Digital product and process twins (digital twins)

Digital twins are among the most promising application examples for Industry 4.0 and the Internet of Things (IoT). These are virtual, dynamic models that reflect a physical object or a process completely and in real time, thereby allowing precise predictions about performance, vulnerabilities, material fatigue, or other risks. Classic digital twins are built especially for particularly cost-intensive, critical and long-lived products – for example complex machines – and enable valuable insights with regard to the optimisation of a product and its maintenance.


Using the digital twin for processes (digital process twin), it is not only possible to carry out analyses of individual products, but rather of the entire value creation process, or even of the entire value added network, and thus optimally raise the potential of digitisation and networking. Here, suppliers and customers of the company are also integrated in real time. Using a virtual simulation of the entire value creation cycle, hidden inefficiencies can be identified, critical situations in manufacturing processes can be quickly solved and thus a continuous improvement in essential structures, processes and resources is achieved.  

The first thing to do when building up a digital process twin is to define process parameters which have an influence on the performance of the processes or systems in focus. In the second step, the existing process data needs to be recorded correctly and, if required, supplemented by further data generated by additional sensors. The collected data is then merged and analysed in a cloud application.

    This creates the basis for constructing a model which maps the process-relevant parameters, their interactions and critical values as accurately as possible across the entire process chain. The resulting digital process twin is a valuable tool for performing descriptive, predictive and prescriptive analyses in real time, for integrating the model with other technologies such as machine learning, and for sustainably improving the quality, efficiency and transparency of the processes – from purchasing and supplier qualification through logistics and manufacturing to customer-specific delivery, or maintenance planning.   


    ROI has extensive content, technological, methodological and industry-specific experience in designing and building digital twins for products, plants and processes. The service portfolio consists of the following in particular:

    • Analysis and design of business cases for the use of digital product and process twins
    • Vendor-independent technology selection and introduction
    • Descriptive, predictive and prescriptive data analytics
    • Implementation taking into account the procedural and organisational characteristics of the respective company and its suppliers and customers


    Mann vor einem Laptop mit Digital Twin am Bildschirm

    “数字过程孪生体” 项目

    当某汽车零部件供应商想优化其仪表板制造工厂时,面临着两个挑战。首先,整个过程链,连同所有与生产、供应商管理相关的风险因素,必须变得更加透明。另一个挑战是,必须降低报废率,因为处理某敏感材料时,即使一个微小的错误也会导致整个部件的报废。该企业与合作,使用 “数字孪生体” 将整个生产过程逐步地绘制出来。由此产生一个物联网试行项目,它能辨识是否有改进的潜力,以及触发针对供应商生产和价值流管理方面的改进。