Digital twins are among the particularly promising examples of applications for Industry 4.0 and the Internet of Things (IoT). These are virtual, dynamic models that completely map a physical object or process in real time and thus allow precise predictions to be made about performance, weak points, material fatigue or other risks. Classical digital twins are built especially for particularly cost-intensive, critical and durable products - for example complex machines - and provide valuable insights into the optimization of a product and its maintenance.
By contrast, digital twins for processes (Digital Process Twin) allow analyses not only of individual products, but of the entire value-added process or even the entire value-added network, thus comprehensively leveraging the potential of digitization and networking. This also involves suppliers and customers of the company in real time. The virtual simulation of the entire value-added cycle allows hidden inefficiencies to be identified, critical situations in production processes to be resolved quickly, and thus continuous improvement in key structures, processes and resources to be achieved.
When setting up a Digital Process Twin, process parameters are first defined that have an influence on the performance of the processes or plants in focus. The second step is to correctly record the existing process data and, if necessary, supplement it with additional data generated by additional sensor technology. The collected data is then merged and analyzed in a cloud application.
This creates the basis for building a model that maps the process-relevant parameters, their interactions and critical values in the entire process chain as accurately as possible. The resulting Digital Process Twin is a valuable tool for performing descriptive, predictive and prescriptive analyses in real time, integrating the model with other technologies such as machine learning, and sustainably improving the quality, efficiency and transparency of processes - from purchasing and supplier qualification to logistics and production to customer-specific delivery or maintenance planning.
ROI has extensive experience in content, technology, methodology and industry in the design and development of digital twins for products, plants and processes. The service portfolio includes the following topics in particular:
- Analysis and conception of business cases for the use of digital product and process twins,
- vendor-independent technology selection and introduction,
- descriptive, predictive and prescriptive data analysis,
- Implementation taking into account the process-related and organisational characteristics of the respective company and its suppliers and customers.
Digital Process Twin: Process optimization through Predictive Quality and Predictive Production
An automotive supplier improved the transparency of work and organizational processes in a production plant for dashboards. With a "Digital Process Twin" from ROI, the company reduced the reject rate and made improvement potentials in its value creation networks visible.