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Using Predictive Analytics to Enhance Productivity and Innovation in the Advanced U.S. Manufacturing Sectors
Abstract
The intersection of predictive analytics, Industrial Internet of Things (IIoT), and cloud-based systems is also changing sophisticated manufacturing in the United States. With increased global competition and the emergence of operational resilience as a strategic goal, manufacturers are turning toward data-driven systems, increasing productivity and driving innovation. This paper investigates the operational performance and innovation of advanced sectors of the U.S. manufacturing industry with the help of predictive analytics by using the Smart Manufacturing IoT-Cloud Monitoring Dataset which includes 100,000 real-time sensor observations of 50 industrial machines. The theoretical framework of Industry 4.0 and resource-based perceptions of technological capability make use of the notion of machine learning models that forecast the maintenance demand and approximate the remaining useful life dependent on sensor variables and indicators of anomalies such as temperature, vibration, humidity, pressure, energy consumption, and the risk of downtime. The algorithms of supervised learning, which are Random Forest and Gradient Boosting, are applied to assess the predictive accuracy based on ROC-AUC, F1-score, and RMSE values. The results of empirical studies indicate that predictive analytics can substantially increase the effectiveness of failure detection and minimize unexpected downtime, which improves the effectiveness of devices in general, in addition to enhancing operational reliability. The findings also reveal that predictive maintenance systems are not only efficiency-enhancing systems, but also innovative enablers, as predictive maintenance systems enable real-time decision-making, optimizing processes and resources in an adaptive manner. Using the ideas of turning reactive maintenance systems into proactive, information-driven approaches, the advanced manufacturers will be in a better position to enhance production continuity, cut expenses on the operation, and become more technological. The research is relevant to academic literature because it forms a quantifiable connection between AI-based predictive maintenance and productivity increase and innovation abilities in the advanced manufacturing ecosystems. In terms of policy and management, the results can be used practically to guide the U.S. manufacturers who want to use predictive analytics to improve industrial competitiveness and maintain their long-term growth in the dynamic digital economy.
Article information
Journal
Journal of Business and Management Studies
Volume (Issue)
8 (5)
Pages
01-23
Published
Copyright
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
Open access

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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