The primary objectives of any manufacturing company are to maximize production capacity (volume) to meet the average customer demand and gain profit (Eshetie, 2018). Uncertainty increases complexity in manufacturing systems, which is considered a major difficulty in many sectors. Uncertainty of demand increases production process complexity and number of operations in the system. The main objective of this study is to analyze and improve manufacturing system performance using system dynamics modeling techniques in the case of Amhara Pipe Factory (APF). On these premises, the study is motivated to investigate the number of dynamic factors that affect the loss of performance in APF and their effect on performance. As a result, this thesis tries to assess critical factors that affect the performance of a firm using IBM SPSS statistics 20 and develop system dynamics simulation model to analyze three different scenarios that can be used to assess the effect of each factor on the performance of the company using Vensim software. The study was conducted using an interview with structural questionnaire having a 71-sample size, and direct observation by using purposive sampling and simple random sampling techniques. The results of correlation and regression analysis demonstrate that human factors, process factors, and product and service factors all significantly affect Amhara Pipe Factory's overall performance. Using these factors as a baseline, a system dynamics model is constructed. The findings of the developed system dynamics model are: 1) when an average customer demand increase by 50%, the company improves their performance by 0.457% from their actual performance. 2) By reducing downtime and cycle time by 50% on average, the company can improve its performance by 2.257% over its current performance. 3) By increasing the value of the training and experience factor from the human factor sub module by 50%, the company improves its performance by 26.6% compared to the company's actual performance (71.843%). Depending on the result, the researcher concludes that the third scenario is the best to be implemented and gain performance improvement. So, the study implies that working on improvement of employee performance has a great impact on the overall performance of manufacturing industry. Key words: system dynamic modeling, performance, manufacturing system.