The term 'system model' is used in many different domains, fields of application and in various forms with different meanings. One of model-based systems engineering's targets is the generation of a system model, which is used to describe complex system aspects across multiple views of disciplines and technical domains. Often a system model generated with systems modeling language is used as a central placed model in development. Besides, there are practical approaches, where models generated with other languages are also sometimes called system models. The scope of this paper is a generic definition of the term 'system model' and its interactions with other types of models in a model-based development ecosystem. Based on the analysis of the actual situation, a concept for the definition of system models is presented, which enables the use of multiple system models and which helps to understand the interactions with other types of models. For better comprehension of a system model's role in development, a three dimensional cube for visualization of system models and specific models is presented. Coupled with the definition of the term, interactions to other approaches like product lifecycle management and the vision of a single source of truth for development are investigated and discussed.
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Decision making is becoming more and more challenging due to the rise in complexity of modern technical products. A lot of industries are currently at a crossroads, and a wrong strategic or technical decision may have disastrous consequences for the future of the company. Within this paper, the SMH approach, that supports decision making processes to put emphasis on sustainable solutions regarding strategic and technical aspects, is introduced. SMH is an acronym that stands for a decision making approach that includes systems thinking (S), model-based systems engineering (M) and the human factor (H). This approach deals with the challenge to consider overall boundary conditions and interactions of the system, the decision which models need to be built in order to have the best data support possible, and the identification what influence the human factor plays in analyzing the data and the consequent decision making based on it. The importance of the human factor is often neglected in technical processes, which may lead to costly mistakes. This theoretical approach is applied to the use case of a chief executive officer (CEO) who has to decide on allocation of research and development (R&D) resources to future powertrain technologies.
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This paper describes the concept of a method that uses an existing system model, that describes the system's functions, to select the most suitable models for development. The term model is understood to encompass models used during the development of systems that: have a certain degree of formalism, are digitizable, connectable and processable. The method describes how specific models that are required can be identified and how they could be connected. The method concept is explained using a well‐understood example taken from the development of automotive powertrains. After stating current challenges and problems in the development of complex systems in the automotive domain, a system cube is used as a structuring principle for models that describe certain system aspects such as structure and behavior. This concept acts as a starting point for the selection of the most suitable specific models allocated to system models based on the functional description of the considered system. Finally, the contribution of this research to the realization of a digital thread is discussed and future research topics are outlined.
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