Designing a Model for Implementing Operational Decisions in the Industry Based on Artificial Intelligence

الموضوعات : International Journal of Mathematical Modelling & Computations

Mayyadah Mohammed Ridha Naser ¹ , Mohammad Jalali Varnamkhasti ² , Husam Jasim Mohammed ³ , Mojtaba Aghajani ⁴

1 - Department of Industrial Management , Isfahan Branch (Khorasgan), Islamic Azad University, Isfahan, Iran.
2 - Department of Science, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran.
3 - Al-Karkh University of Science, Baghdad, Iraq.
4 - Department of Management, Mobarakeh Branch, Islamic Azad University, Mobarakeh, Isfahan, Iran.

تاريخ الإرسال : 09 الثلاثاء , جمادى الثانية, 1446 تاريخ التأكيد : 12 الثلاثاء , شعبان, 1446 تاريخ الإصدار : 12 الثلاثاء , شعبان, 1446

الکلمات المفتاحية: Data Mining, Classification, Decision Making, Technique, Improve, Processes.,

ملخص المقالة :

The increasing complexity and interconnectedness of industrial processes require innovative solutions to optimize operational decision-making. Artificial intelligence (AI) technology has gained attention for its potential to revolutionize operational decisions by harnessing vast amounts of data, analyzing it in real-time, and making informed decisions quickly and accurately. AI algorithms can automate decision-making processes, identify patterns and trends in data, and make predictions about future outcomes.  This study investigates the development of an efficient operating model for the deployment of large-scale artificial intelligence (AI) systems in a selected cement factory in Iraq.  The proposed methodology involves data collection, pre-processing, feature engineering, data visualization, and model selection. Key Performance Indicators (KPIs) were identified through partial least squares regression analysis to model the relationship between energy consumption, production rate, temperature, and moisture content on product quality in a cement factory in Iraq. The results of the study show that the machine learning model accurately predicted energy consumption, production rate, and product quality, with R-squared values of 0.95, 0.92, and 0.88, respectively. The optimized neural network model reduced energy consumption by 12% while maintaining production rate and product quality. Overall, the implementation of AI in the cement industry in Iraq has the potential to improve operational efficiency, reduce costs, and enhance product quality.

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