System Dynamics Modeling for Performance Optimization in a Pipe Manufacturing System: An Ethiopian Case Analysis

This study presents a system dynamics-based case study aimed at evaluating and enhancing the performance of a manufacturing system, with specific application to the Amhara Pipe Factory in Ethiopia. Manufacturing systems in developing economies often face challenges related to capacity utilization, operational inefficiencies, and human performance, which collectively limit their ability to meet customer demand and maintain competitiveness. To address these issues, a system dynamics model integrating human factors, process factors, and product and service factors was developed using empirical data collected through questionnaires, interviews, direct observations, and company records. Statistical analysis using IBM SPSS was applied to identify and prioritize key performanceinfluencing factors, which were subsequently incorporated into the simulation model developed in Vensim software. Three policy scenarios were evaluated to assess potential performance improvements: variation in customer demand, reduction of cycle time and downtime, and enhancement of employee training and experience. Simulation results indicate that increasing customer demand by 50% improves production performance by 0.457%, while reducing cycle time and downtime by 50% increases performance by 2.257%. The most significant improvement was observed under the human-factor-focused scenario, where enhancing employee training and experience by 50% resulted in a 26.6% increase in production performance, achieving up to 98.1% of the desired production capacity. Unlike conventional static performance assessment approaches, the proposed framework captures the dynamic interactions and feedback relationships among manufacturing system variables, enabling long-term evaluation of alternative operational policies. The findings highlight the critical role of human factors in manufacturing system performance and demonstrate the usefulness of system dynamics modeling as a decision-support tool for performance improvement in industrial and environmental engineering applications.