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AI-Based Predictive Models for Enhancing Operational Efficiency

Author(s) Babak Abbasi
Country Australia
Abstract The rapid development of artificial intelligence has created new opportunities for organizations to improve operational efficiency through predictive and data-driven decision-making. Traditional operational management approaches frequently rely on historical reports, manual monitoring, and reactive responses, which may result in delayed interventions, inefficient resource utilization, and unexpected operational disruptions. AI-based predictive models provide an alternative by analyzing historical and real-time data to identify patterns, forecast future conditions, detect potential failures, and support proactive operational decisions. This study examines the role of AI-based predictive models in enhancing operational efficiency across contemporary organizational and industrial environments. A qualitative descriptive research design was adopted using a structured review and conceptual analysis of literature related to artificial intelligence, machine learning, predictive analytics, operational management, and predictive maintenance.
The findings indicate that AI-based predictive models can contribute to improved resource allocation, reduced downtime, better maintenance scheduling, enhanced process monitoring, improved forecasting, and more responsive operational planning. However, implementation remains constrained by data quality, computational requirements, model interpretability, cybersecurity, integration complexity, scalability, and organizational readiness. The study concludes that AI-based predictive modeling can significantly strengthen operational efficiency when supported by reliable data infrastructure, appropriate model selection, continuous validation, human oversight, and effective organizational integration.
Keywords Artificial Intelligence, Predictive Models, Operational Efficiency, Machine Learning, Predictive Analytics, Intelligent Systems, Predictive Maintenance, Data-Driven Decision Making.
Field Engineering
Published In Volume 1, Issue 2, March-April 2019
Published On 2019-03-17
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