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

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ML-augmented automation enables significant enhancements in equipment uptime and delivery accuracy, enhancing overall efficiency.
Improvements in inventory management lead to reduced operational costs while ensuring responsive supply chains in volatile markets.
Comprehensive case study analysis validates performance improvements across manufacturing and logistics through effective integration of technologies.
Challenges in data integration and model interpretability must be addressed to facilitate scalable and ethical AI deployments.

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Framework integrates machine learning models with robotic process automation, improving supply chain efficiency and decision-making.

Pullaiah Babu Alla (2025) studied this question.

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