Dynamic Perishable Pricing Using Camera-Based Stock and Expiry Data
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Solution Overview
Problem
Retail environments face significant perishable shrink due to rapid deterioration rates and unpredictable consumer behavior, leading to financial losses and environmental impact, with conventional price adjustments lacking a data-driven approach to optimize inventory and revenue.
Innovation Solution
A perishable shrink management system utilizing machine learning models and real-time data from edge cameras to dynamically adjust prices based on stock levels, expiration dates, and anticipated deliveries, minimizing shrink through data-driven decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If conventional arbitrary price reductions are applied to reduce perishable shrink, then perishable shrink may be reduced, but revenue and profit margin are undermined due to lack of data-driven optimization
Solution Approach 1:
The system dynamically adjusts prices in real-time based on current inventory levels, product freshness, demand forecasts, and expiration dates. Instead of static or arbitrary price reductions, the pricing strategy adapts continuously to changing conditions, optimizing both perishable shrink reduction and revenue preservation through data-driven decision-making
Solution Approach 2:
The system changes multiple pricing parameters simultaneously including discount depth, promotion timing, and price elasticity factors based on real-time data analysis. By adjusting these parameters optimally rather than applying uniform reductions, the system minimizes perishable shrink while maintaining improved revenue and profit margins
2Loss of substance
If real-time dynamic pricing and inventory monitoring systems are implemented, then perishable shrink is reduced and inventory levels are optimized, but system complexity and implementation costs increase
Solution Approach 1:
The system integrates multiple functions into a unified platform including real-time inventory monitoring, dynamic pricing optimization, demand forecasting, and expiration tracking. This multi-functional approach consolidates what would otherwise require separate systems, reducing overall complexity while achieving comprehensive perishable shrink management
Solution Approach 2:
The system automatically collects data from point-of-sale systems, updates inventory levels, adjusts pricing strategies, and generates optimization recommendations without requiring constant manual intervention. This self-service capability reduces operational complexity while maintaining real-time monitoring and control
Data Source
AI summary
Methods and apparatus for dynamic pricing adjustment and inventory optimization are provided. Stock level data is received via a camera, where the stock level data comprises an estimated stock level of a product batch within a physical site. Product information for the product batch is retrieved from a database, where the product information comprises an expiration date and a first price for the product price. A second price for the product batch is calculated using a machine learning (ML) model based on the expiration date and the estimated stock level. The database is updated with the second price for the product batch.