Dynamic Pricing Engine for Retail Food Waste Reduction
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Solution Overview
Problem
The grocery industry faces significant losses due to food spoilage, with consumers preferring items with longer shelf lives over those about to expire, leading to unsold and wasted goods. Existing pricing methods fail to effectively address this issue by not dynamically adjusting prices based on expiration dates and other relevant factors.
Innovation Solution
A system utilizing RFID tags, barcode scanners, and a dynamic pricing engine with machine learning algorithms to optimize prices in real-time, taking into account factors like expiration dates, stock levels, demand, and marketing campaigns, to reduce waste and increase revenue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If consumers are given the choice between goods with longer shelf lives and goods about to expire at the same price, then consumers prefer goods with longer shelf lives, but goods about to expire do not get sold and are thrown away
Solution Approach 1:
The system dynamically adjusts pricing based on real-time shelf state and expiration dates. Prices are not static but change continuously according to how much time remains before expiration, creating a dynamic incentive structure that encourages consumers to purchase items nearing expiration while still maintaining acceptable quality standards.
Solution Approach 2:
The system changes the price parameter based on the shelf state parameter (time remaining until expiration). By modifying the price parameter in response to changes in shelf life, the system creates economic incentives that align consumer behavior with waste reduction goals without compromising food safety or quality.
2Productivity
If traditional static pricing methods are used for grocery items, then pricing is simple and consistent, but revenue optimization and waste reduction are not achieved
Solution Approach 1:
The system uses automated machine learning models and algorithms that independently analyze shelf state data, expiration dates, and market conditions to determine optimal pricing. The system serves itself by making pricing decisions without requiring manual intervention, thereby increasing revenue optimization capability while managing complexity through automation rather than human processes.
Solution Approach 2:
The system continuously monitors shelf state, sales data, and expiration dates, then uses this feedback to adjust pricing in real-time. The machine learning models learn from historical data and continuously improve pricing decisions based on observed outcomes, creating a closed-loop system that optimizes revenue while adapting to changing conditions.
3Productivity
If goods about to expire are discounted heavily, then sales of these items increase, but revenue per item decreases
Solution Approach 1:
The system applies discounts selectively and proportionally based on the specific shelf state and time remaining until expiration. Rather than applying uniform heavy discounts to all near-expiration items, the system calculates optimal discount levels that are sufficient to stimulate sales while preserving as much revenue as possible. This partial action approach avoids excessive discounting when it is not necessary.
Solution Approach 2:
The system dynamically adjusts the discount parameter based on multiple factors including time remaining, original price, demand elasticity, and competitive conditions. By changing the discount parameter in response to these variables, the system optimizes the balance between sales volume and revenue per item, applying just enough discount to move the product without leaving money on the table.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system dynamically adjusts prices to encourage the sale of items nearing expiration, reducing waste and optimizing revenue by aligning prices with the shelf state and future stock levels, thereby enhancing sales and minimizing losses.
Implementation Method 1
The readable identification tag is a Radio-Frequency Identification (RFID) tag, a barcode, a matrix barcode, and/or a data-enabled barcode.
Data Source
AI summary
Systems for determining a price of goods, reducing food waste, optimizing markdowns for the goods, and controlling prices of the goods in a retail setting are described. A scanner scans a readable identification tag affixed to a good during a time period. A pricing engine of at least one server queries, dynamically and in real-time, a database to identify, from the readable identification tag, the good and the information associated with the good. The pricing engine then applies algorithms to the identified good to calculate a price of the good and modify the calculated price of the good during the time period to optimize a target function. The optimization of the target function depends on price-calculation factors associated with a shelf-state of the good and associated with a future stock of the good. At least one calculated price based on the expiration date of the good is displayed.