Intelligent Decision Systems for Industrial Process Optimization

The increasing complexity of modern manufacturing environments has created a growing need for intelligent decision systems capable of optimizing industrial processes and improving operational performance. This study presents a data-driven intelligent decision framework for industrial process optimization that integrates Industrial Internet of Things (IIoT) data acquisition, machine learning analytics, predictive maintenance modeling, and mathematical optimization techniques. The proposed framework utilizes industrial datasets obtained from machine sensors, production logs, maintenance records, and energy monitoring systems to develop predictive models capable of detecting equipment failures, improving production scheduling, and optimizing energy consumption. Machine learning algorithms are applied to analyze operational patterns and generate predictive insights, while optimization models determine the most efficient operational strategies under industrial constraints. Experimental evaluation demonstrates that the intelligent decision system significantly improves manufacturing performance by increasing production throughput, reducing machine downtime, and enhancing energy efficiency. Comparative analysis further shows that AI-driven decision frameworks outperform traditional rule-based industrial control systems in terms of predictive accuracy, adaptability, and operational efficiency. The findings highlight the importance of integrating predictive analytics and intelligent optimization algorithms within smart manufacturing environments. The study contributes to the advancement of intelligent manufacturing systems by providing a comprehensive framework that supports data-driven industrial decision-making and sustainable production optimization.