Characteristics of Simulation: A Meta-Review of Modern Simulation Applications

Simulation studies enable practitioners and researchers to prove assumptions and hypotheses. Through experiments, they can analyze real-world and conceptual systems. Hence, simulation is an integral part of industrial and scientific work. Nevertheless, simulation applications have to adapt to modern, digitized working changes. As simulation evolves analogously to the industrial world, the scientific world must adjust accordingly, and new research streams for the next steps of simulation's evolution must be defined. This work aims at gathering and exhibiting the properties of recent simulation studies. It provides the groundwork for the definition of research streams for the future of simulation. The paper lays the foundation for prescriptive design knowledge on simulation studies through a structured literature review. Thus, researchers and practitioners are enabled to take on the current challenges of simulation based on a descriptive up-to-date data basis.

1 Introduction

Simulation studies enjoy great popularity in research and industry (Diniz et al. 2021; Moeuf et al. 2018). As simulation enables real-world and conceptual systems analysis, they often come to use for process planning, scheduling, and project management (Gutenschwager et al. 2017; Law 2015). Nevertheless, industrial applications are evolving according to the industry's new technologies. Simulation studies must adapt to these new technologies, including the internet of things, cloud computing, digital surrogates, or sovereign data ecosystem (Boschert and Rosen 2016). Prescriptive design knowledge is necessary to design simulation models that can cope with these challenges. Prescriptive design knowledge is a set of rules that specify the architecture and composition of a given artifact (Chandra et al. 2015). However, prescriptive design knowledge needs a descriptive data foundation (Möller et al. 2021a).