Evidence map›Paper›PMID 42050052›Full record

ArticleScientific reports2026

A multi-agent large language model framework for intelligent vendor evaluation and risk-aware procurement decisions.

Amey Joshi, Mangal Singh, Deepak Parashar, Mahesh Singh

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Amey JoshiSymbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.
Mangal SinghDepartment of E&TC, Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.
Deepak ParasharManipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India. deepak.parashar@manipal.edu.
Mahesh SinghDepartment of Robotics and Automation, Symbiosis Institute of Technology, Pune Campus, Symbiosis International, (Deemed University), Pune, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The process of vendor selection is a multi-dimensional and intricate task that requires analyzing the financial health, amount of risk, industry standards, and the mood in the marketplace. The use of traditional methods that consider individual data points or fixed metrics usually leads to less than optimal procurement decision-making. The current paper introduces a new Multi-Agent Large Language Model (LLM) framework that will improve the process of vendor evaluation based on the analysis of both structured and unstructured data. Its framework uses specialized agents that target different aspects of vendor evaluation: financial analysis, risk profiling, sentiment monitoring, and industry benchmarking. Structured indicators that financial agents consider include liquidity, profitability and solvency. Exposure to geopolitical, operational, and compliance risks is assessed by risk agents. Sentiment agents identify sentiment based on the news articles, reviews, and social media to determine the perception of the people and stakeholders. Agents in benchmarking compare vendors to industry standards to determine outliers and best-in-class vendors. The combination of these insights provides a data-driven yet context-aware procurement recommendation generated by the framework. The qualitative assessments are used to complement quantitative metrics, allowing procurement teams to make informed and holistic decisions. The system does not only help in identifying the most appropriate vendors but also assists in managing relationships with suppliers in the long term whereby the system alerts of any risk and performance deviations in real time. The method enables organizations to concentrate vendor selection in tandem with strategic objectives, reduce risk, and enhance the long-term value. The suggested multi-Agent LLM scheme is a major development in smart procurement systems, which enhance more resilient and value-oriented supply chains.

Indexed as

Industry benchmarkingLarge language models (LLMs)Multi-agent systemsRisk assessmentSentiment analysisVendor selection

Identifiers

PMID42050052
PMCPMC13316148

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.