
An outstanding aspect and highlight of this project was the partnership with Simplifier.

Sebastian Grimm
Head of Operational Excellence, Process Industries and Drives Division at Siemens Healthineers
Siemens Digital Production
Interaction in the smart factory with Simplifier
The manufacturing industry can benefit enormously from the potential of digital transformation. Technologies such as sensor technology, robotics, AR and VR, which enable high-quality data collection and evaluation, promise batch size 1 production, high productivity, falling production costs, increasing competitiveness and zero downtimes.
Siemens AG also wanted to take advantage of these innovative technologies for its Process Industries and Drives division at its Nuremberg site. The focus here is on customer-specific projects with a high proportion of manual production, which means that people still play a major role in the process. IT must therefore enable the worker to interact with the systems and machines.
Production at the Siemens plant in Nuremberg was characterized by a lot of paper-based work. Obtaining and passing on information was very time-consuming for the plant employees. There were either many different data sources or the data was only available in fragmentary form. Isolated solutions made data transparency and quality more difficult and were also sometimes incompatible with ERP systems such as SAP.
This meant that there was no standardized database available for the production process steps. Duplicate entries and additional work were therefore the order of the day. Faults, for example, had to be recorded using several tools so that the relevant departments could allocate costs, rectify faults technically or procure spare parts.
The Simplifier low-code platform can be used to create integrated business and IoT applications in just a short time. It is based on existing IT and connects existing systems with other data sources, such as SAP databases and the IoT platform Mindsphere from Siemens. So-called connectors transfer the data, which can be further processed in the application as required and controlled via a mobile application.
Two application examples in use:
Non Conformance (NC) App
This IoT application was developed on Simplifier and is used to record fault messages that occur during the manufacturing process. To make the final acceptance of a product smarter, data transmission should also be easier in the event of a fault. The worker can call up existing data on the product via the system database and only needs to enter a few details about the fault – quickly and easily using a mobile device. The additional data recorded is also stored in the system so that all relevant data is available and can be used during the fault handling process.
SIPRO app
In contrast to the NC app, SIPRO is used to record faults in machine tools, buildings, telephone systems, etc. rather than faults in the project. The SIPRO app has been in use for several years, but the technology used was no longer up to date and therefore needed to be replaced. Simplifier made it possible to create the same app smoothly in a very short time and the SAP PM system to be used could also be integrated easily via standardized connectors. The SIPRO app is already the second application communicating with SAP that will go live in the plant within a very short time.
The employees benefit from this:
The digitalization of production has improved many things for the workers. Data collection using pen and paper is a thing of the past. However, system integration with Simplifier allows them to interact with the IT system despite the digital processes. Thanks to the innovative technologies and the user-friendly handling of the smart devices, the acceptance rate among plant workers is very high.
The applications were immediately popular, and not only that. The use of Simplifier enabled the plant workers to develop their own desired applications. Mobile data capture also facilitates the process flow in all areas. Consistency in data collection also increases data quality. This enables the analysis of faulty processes and the identification of correlations in production.