Core Insights - The "Dynamic Replenishment Decision Dataset" developed by the company has been recognized as a typical application case of high-quality datasets in Wuhan, enhancing operational efficiency and competitiveness in the market [1] Group 1: Operational Efficiency - The traditional procurement decision-making process was dominated by manual calculations, taking approximately 3 days to complete, which led to stockouts or unsold inventory in a rapidly changing market [1] - The application of the new dataset has significantly reduced the procurement decision time from 3 days to 0.9 days, improving emergency replenishment response speed by 70% [1] Group 2: Inventory Management - The data system monitors inventory turnover rates in real-time, analyzing various factors such as sales data, seasonal trends, promotional activities, weather changes, and store characteristics to accurately predict SKU demand for the next 7-30 days, maintaining an error rate within 5% [3] - Based on accurate forecasts, the system automatically adjusts inventory strategies, reducing the proportion of unsold goods from 15% to 8%, and decreasing expired losses by 40%, while increasing the proportion of best-selling items by 70% [3] Group 3: Cost Control and Industry Impact - The company has established a closed-loop system of "intelligent forecasting - precise execution - dynamic monitoring - risk warning," achieving significant results in operational cost control [3] - This approach provides a growth paradigm for the snack industry, showcasing the potential for improved efficiency and effectiveness in operations [3]