Implementing Digital Twins In Industrial Systems

Industrial companies are increasingly required and willing to adopt advanced algorithms, particularly due to significant advancements in artificial intelligence. However, the effective utilization of modern algorithms necessitates a comprehensive digital transformation within a company. This transformation encompasses various dimensions, including business models, connectivity, retrofitting of non-digital plants, and the integration of data from multiple sources and use cases. In this context, digital twins serve as a central integrating element, enabling the fusion of physical-world data to support analytics, planning, control, and business model development. Once an industrial company commits to digital transformation, the critical question arises: how can a digital twin be created, and what are the necessary fields of action? The challenge lies in the fact that this depends on both the specific data objects required for company-specific use cases and the company's existing level of digitalization. Hence, the objective of this paper is to develop an action model for digital transformation based on digital twins. The methodology employed is a multiple-case study analysis of ten cases based on expert interviews and project workshops. The resulting model provides a systematic framework for practitioners and researchers to strategically implement digital twins.

Action Framework

Data Acquisition

Data Analysis

Digital Transformation

Digital Twin

Industry 4.0