Stage 1
Computerisation
Is the basis for digitalisation. Here, information technologies are used in isolation from each other in the company.
Stage 2
Connectivity
In the connectivity stage, the isolated use of information technology is replaced by connected components. Digital applications are interconnected and reflect the core processes of the company.
Stage 3
Visibility
Here, big data analytics is used to explore cause-effect relationships. The semantic linking and aggregation of data into information as well as its contextual classification represent the cause-and-effect principle knowledge that provides the basis for complex decisions.
Stage 4
Transparency
Here, big data analytics is used to explore cause-effect relationships. The semantic linking and aggregation of data into information as well as its contextual classification represent the cause-and-effect principle knowledge that provides the basis for complex decisions.
Stage 5
Forecasting capability
In this stage, models based on automated machine learning can be used to simulate various future scenarios and subject them to a probability forecast. Forthcoming events can be predicted and suitable measures can be initiated at an early stage. The result is a reduction in unexpected events and a more stable operational process. The ability to predict depends crucially on successful implementation throughout the preliminary stages. A well-developed digital shadow in combination with determined cause-effect relationships lays the foundation for high prediction quality.
Stage 6
Adaptability
Predictive capability is the prerequisite for automatic action and continuous self-optimisation. The goal of stage six is achieved when data from the digital shadow is used in such a way that decisions with the greatest positive impact are made autonomously in the shortest possible time and resulting actions are implemented (in real-time).
