Application of Artificial Intelligence in Production Planning and Supply Chain Management: Examining the Role of Machine Learning, Deep Learning, and Neural Networks in Optimizing Production Processes in the Electronics Industry of Iran

This research investigates the impact of artificial intelligence techniques, including machine learning, deep learning, and neural networks, on optimizing production processes in the electronics industry, focusing on companies based in Tehran. The research sample consisted of 243 managers and experts related to supply chain management within these companies. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the results indicated that each of these techniques positively and directly influences demand forecasting, inventory management, quality control, and cost reduction in production. The findings also highlight the importance of integrating these techniques to achieve synergistic effects and enhance overall production process performance. From a theoretical perspective, this study contributes to expanding the existing literature on the application of artificial intelligence in manufacturing and presents new models for examining the interactions of these techniques. On the other hand, the results are practical for managers in the electronics industry and other manufacturing sectors, enabling them to leverage artificial intelligence tools for process optimization, productivity enhancement, and cost reduction. Ultimately, this research demonstrates that artificial intelligence can act as a transformative factor in the manufacturing industry, guiding organizations toward sustainable competitiveness.