多点数智AI产品专家宋楠：用AI解决超市场景痛点

Core Insights - The article emphasizes the importance of AI in optimizing fresh product clearance in the retail industry, combining demand forecasting with dynamic pricing to enhance both profit and efficiency [2][3][19] Group 1: Industry Challenges and AI Opportunities - Fresh product clearance is a critical yet challenging operational scenario in supermarkets, directly impacting product freshness and consumer experience [3][4] - Poorly designed discount strategies can lead to significant profit losses for stores, highlighting a persistent pain point for retailers [3][4] - AI's core value lies in its ability to integrate demand prediction with dynamic pricing, helping businesses ensure product sell-through while increasing the proportion of full-price sales [9][19] Group 2: Company Overview and Technological Advancements - Dmall Inc., established in 2015, is a leading provider of retail digital solutions in Asia, addressing the fresh product clearance challenge through advanced technology [3][4] - The company plans to upgrade its core system, Dmall OS, to version 3.0 in 2024, incorporating AI technology, and will prioritize generative AI in its strategy by 2025 [3][4] Group 3: AI Implementation and Operational Efficiency - Dmall's AI model utilizes large-scale data to optimize clearance strategies, balancing product freshness with store profitability [4][8] - The model aims to automate decision-making processes, reducing reliance on manual approval and enhancing operational efficiency [8][12] - The implementation of AI has shown to improve profit margins significantly, with examples indicating a daily profit increase of 3,000 yuan and a monthly profit increase exceeding 90,000 yuan for certain stores [12][19] Group 4: Feedback and Continuous Improvement - Continuous feedback from business personnel is crucial for refining the AI model, ensuring it aligns with real-world operational needs [11][17] - The model's design allows for autonomous learning, enabling it to adapt to various scenarios without being strictly bound by predefined rules [14][19] - The transition to AI-driven decision-making has led to a shift in employee roles, allowing staff to focus on higher-value tasks while the model handles repetitive processes [18][19]