MindSphere enables industrial companies to improve plant performance by collecting and analyzing large volumes of production data. The MindSphere platform and associated applications are to be offered on Microsoft Azure in the future. Companies that want to develop and deploy their applications in the public cloud will be able to choose between Microsoft data centers at different locations worldwide. With 'Azure Stack', they will also be able to use Azure services in their own data center. Users will thus benefit from the scalability and efficient management of a public cloud solution without having to forego the control options of their own data center.

The atmosphere of the 'MindSphere Lounge' at the Siemens stand in Hall 11 makes the future tangible, for example with the 'Holo-S7 MindApp demo'.

© WEKA Trade Media

In addition to Java, JavaScript and Python, MindSphere will also support .NET applications in future: .NET developers can benefit from their existing applications and expertise when developing new MindSphere apps. This reduces the amount of training and programming required and enables projects to be implemented more quickly.

MindSphere is designed as an open IoT ecosystem. Industrial companies can use MindSphere as the basis for their own digital services, for example for predictive maintenance, energy data management or resource optimization. Machine and plant manufacturers can use MindSphere to monitor globally distributed machine fleets for service purposes, reduce their downtimes and thus offer new business models. MindSphere is also the basis for data-based services from Siemens, such as predictive maintenance of machine tools or integrated drive systems.

At SPS IPC Drives, Microsoft will be presenting services in Siemens' 'MindSphere Lounge' (Hall 11) that make it easier for app developers to develop solutions for the MindSphere platform, such as a mixed reality application on Microsoft HoloLens that is connected to a Simatic S7 controller. The 'Holo-S7-MindApp-Demo' shows a three-dimensional model, the digital twin of the device, visualizes its operating status and writes back values via gesture control.