Assessing The Impact Of Pega's Robotic Process Automation On Supply Chain Management Efficiency

Today successful business success relies on efficient supply chain management (SCM) in the ever-changing global marketplace. Traditional SCM processes are subject to inefficiency, manual intervention, destruction, etc. This paper evaluates the contribution of Pega’s Robotic Process Automation (RPA) in effecting transformation in SCM efficiency. Powered by AI and Machine Learning, Pega’s RPA streamlines processes like order management, inventory tracking, demand forecasting and so much more, moving supply chains from reactive to predictive. This was a mixed methods approach combining case studies, surveys, and interviews with industry professionals. Results indicate significant operational improvements: This resulted in a 40 percent reduction in processing times, an 85 percent decrease in manual errors, and a 30 percent reduction in operational costs. Another case example illustrates how Pega’s RPA improves scalability, decreases errors and is cost-efficient. While the technology is beneficial, the initial costs are high, there is workforce adaptation, and enough makes the integration challenging. The findings underscore the need for strategic planning and workforce readiness for implementation success. Finally, the study concludes that Pega’s RPA is not just another tool for automation, but an investment in supply chain modernization that provides actionable insights to organizations and serves as a foundation for future RPA research on scalability and integration with new technology. Keywords: Supply Chain Management (SCM), Robotic Process Automation (RPA), Pega, Operational Efficiency, AI and Machine Learning, Cost Reduction, Scalability