This study examines reliability of equipment (RoE) through three approaches: equipment design reliability, human reliability (HR), and maintenance-based reliability. HR plays a key role in minimizing human error (HE) and subsequently enhancing RoE. Given that equipment reliability is influenced by numerous fac- tors, these technologies come with various constraints, multiple outputs, and inputs. Compared to mathematical programming and analytical models, simu- lation methods in the field of reliability are relatively limited. However, system dynamics (SD) modeling is well-suited to capture dynamics and complexity of systems, making it a valuable tool for long-term strategic decision-making. In this study, a combination of SD and regression approaches has been employed to explore the relationship between reliability and key variables such as HE and profit. Initially, the variables influencing reliability are identified, and then SD is utilized to understand the processes and interactions among these variables. Furthermore, linear regression is employed to establish the relationship between affective variables, reliability, and HE. To validate the results obtained from the proposed method, a sensitivity analysis is conducted. The results demonstrate the effectiveness of the proposed model. Simulation results indicate that implement- ing policies such as employee training and preventive maintenance significantly enhances RoE, leading to increased sales and profits for the organization. There- fore, managers should prioritize these variables and allocate adequate attention and resources to them.

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