Unabhängiges Magazin für Wirtschaft und Bildung

21. Juli 2017

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From intelligent machines to the customers

From intelligent machines to the customers© Bilderbox.com

New added-value chains extend from networked machines to the product and the customer.

High-availability and fast and flexibly adaptable IT architectures and services have become key components in the technology-based management of the company, especially for companies with extensive customer relationships.
For the digital transformation of connected business processes, the management of rapidly increasing data volumes and especially cloud computing, are important cornerstones. The so-called Internet of Things (IOT), or interlocked industry 4.0 solutions via automated and intelligent, self-learning machines, cannot be imagined without this almost endlessly scalable technology which is always available.

Many fields of applications
However, networking is only one criterion, management and the evaluation of data amounts resulting therefrom are also essential. The fields of application are infrastructure projects of countries or cities as well as the energy, automobile, truck or railway industry sectors. Deutsche Bahn (German Railways), for example, operates a pilot project with the IT service provider T-Systems in the field of predictive maintenance, where modern sensor technology is combined with real-time data analysis.
Through communication from machine to machine (M2M) sensor data is transferred into an integral cloud by T-Systems and there it is connected with a multitude of data from diverse sources such as route networks, weather situations or energy supplies and with earlier elicitation. From this, a prediction model is generated which recognises possible damages long before they occurrence.

Real-time forecast of start-up and departure times
The practical use however shows further results, for example, from the error codes from locomotives, conclusions can be drawn concerning disruptions to energy plants (supply) or rails in the current systems. Alongside railway companies, related industries can also profit from these solutions, for example operators of large fleets of heavy goods vehicles, who lower maintenance costs with these and thus increase availability as well as ultimately customer satisfaction. “A relevant added-value of predictive maintenance is also valuable insights for Product Development”, says Axel Quitt, Sales Manager Big Data at T-Systems.

In a further project, Deutsche Bahn is now using real-time forecast of start-up and departure times in the railway sector. On the basis of the given timetable for the entire passenger traffic, timetable data of more than two million stops are compared per day. The position notifications (messages) of all trains on the move are analyzed in the data centre within seconds.
As a result, a real-time forecast of the estimated time of arrival with possible effects on connections is shown. With the new services, Deutsche Bahn customers can get information about departure times in real-time via a Smartphone and app as well as directly at the train stations up to 90 minutes in advance. The forecast solution is a proprietary development of T-Systems Multimedia Solutions and will be further developed and implemented in a joint project with Deutsche Bahn.

Links

red/czaak; translation by economy, Economy Ausgabe Webartikel, 27.04.2017