Dynamic Pricing Engine for Perishable Goods
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Solution Overview
Problem
The grocery industry faces significant losses due to food spoilage, as consumers are less likely to purchase items nearing expiration, leading to unsold goods being discarded. Existing pricing methods fail to effectively address this issue by not optimizing prices based on real-time factors to reduce waste and increase sales.
Innovation Solution
A dynamic pricing system utilizing a computing device with a dynamic pricing engine, coupled with a scanner and readable identification tags (RFID, barcode, or matrix barcode), applies algorithms (such as reinforcement learning, deep learning, or machine learning) to adjust prices in real-time based on various factors like expiration date, demand, and shopping patterns to optimize revenue and reduce waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If dynamic pricing is implemented to reduce food waste, then loss of substance decreases, but device complexity increases
Solution Approach 1:
The dynamic pricing system automatically adjusts prices based on real-time data from scanners, RFID tags, and inventory management without requiring manual intervention. The system self-regulates pricing to optimize waste reduction while managing its own complexity through automated algorithms and machine learning models.
Solution Approach 2:
The pricing system integrates multiple functions including inventory tracking, demand prediction, price optimization, and waste reduction into a single unified platform. By combining these functions, the system reduces overall complexity compared to having separate systems for each function while achieving waste reduction goals.
2Productivity
If real-time price adjustments are made based on multiple factors, then productivity increases, but measurement precision requirements increase
Solution Approach 1:
The system continuously collects data from scanners, RFID tags, and sales transactions to monitor actual pricing outcomes. This feedback loop allows the system to refine its price-calculation factors over time, improving measurement precision while maintaining high productivity through automated real-time adjustments.
Solution Approach 2:
The system dynamically adjusts multiple parameters including price, time remaining until expiration, demand forecasts, and inventory levels. By changing these parameters in real-time based on weighted algorithms, the system achieves high productivity while managing measurement precision requirements through continuous optimization.
3Loss of time
If machine learning algorithms are used to optimize pricing, then loss of time decreases, but device complexity increases
Solution Approach 1:
The system pre-trains machine learning models with historical sales data, inventory patterns, and consumer behavior before deployment. This preliminary action allows the algorithms to make rapid real-time pricing decisions without requiring complex computations during actual pricing events, thus reducing time loss while managing algorithmic complexity through prior preparation.
Solution Approach 2:
The system replaces manual pricing decisions and traditional rule-based algorithms with machine learning models that automatically analyze multiple factors and determine optimal prices. This substitution reduces the time required for price determination while the complexity is managed through automated model selection and deployment infrastructure.
Data Source
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AI summary
Methods, systems, and computing devices for determining a price of a good during a time period are described. The method includes scanning, via a scanner coupled to a computing device or a server, a readable identification tag affixed to the good and querying the computing device or the server to identify, from the readable identification tag, the good and information associated with the good. The information comprises one or more price-calculation factors assigned to the good. The method further includes applying an algorithm of a dynamic pricing engine to the identified good to: calculate a price of the good and modify the calculated price of the good to optimize a target function. The optimization depends on the one or more price-calculation factors. The method further includes transmitting the optimized price of the good to a display for display to a customer.