AI Entity Selection Module for Procurement Simulations
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Solution Overview
Problem
Organizations face challenges in selecting the optimal entity for procuring goods and services due to the complexity of manually evaluating multiple factors such as value, quality, timeliness, and organizational constraints across multiple entities and auctions.
Innovation Solution
A selection module that utilizes a Name Entity Recognition (NER) model and an Artificial Intelligence (AI)/Machine Learning (ML) model to parse user queries, extract key information, and predict desired entity characteristics, thereby automating the generation of simulations and recommending entity selections based on weighted characteristic parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of multiple simulations is performed to select an entity, then the organization can evaluate multiple factors including value, quality, and timeliness, but the process requires significant effort and time
Solution Approach 1:
The patent replaces the manual mechanical analysis process with an automated computer-based system that uses machine learning models and algorithms to evaluate entity proposals, extract characteristics, and generate simulations automatically, eliminating the need for manual intervention while maintaining comprehensive evaluation
Solution Approach 2:
The patent introduces an intermediary automated selection system that acts as a mediator between the organization's procurement needs and multiple entity proposals, using AI/ML models to objectively evaluate and compare entities based on multiple factors without direct human analysis
2Reliability
If the organization considers multiple factors such as value, quality, and timeliness in entity selection, then the selection quality improves, but the complexity of the selection process increases
Solution Approach 1:
The patent segments the complex selection process into distinct automated components: entity characteristic extraction, proposal evaluation, simulation generation, and selection recommendation, where each component handles specific tasks independently, reducing overall process complexity while maintaining comprehensive evaluation
Solution Approach 2:
The patent transforms multiple qualitative evaluation factors (value, quality, timeliness) into quantifiable parameters that can be processed by AI/ML models, converting complex multi-criteria evaluation into a standardized parameter-based assessment system
3Adaptability or versatility
If multiple entities bid multiple times for multiple items in auctions, then the organization can procure diverse goods and services, but it becomes increasingly difficult to select the optimal entity
Solution Approach 1:
The patent enables the selection system to automatically manage and evaluate multiple entities bidding for multiple items without human intervention, where the AI/ML models self-adjust and re-evaluate entities based on their performance across different auctions and items, simplifying the selection process while maintaining procurement flexibility
Data Source
AI summary
According to some embodiments, systems and methods are provided including a memory, a processing unit and program code to: create an event, the event including an item value for a given quantity of an item provided by each of a plurality of entities; receive an event target; extract an entity identifier from the event target for each entity from the event; generate one or more simulations based on a plurality of characteristic parameter constraints and the event target; generate a characteristic parameter value for each of the plurality of characteristic parameters via execution of a machine learning model trained with prior values of characteristic parameters for the plurality of entities; scale the generated characteristic parameter values to a common unit of measure; generate an output, including a quantity distribution of the given quantity to at least one of the plurality of entities. Numerous other aspects are provided.