AI Procurement System for Dynamic Resource Selection

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Solution Overview

Problem

Buyers in procurement processes often face asymmetrical market knowledge, lack of negotiation leverage, and inflexible procurement plans, leading to suboptimal resource selection and cost savings due to limited supplier engagement and inaccurate market data.

Innovation Solution

A computer system employing a predictor engine, extractor engine, and acquirer engine for iterative resource selection, utilizing machine learning to stack rank resources, generate category trees, and initiate reverse auctions to optimize resource acquisition based on performance enhancements and feedback.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If buyers engage with a limited number of suppliers to simplify procurement processes, then the procurement process becomes easier to manage, but negotiation leverage and cost savings opportunities are reduced

Engineering Contradiction:

Improveprocurement process managementVSAvoidnegotiation leverage

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The system enables automated self-service procurement through AI agents that independently negotiate, evaluate suppliers, and execute transactions without requiring extensive human intervention in each negotiation, thus maintaining ease of operation while expanding supplier engagement

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically adjusts procurement parameters such as supplier selection criteria, negotiation strategies, and contract terms based on real-time market data and performance metrics, enabling adaptive negotiation leverage across multiple suppliers while maintaining process simplicity

Inventive Principle:

Principle #35Parameter changes

2Reliability

If suppliers structure inflexible contracts to reduce buyer maneuverability, then supplier control and predictability are improved, but buyer adaptability and ability to shop elsewhere are reduced

Engineering Contradiction:

Improvesupplier controlVSAvoidbuyer maneuverability

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system implements dynamic contracts with adjustable parameters that can be modified in real-time based on performance metrics, market conditions, and mutual agreement, replacing static inflexible contracts with adaptive agreements that maintain reliability while enabling buyer maneuverability

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system continuously monitors contract performance and provides feedback loops that enable both parties to adjust terms based on actual outcomes, ensuring supplier control through performance verification while maintaining buyer adaptability through data-driven renegotiation opportunities

Inventive Principle:

Principle #23Feedback

3Ease of operation

If buyers rely on supplier-provided market information, then the procurement process is simpler, but information accuracy and market knowledge are compromised

Engineering Contradiction:

Improveinformation gatheringVSAvoidmarket knowledge accuracy

Core Design Contradiction:

Ease of operationVSLoss of information

Solution Approach 1:

The system introduces an AI intermediary that independently gathers, verifies, and analyzes market information from multiple sources, acting as an unbiased mediator between buyers and suppliers to provide accurate market intelligence without requiring buyers to conduct extensive independent research

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs multiple information-gathering functions simultaneously including market pricing analysis, supplier performance evaluation, contract term benchmarking, and negotiation strategy development, providing comprehensive accurate market knowledge through a single integrated platform

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Adaptability or versatility

If buyers use an unstructured impromptu negotiation process, then the negotiation process is more flexible and responsive, but consistency and efficiency are reduced

Engineering Contradiction:

Improvenegotiation flexibilityVSAvoidnegotiation efficiency

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The system performs preliminary actions by pre-analyzing market data, pre-evaluating supplier capabilities, and pre-determining optimal negotiation strategies before actual negotiations begin, enabling structured yet flexible negotiations that are both efficient and adaptive

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS10979315B1Machine learning feedback loop for maximizing efficiency in transaction flow

Publication Date: 2021.04.13 AUCTIONIQ LLC

AI summary

A computer system includes a predictor engine, an extractor engine, and an acquirer engine. The predictor engine obtains resource data and stack ranks the data based on an expenditure amount each resource consumed. Resources are categorized into a tree structure. The predictor engine compiles a list identifying predicted performance enhancements and generates a notification detailing the list. In response determining that the predicted performance enhancements satisfy a threshold, the extractor engine generates an inventory of resources and rearranges the inventory based on the categories. The inventory is used to generate a baseline contrasting performance requirements with performance usages. The acquirer engine selects a resource to be used for a category based on the baseline, a list of potential resources, and the performance requirements. The acquirer engine generates or modifies an executable document. The computer system modifies these operations based on feedback to iteratively improve this transactional flow.