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Artificial Intelligence for Predictive Maintenance: Analyzing Work Order Trends across Global Pharmaceutical Sites

Author(s) Srikanth Reddy Katta
Country United States
Abstract Predictive maintenance (PdM) has emerged as a critical strategy for ensuring the reliability and efficiency of pharmaceutical manufacturing operations. Leveraging Artificial Intelligence (AI) to analyze work order trends across global sites can unlock significant operational insights, minimize downtime, and enhance productivity. This study explores the integration of AI techniques, such as machine learning algorithms, Natural Language Processing (NLP), and time-series analysis, to predict maintenance needs accurately. By examining work order trends from multiple pharmaceutical facilities worldwide, this research identifies key patterns and anomalies, facilitating a proactive approach to maintenance. The methodology combines data pre-processing, feature extraction, and advanced predictive modeling, resulting in robust decision-making frameworks. Results demonstrate the effectiveness of AI-driven solutions in optimizing maintenance schedules and reducing operational disruptions. The findings underscore the transformative potential of AI in pharmaceutical manufacturing, paving the way for more resilient and efficient production systems.
Keywords Predictive Maintenance, Artificial Intelligence, Work Order Trends, Pharmaceutical Manufacturing, Machine Learning.
Field Engineering
Published In Volume 5, Issue 8, August 2024
Published On 2024-08-07
Cite This Artificial Intelligence for Predictive Maintenance: Analyzing Work Order Trends across Global Pharmaceutical Sites - Srikanth Reddy Katta - IJLRP Volume 5, Issue 8, August 2024. DOI 10.5281/zenodo.14710540
DOI https://doi.org/10.5281/zenodo.14710540
Short DOI https://doi.org/g82n7h
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