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, as consumers are less likely to purchase items nearing expiration, leading to unsold goods being discarded, necessitating a method to enhance sales and reduce waste through dynamic pricing.
Innovation Solution
A system utilizing a dynamic pricing engine with a computing device, scanner, and readable identification tags (RFID, barcode, or matrix barcode) applies algorithms to calculate and modify prices based on various factors such as expiration date, demand, and advertising campaigns to optimize revenue and reduce waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If goods are priced at a fixed price throughout their shelf life, then pricing simplicity is maintained, but food waste increases as consumers are less likely to purchase items nearing expiration
Solution Approach 1:
The patent implements dynamic pricing by continuously adjusting prices based on real-time factors including remaining shelf life, demand patterns, competitor pricing, and promotional campaigns. The system transitions from static fixed pricing to dynamic adaptive pricing, where prices automatically change as goods approach expiration dates, thereby incentivizing consumers to purchase near-expiration items and reducing food waste.
Solution Approach 2:
The pricing system incorporates multiple feedback loops that monitor consumer purchasing behavior, inventory levels, time remaining until expiration, and external market conditions. This feedback information is processed to automatically adjust prices, creating a closed-loop system that responds to actual market dynamics and consumer preferences rather than relying on predetermined fixed prices.
2Productivity
If dynamic pricing is implemented to reduce food waste, then revenue optimization is achieved, but system complexity increases
Solution Approach 1:
The pricing engine is designed as a universal multi-functional system that simultaneously performs multiple tasks: calculating optimal prices based on shelf life, monitoring demand patterns, tracking competitor pricing, managing promotional campaigns, and generating revenue forecasts. This consolidated multi-functional approach enables comprehensive revenue optimization without requiring separate specialized systems for each function.
Solution Approach 2:
The system optimizes revenue by dynamically changing multiple parameters including price points, discount levels, promotional intensity, and timing of price adjustments. These parameter changes are continuously adjusted based on real-time data inputs, allowing the system to adapt to varying market conditions and maximize revenue while minimizing waste across different product categories and time periods.
3Loss of substance
If prices are adjusted dynamically based on multiple factors, then waste reduction is achieved, but calculation complexity increases
Solution Approach 1:
The pricing calculation process is segmented into distinct modular components: shelf-life-based pricing calculations, demand-pattern analysis, competitor-pricing integration, and promotional-campaign coordination. Each segment handles specific factors independently, and their results are combined to determine the final price. This segmentation reduces calculation complexity by breaking down the complex multi-factor pricing problem into manageable independent modules.
Solution Approach 2:
The system performs preliminary calculations and preparations in advance, such as pre-establishing pricing algorithms for different product categories, pre-analyzing historical demand patterns, and pre-configuring promotional strategies. These preliminary actions reduce the complexity of real-time calculations by having reference data and computational frameworks ready before actual pricing decisions are needed.
Data Source
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.