End-to-End Supply Chain Optimization via ML-Augmented RPA and Predictive Maintenance Models

Abstract

Modern supply chains face unprecedented challenges from increasing complexity, volatile market conditions, and the need for rapid adaptation to disruptions, making traditional rule-based automation systems inadequate for contemporary operational demands. This article presents a comprehensive framework that integrates machine learning models with robotic process automation to create intelligent, self-adapting supply chain optimization systems capable of predictive decision-making and autonomous workflow management. The article combines real-time sensor data, transactional information, and predictive analytics using advanced algorithms, including Random Forest, Gradient Boosting, and Long Short-Term Memory networks, to enable proactive maintenance scheduling, inventory optimization, and logistics coordination. Implementation of the ML-augmented RPA system demonstrates significant improvements in equipment uptime, delivery accuracy, and inventory management while reducing operational costs and enhancing organizational responsiveness to market fluctuations. The article addresses critical challenges in data integration, model interpretability, and system scalability while maintaining compliance with regulatory requirements and ethical AI principles. Key contributions include the development of a scalable automation architecture, demonstration of successful cross-functional integration across manufacturing and logistics operations, and validation of performance improvements through comprehensive case study analysis. The article reveals that organizations implementing ML-enhanced automation achieve substantial operational benefits, including reduced manual intervention, improved decision-making accuracy, and enhanced supply chain visibility. However, successful deployment requires careful attention to data quality management, organizational change processes, and continuous model monitoring to maintain system effectiveness over time. This article establishes a foundation for future research in intelligent supply chain automation and provides practical guidance for organizations seeking to leverage artificial intelligence for competitive advantage in dynamic market environments.