Dynamic Supply Chain Pricing via Multi-Party Feedback
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Solution Overview
Problem
Current supply chain operations for pricing in e-commerce settings are inflexible and do not effectively account for logistics costs, leading to inefficiencies and higher costs for both sellers and buyers.
Innovation Solution
An intelligent supply chain optimization system that uses processors to generate dynamic offers based on user profiles, multi-party entity feedback loops, and transaction agreement fulfillment requirements, incorporating blockchain for secure transaction ledgers and machine learning for cost-advantageous options and buyer preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current supply chain operations use fixed pricing models, then transaction simplicity is maintained, but supply chain efficiency and cost optimization are reduced
Solution Approach 1:
The patent implements dynamic pricing that adapts to real-time supply chain conditions, user profiles, and transaction parameters. The system continuously adjusts pricing based on feedback loops and changing conditions, transforming static pricing into a dynamic optimization mechanism that improves supply chain efficiency without requiring manual intervention for each adjustment.
Solution Approach 2:
The system employs multi-party feedback loops where pricing decisions are continuously refined based on feedback from buyers, sellers, and supply chain participants. This feedback mechanism enables the system to learn from transactions and optimize pricing strategies over time, improving efficiency while maintaining adaptive complexity that responds to actual system conditions.
2Measurement precision
If logistics costs are not accounted for in pricing, then transaction speed is maintained, but cost accuracy and supply chain optimization are reduced
Solution Approach 1:
The system performs preliminary calculations of logistics costs and transaction parameters before finalizing pricing agreements. By pre-computing cost components and incorporating them into the pricing model in advance, the system ensures accurate cost measurement without requiring time-consuming calculations during the actual transaction execution phase.
Solution Approach 2:
The pricing system automatically calculates and incorporates logistics costs without requiring manual input or external computation. The system self-determines cost components based on integrated supply chain data, eliminating the need for separate cost assessment processes and maintaining transaction speed while improving cost accuracy.
3Adaptability or versatility
If interactive pricing with multiple parameters is implemented, then pricing optimization is improved, but negotiation complexity and system requirements increase
Solution Approach 1:
The patent creates a universal pricing framework that handles multiple transaction types, supply chain scenarios, and negotiation parameters through a single integrated system. This multi-functional approach allows the system to adapt to diverse pricing needs without requiring separate systems for each scenario, maintaining versatility while managing complexity through unified architecture.
Data Source
AI summary
Intelligent classification for product pedigree identification are presented. A transaction agreement request may be received from a user. A revised transaction agreement request may be generated based on one or more user profiles, a multi-party entity feedback loop, one or more constraints relating to the transaction agreement request, and a transaction agreement fulfillment requirements of the entity.