Data Management, Networking and Analysis

R&D Trend 2022 / 2023

Zeros and Ones at the Factory

No matter who you talk to in the industrial environment, everyone speaks about the challenge of capturing data and storing it appropriately, transmitting it securely and analyzing it intelligently. Behind all this is the goal of using data-driven solutions to make processes more efficient or to generate new business models. But how is this supposed to work?

There was a time when machine tools were closed systems. It is true that since the 1960s they have increasingly been equipped with digital controls, which, in addition to the automatic control of states and movements, facilitated the setup of machining processes and to a certain extent also enabled them to be monitored. However, comprehensive condition monitoring or even connecting a machine to a cross-company production control level would not have been possible on this basis. This has changed when the idea of »Industry 4.0« emerged: Today, wireless units in machine tools provide network capability, and sensors transmit a wide variety of operating data. But what is the point of all this? 

From selective monitoring to the 360° digital twin

The basic idea: By evaluating sensor data, machine behavior can be monitored or machining processes can be optimally adjusted. Via wireless technology, a machine signals to production management that it is ready for the next job. And that is only a fraction of the possibilities. The picture becomes more comprehensive with a so-called digital twin. The virtual image of a process or machine reflects their geometry and structure as well as their behavior. Imagine if all process levels in a company, from product development and purchasing to production and assembly, sales and marketing, were underpinned by such systems. If these were also linked across disciplines, the result would be a 360° twin, a highly integrated corporate image, that can help bring a previously unattainable degree of efficiency to value creation. 

This is no longer a pipe dream. Digital twins have reached application maturity, are in demand by industry and are being made fit for various sectors and areas of application at our institute, among others. In the »Mastering Digital Twins« certificate program, we provide background knowledge that enables companies to put digital twins to use. 

»Our greatest progress in recent years has been to actively use data from our plants to analyze processes, describe our company performance or underpin decisions regarding new technologies,« says Dr. Christoph Hübert, Senior Director Development Leads at medical device manufacturer BIOTRONIK SE & Co. KG. But very few have advanced that far. Even established companies in the manufacturing industry are still in the early stages of using data ­intelligently.  

Collecting suitable data sets with IIoT platforms

That’s not surprising, either, because the challenges start two steps earlier. »At the moment, some consulting companies are conveying that all you have to do is integrate plenty of sensors into machines and stream masses of data into a cloud, and then you would have Industry 4.0,« criticizes Dr. Ansgar Kriwet, member of the management board sales at Festo SE & Co. KG and member of the Fraunhofer IPK Board of Trustees. »But nobody gets anything out of that.« Rather, he says, the first step at the beginning of digitizing production should be to analyze which data make sense for the company-specific use case, because they enable added value. Often, this data can already be accessed through existing sensors. We are addressing this question in a series of research projects with CONTACT Software GmbH on the topic of Industrial Internet of Things (IIoT) platforms. The goal is to move from big data to smart data in order to make data volumes manageable and to collect precisely the data that is of value to the respective company.

And the collaboration with CONTACT goes even further: How can large volumes of data be securely transferred, stored and retrieved? »Integrating machines into a network requires an IT infrastructure that many companies don’t even have,« reports Martin Kapp from the KAPP NILES group and another member of our board of trustees. Cloud and edge technologies can be a solution here. The complex interplay of data ecosystem, infrastructure and services is addressed, among other things, in the European cloud project »GAIA-X«, in which researchers from our institute are participating. Data security and data sovereignty are also being addressed, because: »The acceptance of distributed solutions presupposes that these issues are resolved,« as Sven Hamann, CEO of Bosch Connected Industry, notes. 

Product data management throughout the lifecycle

A final intermediate step before data utilization is data consistency. The goal must be to pass data from early lifecycle phases of a product through the production process to application. Ideally, usage data should flow from the last point in the chain into optimization loops. Such comprehensive Product Life­cycle Management (PLM) has been an important research topic at our institute for years. In the »Mastering PLM« training course, we also provide hands-on training for product data management specialists. The next logical step in data handling must then be to link PLM with process control right down to the shop floor, so that design data, for example, can be used to set up manufacturing processes without any detours.

»But the really big leap would be to include data from supplier companies in the overall view of the company’s own data network,« says Dr. Hübert, formulating the next challenge. »That would simplify certifications and quality controls enormously.« Looking into the data environment of companies is still difficult to realize. But there are promising approaches. With the situational awareness cockpit, we offer a tool with which companies can link environment information with internal capabilities. In this way, alternative courses of action can be developed quickly if, for example, a supply chain breaks down. 

Data-based value creation increases profitability

Efficient data handling and intelligent data use take value creation to a new level. Processes can be simplified and accelerated, for example with artificial intelligence (AI) and machine learning. Intelligent process control is becoming a reality, as are adaptive assistance systems that support the handling of variants, quality assurance or the maintenance of machinery. Completely new business models are even emerging, such as service offerings based on data from machines.

Our solutions for this topic area

  • IIoT architectures that extract meaningful data from raw data
  • Cloud and edge technologies for application in production
  • Data continuity based on PDM, PLM and other technologies 
  • Digital twins for all business units and technology levels
  • Enterprise cockpits that merge data across domains

Q & A

Dr.-Ing. Patrick Müller

CONTACT Software GmbH


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