Machine Learning Applications in U.S. Manufacturing: Predictive Maintenance and Supply Chain Optimization

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Abstract

Machine learning (ML) technologies are swiftly coming into the U.S. manufacturing industry to solve the old issues of equipment upkeep and supply chain management. There is a transformative research study about ML and its application to improve predictive maintenance and plan inventory and logistics decisions. The study makes use of actual data and variable set manufacturing data on a regional basis, and then uses tree-based ML techniques (XGboost, random forest) to forecast the failure of equipment and supply blockades. The methodology involves elaborate feature engineering as well as breakdown of demand with model calibration to account lead-time variability and heterogeneity of operations. It is also observed that, compared to conventional regression methods, XGBoost is better in predictive maintenance and has higher adaptability to nonlinear trends in demand prediction. Additionally, the paper examines model robustness, distribution regional impact, as well as anomaly identification in order to demonstrate how possible ML is to be utilized to reduce operational downtime and enhance inventory turnover. The most significant implementation issues are discussed, such as integrating previous generation equipment, data imbalance and cybersecurity. This paper ends with the discussion of what can be expected in the future in terms of Edge AI and Federated Learning and the importance of those technologies in securing and sustainable smart manufacturing systems. This study will provide practical results to manufacturers aiming to transform to the smart and resilient models and the data driven manufacturing

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