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DECISION SUPPORT ALGORITHM DEVELOPMENT FOR ASSORTMENT OPTIMIZATION IN THE RETAIL CHAIN
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AI
DOI：10.3846/jbem.2025.22952.png)
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Abstract
En 中文
As the consumer market changes rapidly, retail networks require a system to optimize the quantity and assortment of goods. The authors develop and test theoretical and practical assortment optimization and distribution principles. The study aims to improve retail assortment management by creating a decision support system for optimizing commodity composition, quantity, and location. The system's primary objective is to enhance the trading margin obtained from the sale while considering constraints related to commodity resources and shelf space. This entails optimizing the procurement and inventory management processes to maximize the profit margin. By generating freight invoices, distributing, and redistributing commodities within the network under inbound logistics orders, the system optimizes the allocation of commodities using information from the company's existing software. The authors present an optimization method for commodities that relies on mathematical modeling and the calculation of the consolidated profitability ratio. It ensures the necessary accuracy and provides assortment management within time and cost limits, without substantial investments in equipment and updating qualifications of employees. The research outcomes are applicable to apparel retail. The practical outcome is maximizing the return on investment for goods sold per day. The algorithm's benefits and effectiveness were calculated based on real data after implementation.
Keywords:
retail chain
assortment optimization
assortment management
commodity management
decision support system
apparel retail
sales analytics
business processing
Journal
IF:
2.7
Papers:
36
Citations:
2.0K