Simulation-Based Engineering Management: A Review of Techniques for Improving Decision-Making

Abstract

Simulation-based techniques have become increasingly vital in engineering management, offering sophisticated tools for improving decision-making processes across a range of applications. This narrative review provides a comprehensive examination of key simulation methods, including Monte Carlo simulations, discrete-event simulations, system dynamics, and agent-based modeling, and their relevance in addressing complex engineering management challenges. The review highlights how these techniques enhance decision-making by enabling the modeling and analysis of complex systems, thereby allowing managers to predict outcomes, optimize processes, and mitigate risks. However, the implementation of these techniques is not without challenges. Technical difficulties, such as computational complexity and data accuracy, along with organizational barriers, including resistance to change and a lack of expertise, present significant obstacles. Additionally, the current research landscape reveals gaps in the practical application and scaling of these models, underscoring the need for further investigation. The review also explores emerging trends, such as AI-driven simulations and real-time decision support systems, which are set to shape the future of engineering management. Practical recommendations for engineering managers are provided, emphasizing the importance of integrating simulation tools with existing systems and fostering a culture of experimentation and iterative learning. The article concludes by discussing the broader implications of simulation-based techniques for engineering management and the critical need for ongoing research and development in this evolving field.