Supplier selection is a fundamental aspect of supply chain management (SCM), playing a crucial role in enhancing operational efficiency, cost management, and overall performance. This study evaluates supplier performance using advanced data analytics and predictive modeling techniques. Leveraging a comprehensive dataset sourced from Microsoft Ignite, key performance metrics such as defect quantity, downtime, and categorical identifiers were analyzed to uncover trends and actionable insights. Visualizations created using Power BI dashboards highlighted critical performance trends, guiding targeted interventions for operational improvement. Various methodologies, including logistic regression, multinomial and ordinal regression, random forest models, and Gaussian Naive Bayes, were applied using Minitab, Power BI, and Python to explore the relationships between predictor variables and performance outcomes. The initial analysis revealed limitations in linear models due to weak correlations among categorical variables, prompting the adoption of multinomial and ordinal logistic approaches for improved classification accuracy. This study demonstrates how robust modeling and visualization frameworks can inform strategic decision-making in supply chain management, fostering sustainable and adaptive supplier relationships. Despite these advancements, the findings emphasize the necessity for further refinement to address complex interdependencies and enhance predictive accuracy.