Over the last two decades, a concept called Digital Twin has evolved rapidly. Yet, there is no unified definition of the term. Based on a literature study and an industrial case study, an overarching definition of Digital twins is presented. Three characteristics were identified – representation of a physical system, bidirectional data exchange, and the connection along the entire lifecycle. Further, three sub-concepts are presented, namely: Engineering Twin, Production Twin, and Operation Twin. The presented paper thus formulates a consistent and detailed definition of Digital Twins.

A Digital Twin as a virtual representation of a physical system is becoming a key technology. While potential benefits are evident, there is no approach in literature or practice comprehensively supporting its introduction. In an industrial case study, a generic procedure model for the conception and implementation of a Digital Twin was developed. The relations between use cases, usage data, and virtual models resulted in a target concept as well as requirements for the implementation. Thereby, companies can access the potentials of a Digital Twin taking into account their specific situation.

Process models are among the principal artefacts used for managing design projects. However, the selection of effective modelling approaches can be difficult for design project managers, given that a plethora of tools exists for various modelling purposes. In addition to date no systematic approach for the assessment and selection of process modelling approaches is available to practitioners. This paper presents the development of criteria for benchmarking and selecting different process modelling tools. The results are based on three elements. (1) In a four-hour workshop undertaken by the Design Process SIG of the Design Society, bringing together around 20 international researchers and practitioners in design process modelling, an initial set of 58 criteria were brainstormed and consolidated during the workshop and in follow-up meetings. (2) The consolidated criteria were then compared with literature. The finalised criteria list was then validated by external experts in industry (3). The resulting list of 12 criteria provides a sound basis for practitioners to support a systematic selection of process modelling approaches. Further, it lays the foundation of a benchmarking tool, which is subject to future work.

The growing digitization affects all areas of engineering. Together with fast-paced trends, it drives complexity and uncertainty in many domains. Yet, its potentials are manifold and, in most cases, outweigh the disadvantages. Beneath terms such as "big data", "digital twin", the term "data-driven engineering" has evolved over the last years. However, neither in literature nor in industry, there is a unified definition or understanding of the term. The presented research is based on a literature review as well as an industrial case study. Several databases were screened systematically for the literature review and forward and backward searches were used additionally. The case study was conducted in a collaboration with a company in the climate system sector. First, a literature-based distinction between the terms model-based, modeldriven, data-based, and data-driven as well as definitions of data-driven engineering were investigated. Representatives of the company then evaluated these findings in a workshop and together with the industry partner a consistent definition was developed. The authors define data-driven engineering as a framework for product development in which the goal-oriented collection and use of sufficiently connected product lifecycle data guides and drives decisions and applications in the product development process. Further, promising use cases for the industry partner regarding data-driven engineering were formulated. The use cases were initially evaluated and prioritized regarding their cost-benefit ratio. Symbioses with other strategies of the company such as Digital Twins, model-based engineering, and solution space engineering are outlined. For academia, the presented findings provide a consistent definition that can be used as a promising direction for future research. Especially a procedure model for the systematic conception and implementation of data-driven engineering would be beneficial. For industry, this paper provides insights on potentials of data-driven engineering, a differentiation from related concepts, and very concrete use-cases serving as a starting point for a company-specific implementation.

A reason for the slow adoption of digital twins in industry is a lack of trust in the concept and between the stakeholders involved. This paper presents a Trust Framework for Digital Twins based on a literature review and an interview study, including seven recommendations: (1) explain your twin, (2) create a common incentive, (3) make only one step at a time, (4) ensure IP protection and IT security, (5) prove your quality, (6) ensure a uniform environment, and (7) document thoroughly. Together with 20 concrete measures it supports practitioners in improving trust in their Digital Twin.