Fresh Produce Pricing Optimization: 3% Profit Increase Per Store

Store managers often applied subjective discount timings and rates, such as early or excessive discounting, based on their judgment. This reliance on experience, without considering store characteristics and inventory status, led to reduced sales and high disposal costs for fresh products due to same-day sales principles. Verifying the model's effectiveness and stability in a single store was necessary before expanding to multiple stores.

A predictive model was developed to analyze the discount-sales relationship using product details, time of day, and discount rates to derive optimal discount rates. Historical data was used to train the model for the best discount strategies. The model was tested through simulation for effectiveness and stability before it was implemented in real-world scenarios. The AI platform (Runway) quickly analyzed real-time data and delivered results to relevant departments. An automated training-to-deployment pipeline was used to quickly implement feedback from real stores.

The AI model was seamlessly deployed through interfaces integrated with existing systems. It reduced the workload of store managers by automating discount price predictions, leading to a 3% increase in store profits through data-driven optimal pricing. This approach also minimized waste and protected margins. Within just two weeks, the model's validated results were rapidly applied and stabilized across four stores, demonstrating quick adaptability and effectiveness.