Dynamic Pricing System for Perishable Goods
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Solution Overview
Problem
Existing pricing methods for perishable goods often result in incorrect pricing due to predetermination, leading to either rapid sell-out or unsold products, especially for items like transportation tickets, event tickets, and accommodations, as they fail to account for real-time demand effectively.
Innovation Solution
A dynamic pricing system that uses a programmed data processing apparatus to monitor time parameters, periodically update prices, offer temporarily fixed prices in response to buying interest, and adjust prices based on accepted or rejected orders to optimize sales before perishable goods become worthless.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predetermined prices are set for perishable goods, then pricing simplicity is maintained, but pricing accuracy deteriorates leading to incorrect pricing
Solution Approach 1:
The patent implements dynamic pricing by continuously updating prices based on real-time demand signals and time remaining before perishability. The system transitions from static predetermined prices to dynamic prices that adapt to changing market conditions, resolving the contradiction between pricing simplicity and pricing accuracy.
Solution Approach 2:
The system incorporates feedback mechanisms by monitoring customer interest and purchase behavior in real-time, then using this feedback to adjust prices dynamically. This closed-loop approach ensures pricing accuracy while maintaining operational simplicity through automated decision-making.
2Productivity
If prices are set too low for perishable goods, then sales velocity increases, but revenue loss occurs due to rapid sell-out
Solution Approach 1:
The system dynamically adjusts prices based on real-time demand assessment, allowing prices to be high when demand is strong (maintaining revenue) and low when demand is weak (maintaining sales velocity). This resolves the contradiction by making sales velocity and revenue loss contingent on actual market conditions rather than fixed price points.
Solution Approach 2:
The patent changes the price parameter dynamically based on multiple factors including time remaining before perishability, current demand level, and sales velocity. This parameter adjustment strategy optimizes the trade-off between sales velocity and revenue loss by adapting prices to current system state.
3Loss of energy
If prices are set too high for perishable goods, then revenue potential increases, but unsold products increase leading to waste
Solution Approach 1:
The system dynamically lowers prices when demand is weak or time remaining is limited, preventing unsold products while capturing revenue potential when demand is strong. This resolves the contradiction by making the price adaptive rather than fixed, allowing the system to navigate the trade-off between revenue and waste based on real-time conditions.
Solution Approach 2:
The system performs preliminary price adjustments based on predicted demand patterns and time-to-perishability, preventing the occurrence of unsold products before they happen. By anticipating demand shortfalls and adjusting prices proactively, the system captures revenue potential while avoiding waste.
4Measurement precision
If real-time demand monitoring is implemented, then pricing accuracy improves, but system complexity increases
Solution Approach 1:
The system implements self-service pricing by using automated algorithms to monitor demand, assess market conditions, and adjust prices without human intervention. This resolves the contradiction by automating the complex monitoring and adjustment processes, maintaining pricing accuracy while minimizing the operational complexity burden on users.
Solution Approach 2:
The patent replaces manual pricing mechanisms with automated computational systems that continuously monitor demand and adjust prices. This substitution of mechanical/manual processes with automated electronic systems improves pricing accuracy while managing system complexity through algorithmic decision-making.
Data Source
AI summary
Techniques for dynamically pricing perishable goods. The objective is to sell all items before any remaining items become worthless. The method comprises: monitoring a time parameter T corresponding to period T0 to Tn; if T<Tn, periodically updating current price Pcurrent for an item; in response client's interest, offering a temporarily fixed price Pfreeze=Pcurrent for a freeze period ΔTfreeze; updating Pcurrent in response to offering the fixed price Pfreeze; accepting a purchase order for the item at Pfreeze if purchase order received within the freeze period ΔTfreeze; rejecting the purchase order after the freeze period ΔTfreeze; updating Pcurrent in response to receiving an accepted and/or rejected order; and repeating at least some of the above the acts until T equals maximum time Tn.