Simulation modeling has become a cornerstone of decision support in complex industrial systems, offering a robust analytical foundation for evaluating alternative strategies, improving performance, and managing uncertainty without disrupting real-world operations. Over the past two decades, discrete-event simulation (DES) and its hybrid variants have played a central role in supporting operational planning, production scheduling, resource allocation, and logistics optimization. As manufacturing systems evolve toward Industry 4.0 paradigms—characterized by cyber-physical integration, real-time data exchange, and autonomous decision-making- the role of simulation is expanding rapidly. However, despite significant technological progress, the field remains fragmented, with many implementations confined to tactical decision contexts and limited in their adaptability to real-time, multi-objective, or strategic challenges. This review aims to synthesize the state of the art in simulation-based decision-support for industrial systems by classifying the current body of literature based on simulation type, application domain, decision level, and tool integration; identifying methodological innovations; highlighting key research gaps; and proposing a forward-looking research agenda to guide future scholarship and practice.