Optimizing Semiconductor Manufacturing for Small and Medium Enterprises: A System-Dynamics and Machine Learning Approach

Small businesses in the semiconductor industry face unique challenges in optimizing low-volume, highly customized production. Our study introduces an optimization framework that integrates system-dynamics modeling, linear programming, and predictive analytics to streamline supply chain networks and improve manufacturing efficiency. By leveraging Python-based simulations, our approach enhances cost-effectiveness, supports rapid prototyping, and utilizes cross-validated machine learning for predictive modeling to optimize production outcomes. Through statistical validation including correlation analysis and ANOVA, plus comparative analysis with alternative optimization techniques, our framework demonstrates significant improvements in both theoretical efficiency and practical application. The framework not only advances the theoretical foundation for specialized semiconductor manufacturing but also provides practical insights tailored to the constraints and implementation challenges faced by Small and Medium Enterprises (SMEs).