance. Thus, this model can dynamically adapt decision plans according to changes in the business environment.This research is expected to contribute to the development of best practices for operational decision-making in the multiproduct retail industry. By optimizing product allocation and inventory management, retail companies can reduce operational costs, enhance customer service, and optimize overall performance. Additionally, the efficiency learning approach will help companies remain responsive to market changes in a more adaptive and rapid manner. | en_US |
