Hunters of the hidden data treasure – the DTO

Digitalisation and industry 4.0 | People at thyssenkrupp | Fast, reliable, highly available: These are processes at thyssenkrupp. But because there is always room for improvement, data analytics and data science are major topics in all segments. On behalf of the businesses, the Digital Transformation Office dives into targeted information streams to unearth hidden data treasures.

It’s a nightmare that brings a cold sweat to the brow of every production manager: A defective production part runs through the entire production chain, wasting storage capacity, time and money on its way through the various stations – only to be finally disposed of due to poor quality. It is similarly bad when the defect shows an atypical pattern in the numerous sensors at an early stage, but this has not yet been understood and evaluated.

DTO – please, take over

 To prevent such worst-case scenarios from occurring at thyssenkrupp in the first place, more and more businesses are working closely with the Digital Transformation Office (DTO) of thyssenkrupp Materials Services. The experienced team deals with data sets and software developments in various projects. The team is able to examine process improvements requested by the business departments – and supported by corresponding hypotheses – and integrate them into the operational processes if they are successful.

This does not necessarily always have to be about production. Often, the experts are also involved in planning and logistics issues, for example: “Can we reliably forecast which materials will be needed where and at what time on the basis of our data? Ideally, an optimized process model emerges from the data analysis. This saves time and costs as soon as it is successfully integrated into the day-to-day operations of the specialist department via user-friendly software applications. DTO experts call this “Data Analytics as a Service”.

Deep Dive for Data

At the beginning of each project, the team dives deep into the sea of data of the respective business to conduct intensive detailed investigations of the individual information flows. The effort and meticulousness involved are reminiscent of criminal detective work. They are looking for the proverbial needle in the haystack, because everyday millions of data records are generated, stored and processed in the Group areas at thyssenkrupp. But not all data records have the potential to make processes even more efficient and simpler in the long term.

When sifting through the data jungle, success doesn’t depend on skill alone. “It also takes a bit of luck,” says Markus Tautschnig, Data Solution Architect at thyssenkrupp Materials Services. “Of course, we only start projects where we see a high chance of success based on the hypotheses we set up in advance.” But there is no guarantee, he says. Similar to a treasure hunt, the data specialists, who work according to the principle of “trial and error,” also need a certain perseverance and tolerance for failure.

Markus Tautschnig, Data Solution Architect at thyssenkrupp Materials Services, and his team uncover hidden correlations from big data to sustainably improve process chains.

When, what, where?

The successes taste all the sweeter. Like the newly tailored distribution of orders in the logistics network, which was implemented as a “Data Analytics as a Service” project. “The starting point was Materials Services’ extremely complex logistics network, which consists of many sites with an incredible number of material movements of a huge product range. After all, our goal in business is for our customers to get the material they ordered – when and where they want it,” Markus Tautschnig recalls. As a theoretical physicist, Tautschnig worked at the Max Planck Institute before joining thyssenkrupp and is therefore familiar with challenging and complex problems. This time it was: “From where do we deliver which material best when to where in order to serve our customers and at the same time be as efficient as possible?”

Based on extensive data analysis, the project teamed up with logistics experts to optimize the distribution of orders in the network.  “With the help of automated simulations, we were able to calculate new scenarios and distribution routes based on millions of historical transactions,” explains Andrea Vennemann, project manager in Supply Chain Management at thyssenkrupp Schulte. “The DTO has thus enabled us to independently use the potential of data analytics and cloud technology for our core tasks. In the process, our colleagues at the DTO have provided us with continuous support,” says Vennemann.

Andrea Vennemann, project manager in supply chain management at thyssenkrupp Schulte, translates the findings from data analysis into added value for the entire value chain.

Transparency in real time

For users, the new system is extremely transparent. As the name suggests, “Data Analytics as a Service” is not limited to data analysis and process modeling. It also provides the responsible employees with the appropriate software services for controlling, monitoring and viewing in real time. This also includes the hypothetical calculation of scenarios. On request, the system simulates new material movements, calculates transport costs, and thus provides indications of how things will behave if certain parameters are changed.

“Acceptance at the application level is a fundamental factor in the success of our projects,” Markus Tautschnig knows. This starts with very mundane things, such as the question of how information is offered in the most user-friendly way: By e-mail? Or by dashboard? “After all, it wouldn’t be the first time that a machine had interesting suggestions ready for people, but they were then ignored because the communication didn’t fit,” Tautschnig continues.

“Value does not materialize in size”

What users don’t see, despite all the user-friendliness: Behind every small but important detail is an interdisciplinary and international team. This is not only composed of data analysis and data science. UX design, app development and the support of the system are also part of it. All united in the goal of turning something small into something significant. “The value of data doesn’t materialize by its size or quantity. The value is afterward an improvement in business. For example, through better customer service, lower costs or time savings,” says Ms. Vennemann. And if the nightmares don’t materialize, all the better.

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