The rapid evolution of global supply chains, intensified by disruptions such as the COVID-19 pandemic and geopolitical tensions, has necessitated the development of sophisticated performance measurement frameworks that can adapt to dynamic market conditions. This research investigates how dynamic, multi-criteria performance models can enhance supplier relationship management (SRM) and operational agility in Indian manufacturing supply chains. Through a comprehensive analysis of current literature and industry practices, this study proposes an integrated framework that combines real-time performance monitoring, multi-criteria decision-making methodologies, and dynamic capability development to optimize supplier relationships and enhance operational responsiveness. The study reveals that traditional static performance measurement systems are inadequate for addressing the complexities of modern supply chains operating in emerging markets like India. Dynamic performance models that incorporate multiple evaluation criteria—including quality, cost, delivery, sustainability, and innovation—provide a more holistic approach to supplier evaluation and relationship management. The research demonstrates that organizations implementing such frameworks experience significant improvements in supply chain agility, cost efficiency, and overall operational performance. Key findings indicate that the integration of technologies such as artificial intelligence, machine learning, and real-time analytics into performance measurement systems enables organizations to develop predictive capabilities and proactive supplier management strategies. The study concludes with practical recommendations for Indian manufacturing organizations seeking to implement dynamic, multi-criteria performance models to enhance their supplier relationships and operational agility.