World Research of Business Administration Journal
Vol.5 No.2 July 2025
DOI https://www.doi.org/10.56830/WRBA07202502
Authors
Pradnya Markale
Pratik Dahule
Abstract
In the modern business landscape, supply chain management is crucial for achieving operational efficiency and aligning external partnerships with organizational goals. To effective supply chain management, it is imperative to establish vendor performance management system and effective performance metrics. This paper explores the challenges and how to maximize the value addition via vendor management by integrating structured methodologies such as vendor performance scorecards with advanced data analytics. Vendor scorecards offer a standardized and systematic approach to defining key performance metrics (KPIs) that align business objectives, ensuring consistent and objective evaluation. This study presents a utility business case for designing these scorecards, emphasizing the importance of selecting the right KPIs and scoring mechanism. Furthermore, the paper discusses how data analytics can enhance performance evaluations by leveraging historical data to identify trends, benchmark vendor performance, and identify areas for improvement. The Monte Carlo method is introduced as an optimization tool for simulating individual vendor performance and refining benchmarking metrics, providing deeper insights into performance variability. Additionally, the role of ML and AI is discussed to dynamically adjust the weightage of KPIs on the vendor scorecard based on real-time data and business priorities, enabling adaptive and responsive performance management. The integration of these technologies offers a data driven, comprehensive approach to evaluating vendor performance, facilitating improved decision making and long-term strategic alignment.
Keywords: Vendor Performance Management, Vendor Scorecards, Key Performance Indicators (KPIs), Supply Chain Management, Data Analytics, Artificial Intelligence (AI), Machine Learning (ML), Monte Carlo Simulation, Adaptive KPI Weighting.
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