Authors:
Lakshmi Narasimha Raju Mudunuri

Addresses:
1Department of Senior Business Systems Design Specialist-Refining Systems Information Services, Valero Energy Corporation, Texas, United States of America. raju.mudunuri@valero.com1

Abstract:

In today’s fast-paced business environment, the ability to quickly and accurately identify suitable vendors is crucial for maintaining competitive advantage. Traditional vendor selection processes can be time-consuming and prone to errors, leading to suboptimal partnerships. This paper explores an AI-powered approach to vendor matchmaking, leveraging machine learning algorithms and big data analytics to enhance decision-making accuracy and efficiency. The proposed method involves a comprehensive analysis of historical vendor performance data using advanced machine learning models to evaluate vendors based on multiple criteria, including performance history, cost-effectiveness, and compliance with regulatory standards. Tools such as Python for data processing, sci-kit-learn for model development, and Matplotlib for data visualization were utilized. The dataset, spanning five years and including data on over 500 vendors, was sourced from internal business records and external market intelligence. Our findings suggest that AI-powered matchmaking significantly improves the quality of vendor selection, reducing both time and cost while increasing overall satisfaction and performance. The study underscores the transformative potential of AI in streamlining business operations and fostering strategic partnerships.

Keywords: Artificial Intelligence; AI-Powered Matchmaking; Vendor Selection; Machine Learning; Big Data Analytics; Business Efficiency; Accurate and Relevant; Matplotlib for Data.

Received on: 22/08/2023, Revised on: 19/10/2023, Accepted on: 03/12/2023, Published on: 09/03/2024

DOI: 10.69888/FTSIN.2024.000155

FMDB Transactions on Sustainable Intelligent Networks, 2024 Vol. 1 No. 1, Pages: 27-39

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