Project management for procurement of materials for the production process of AI-based electrical panels

This study explores the implementation of an AI-based project management model to optimize the procurement and production processes in the electrical panel manufacturing industry. Traditional project management approaches, such as linear programming, often fail to adapt to real-time fluctuations in demand and supply chains. Addressing these limitations, the proposed AI-driven framework integrates predictive analytics, real-time data processing, and adaptive scheduling algorithms to enhance procurement precision, reduce lead times, and lower operational costs. A mixed-method approach, utilizing both quantitative simulation and qualitative case studies, was employed to evaluate the model’s effectiveness. Results demonstrate that AI-based optimization can reduce project timelines by up to 36% in expedited scenarios while balancing cost efficiency through selective task prioritization. This study contributes to Industry 4.0 advancements in smart manufacturing, offering a practical approach to overcoming challenges in high-variability production environments.