Parametric Discrete Event Simulation for Performance Evaluation and Decision Support in Production Systems

Abstract

Modern production systems operate under increasing uncertainty due to fluctuating demand, limited resources, and system disruptions such as machine failures and maintenance activities. From an economic and managerial perspective, evaluating the performance of such systems is critical for supporting operational decision-making related to capacity planning, resource utilization, and service efficiency. However, traditional analytical approaches often require restrictive assumptions and fail to capture the dynamic nature of real-world production processes. In this study, a parametric discrete event simulation model is proposed as an a computational/software based decision support tool for the performance evaluation of a representative production service system. The model captures key operational parameters, including arrival rate, service time, failure probability, and maintenance duration, which directly influence system efficiency and economic performance. The model is evaluated through repeated simulation experiments to obtain statistically reliable performance indicators. In particular, the impacts of variations in service time and arrival rate on business-relevant performance metrics such as average waiting time, system availability, resource utilization, number of failures, and the number of serviced entities are systematically analyzed. The results demonstrate that increases in service and arrival intensities lead to performance degradation, highlighting critical trade-offs between system capacity, operational efficiency, and service quality. The proposed approach provides a practical and computationally lightweight framework for preliminary performance analysis and operational decision support in production and service-oriented systems. In addition to its applicability for early-stage economic evaluation, the model also offers educational value by enabling a clear understanding of discrete event simulation principles within an information systems context.