The potential benefits are diverse and directly depend on the application case. The collection and visualisation of existing process and machine data from the different levels of the automation pyramid help to create new data transparency. This provides an important impetus for the continuous improvement process in manufacturing. In addition, analysis algorithms and refined policies for large data volumes help to selectively realise applications that are tailored to the respective needs of the end customers. These range from the continuous analysis of process data for the purpose of reducing scrap and rework, through the monitoring of machine cycle times (specifically at the bottleneck) to maximise the output, on to predictive maintenance for a selective planning of maintenance activities and minimisation of down time. All of this is possible. Finally, we must also focus on automation. Both simple and complex business processes can be initiated and monitored with the new applications: from simple notifications by e-mail, text message or app to automatically initiated ordering of spare parts and monitoring of maintenance orders.
In the context of Industry 4.0 it is essential to develop solutions in close collaboration with the users. The Bosch Group has been doing this with different users in the automotive, industry and consumables production for many years.
In the future, we see the need for investments mainly in the field of data intelligence, i.e., for derivations of profitable activities from the analysis of available data. We are talking about general and also far-reaching IT questions such as the connection and provision of data in the requested format. This also includes the use of control technology and specific software architecture expertise, for example, in security architectures in the context of remote access. These aspects are becoming ever more important for manufacturing, but they should certainly not be a core competence of a manufacturing unit in the future either.
The great potential of Industry 4.0 lies in data and particularly in the efficient use of newly gained