Intelligent decision-making system for jewelry retail prediction and inventory management integrating CRM data - Han, Ting-Ting (Edelweiss Applied Science and Technology, 2025)
This study develops an intelligent decision support system that integrates Customer Relationship Management (CRM) data with inventory optimization to address the unique challenges of jewelry retail, characterized by high-value merchandise, emotional purchasing patterns, and seasonal demand fluctuations. A case-based empirical approach using semi-structured interviews and field observations (qualitative) alongside analysis of sales data, inventory records, and customer transactions (quantitative) is employed to evaluate the customer-centric inventory management system. Empirical evaluation in a mid-sized jewelry retail environment demonstrated significant performance improvements: a 23.5% increase in inventory turnover, 38.7% fewer stockout events, and a 14.6% higher customer satisfaction compared to control stores. The system enabled a "less inventory, better service" strategy, reducing total inventory by 12.3% while increasing product availability for high-value customers by 27.5%. The integration of CRM data with inventory management creates a transformative approach to retail operations, shifting from product-oriented to customer-oriented decision-making while simultaneously improving financial and service metrics. With a demonstrated ROI of 167% and an 18.6-month payback period, this study provides both a theoretical framework for blending customer data with inventory control decisions and a practical implementation guide for specialty retail environments.