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, model-driven, 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.     «

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...     »