Autonomous Procurement System Using AI Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional procurement systems require significant human intervention and are limited in automating the entire procurement process, leading to inefficiencies and increased costs due to the need for skilled professionals and manual authentication processes.
Innovation Solution
An autonomous procurement system that uses AI, machine learning, and blockchain to automate the procurement process from creating pro forma contracts to selecting winning bidders, eliminating the need for human intervention by performing syntax, semantics, and pragmatic analyses on user inputs to create a variables matrix, selecting bidders, issuing solicitations of interest, evaluating bids, and creating final contracts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional procurement systems use manual processes with human intervention, then procurement integrity and policy compliance can be maintained through human judgment, but the system requires significant human resources, increases payroll costs, and reduces efficiency
Solution Approach 1:
The procurement system performs self-service through autonomous AI agents that automatically create pro forma contracts, analyze user inputs through syntax/semantics/pragmatics analysis, select bidders, issue solicitations, evaluate bids, and finalize contracts without human intervention, eliminating the need for manual procurement processes while maintaining efficiency
Solution Approach 2:
The patent replaces manual mechanical procurement processes with an automated digital system using AI and machine learning algorithms. The system substitutes human judgment and manual authentication with computational analysis, including natural language processing of procurement requests and automated evaluation of bidder proposals
2Extent of automation
If the system automates the entire procurement process without human intervention, then payroll costs are reduced and efficiency is improved, but the system complexity and difficulty of implementation increase
Solution Approach 1:
The autonomous procurement system performs multiple functions within a single integrated platform: receiving procurement requests, performing syntax/semantics/pragmatics analysis, creating pro forma contracts, selecting bidders, issuing solicitations, evaluating bids, and finalizing contracts. This multi-functional approach consolidates what would otherwise require separate systems into one universal autonomous platform
Solution Approach 2:
The system uses AI language models and machine learning algorithms as intermediaries between user inputs and procurement outcomes. These intermediaries translate natural language procurement requests into structured variables matrices, automatically interpret policy requirements, and facilitate automated decision-making without direct human intervention
3Reliability
If manual authentication processes are used, then procurement integrity is maintained through human verification, but the process time increases and efficiency decreases
Solution Approach 1:
The system replaces manual authentication with automated digital verification processes. AI algorithms automatically verify bidder qualifications, validate contract terms against organizational policies, and authenticate procurement requests through computational checks, eliminating the need for human verification while maintaining integrity and reducing time
Solution Approach 2:
The autonomous system implements continuous feedback loops where AI agents automatically monitor procurement processes, validate data integrity, and adjust evaluations based on predefined criteria and organizational policies. This automated feedback mechanism maintains procurement integrity without requiring manual intervention at each step
Data Source
AI summary
Systems and methods include a computer-implemented method for autonomous procurement. User inputs defining a procurement request are received through a user interface. A pro forma contract is created, including performing a syntax analysis, a semantics analysis, and a pragmatic analysis on the user inputs to create a variables matrix of variables defining the pro forma contract. Bidders for the pro forma contract are selected, including creating a bidder list identifying suppliers who are to be invited to participate in bidding or submitting a quotation. A statement of work (SOW) is created based on the variables defining the pro forma contract. Solicitations of interest including the SOW are issued by the autonomous procurement system to bidders identified in the bidder list. Bidding parties are evaluated to identify qualified bidders. A winning bidder is selected from the bidding parties, and a final contract for the winning bidder is created.