To study & optimize supplier selection in the purchasing cycle

Abstract

In today's competitive business environment, optimizing supplier selection processes is crucial for enhancing operational efficiency and achieving strategic objectives within the purchasing cycle. This study explores the multifaceted dimensions of supplier selection, emphasizing the integration of quantitative and qualitative criteria to improve decision-making. By employing advanced analytical techniques, such as multi-criteria decision analysis (MCDA) and machine learning algorithms, we propose a comprehensive framework that enables organizations to evaluate potential suppliers based on factors such as cost, quality, reliability, and sustainability. The research highlights the importance of aligning supplier selection with organizational goals and market dynamics, while also considering the impact of digital transformation on procurement practices. Through case studies and empirical data, we demonstrate the effectiveness of the proposed optimization model in reducing procurement risks, enhancing supplier relationships, and ultimately driving value creation. The findings underscore the necessity for organizations to adopt a systematic approach to supplier selection, ensuring that they remain agile and competitive in an ever-evolving marketplace.