Perishable Item Pricing Using Freshness and Demand Elasticity
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Solution Overview
Problem
Retailers face challenges in determining optimal pricing for perishable items to minimize waste while maximizing profits, especially when multiple items with varying freshness rates are involved, and this process is complicated by the need for frequent adjustments based on inventory and demand.
Innovation Solution
An online concierge system uses machine-learning models to predict freshness satisfaction scores and elasticity of demand for items, allowing for the determination of an optimal value based on these factors, which informs price adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If retailers discount items by greater amounts as freshness diminishes, then waste is reduced, but profits are lost
Solution Approach 1:
The system dynamically changes the price parameter based on predicted elasticity of demand and freshness satisfaction score. Instead of using fixed discount rules, the system calculates optimal prices that balance waste reduction and profit maximization by adjusting prices according to real-time demand elasticity predictions and freshness metrics.
Solution Approach 2:
The system implements feedback loops where freshness satisfaction scores and elasticity of demand predictions continuously inform price adjustments. The machine learning models process historical data and real-time metrics to provide feedback on optimal pricing strategies, enabling dynamic adaptation to changing conditions.
2Measurement precision
If retailers manually determine price adjustments frequently, then pricing accuracy improves, but time consumption increases
Solution Approach 1:
The system enables self-service pricing through automated machine learning models that continuously predict elasticity of demand and determine optimal prices without manual intervention. The automated system processes inventory data, freshness metrics, and demand patterns to autonomously adjust prices, eliminating time-consuming manual analysis while maintaining high pricing accuracy.
Solution Approach 2:
The patent replaces manual mechanical price determination processes with automated machine learning systems. The machine learning models substitute human decision-making with algorithmic predictions based on historical data and real-time metrics, achieving both speed and accuracy in pricing decisions.
3Adaptability or versatility
If retailers consider multiple factors for pricing, then pricing comprehensiveness improves, but process complexity increases
Solution Approach 1:
The machine learning system provides universal pricing recommendations that simultaneously consider multiple factors including freshness satisfaction score, elasticity of demand, inventory levels, and historical data. The single automated system handles diverse pricing scenarios and constraints, eliminating the need for separate manual analysis processes for each factor.
Solution Approach 2:
The system merges multiple pricing considerations into a unified machine learning model that processes all relevant factors simultaneously. By combining freshness metrics, demand elasticity predictions, inventory data, and historical patterns into a single integrated system, the patent simplifies the overall process while maintaining comprehensive pricing analysis.
Data Source
AI summary
An online concierge system receives item data for an item included among an inventory at a retailer location, in which the item data includes a set of real-time item data for the item and a set of constraints. The system accesses and applies a first machine-learning model to predict a freshness satisfaction score for the item based at least in part on the item data. The system updates the item data to include the score and accesses and applies a second machine-learning model to predict an elasticity of demand for the item based at least in part on the updated item data. The system determines an optimal value associated with the item based at least in part on the freshness satisfaction score, the elasticity of demand, and the set of constraints. A value associated with the item is then adjusted based at least in part on the optimal value.