In almost every area of our lives, an immense amount of information and data is created every day. Dr. Vincent Dekker knows the potential of analysing these data streams and how this treasure of data can be used with the help of innovative technologies such as artificial intelligence (AI). In the Data Analytics and AI Powertrain team, he and his colleagues develop data-based solutions to further improve production processes and make Daimler's vehicles and services even better. In his interview, the statistics expert tells us why the digital transformation is a huge opportunity, and how important a culture of data-based decisions can be for companies.
Dr. Dekker, you and your team develop solutions to make large amounts of data usable. What excites you about this topic?
A lot of things, because the digital transformation offers completely new technological possibilities. This is a huge opportunity and, at the same time, a great challenge for us as a society and also for companies, of course. Data plays a central role in this. In order for us at Daimler to be able to help shape the future of mobility, we have to set the course for it today and create structures to use the data available in every department. At the same time, it is important to foster a culture of data-based decision-making throughout the whole organisation. For me it is very exciting to be driving this process forward myself.
What do you mean by a culture of data-based decisions?
At Daimler, thousands of decisions are made every day. Particularly, for important economic and strategic topics, tools such as, data analyses, market studies and much more come into play. In some decisions of the day-to-day business, we might also go with our gut feeling. That is a good thing and will certainly also be important in the future. However, artificial intelligence (AI) tools can often help us make better decisions, or they can even make them for us. We just need to use the potential of the existing data.
For digital transformation to succeed, we need to practise a culture of data-driven decision-making throughout the organisation. I find it very exciting to drive this process forward.
And that requires cultural change?
Yes. It is good for our colleagues in all departments to develop a sense of what is possible, when we use the data that surrounds us. And of course, this also includes being open to the use of these new technologies. That means “having the ability” on the one hand and "wanting to", on the other. To drive this change further, we have a trainer in our team who raises awareness among managers, such as the production management in our factories and also initiates training opportunities. There are numerous use cases for data analytics and AI, from production to service. It is important to banish the fear of "artificial intelligence" and at the same time to deal with it responsibly. Many data projects can be realised quite simply. We want to provide our colleagues with the necessary know-how for this. And for larger projects, we support them like a kind of internal service provider.
Can you give us an example of such a project?
My favourite project, which we are currently working on, starts with quality management. Not least for legal reasons, we keep production data records for every single part installed in our vehicles, over the entire service life of the vehicle. With this data, we can create added value for ourselves and our customers.
And how exactly do you create this added value?
In our project, for example, we use algorithms to restructure the data in such a way that it enables more in-depth error analyses. We can then compare the production data of previous weeks and identify patterns using intelligent analysis tools. A simple example would be: There were complaints about a certain component during quality control. All of these components have passed through machine X in plant Y. With this information, the colleagues in production can search for the source of the error much more efficiently and rectify it. In this way, we and our team make potential for improvement visible with a depth that was not possible before.
How does your team look like?
There are seven of us in total. Our team leader studied mechanical engineering and has already worked on digitisation projects in the past. Two other colleagues are responsible for data analysis and visualisation. Our Data Engineer also has a technical background and is the expert when it comes to retrieving data from production facilities. In addition, our trainer for cultural change has been with the company for more than 30 years. He has got a lot of experience from working in production. In the technical project management, I am responsible for the coordination of analytical topics. I work very closely with our PhD student on this. He researches explainable AI and is an absolute expert in his field. Our team is a good example of digital transformation, as most colleagues have familiarised themselves with the topic "on-the-job".
How did your personal path in becoming an AI expert and joining Daimler look like?
My path is a little bit special, because I originally studied economic science with a focus on macroeconomics. After completing my Master's degree I was a doctoral student at the University of Hohenheim, in Stuttgart, where I worked on statistical methods to measure the effects of tax policy on consumer behaviour. When I completed my dissertation in 2017, I continued to work at the university as a research assistant. Then somebody told me about an open job position at Daimler. I took a closer look at the tasks and at first I was surprised that my economics and statistics background is also sought and needed in the field of production technology.
So you started your career in the production environment. How were the first weeks for you?
Very good. I'm sure it was also because the topics excite me a lot. Artificial intelligence and data analytics are key fields that are already significantly shaping the industry today. What helped me on the one hand, especially in the beginning, is that we have a strong dynamic in our team and support each other. If I have questions about production-specific topics, my colleagues are always there to help me. And on the other hand, I contribute my knowledge on statistics. Our mix of expertise is very helpful for data analytics and AI. Our field is still so young that we try out new things almost every day and discuss solutions in front of the whiteboard. At Daimler, we have the opportunity to take this field to the next level.
One last personal question: If time travel were possible, where would you like to travel to?
I would travel to the 1970s. To experience one of the legendary concerts of Pink Floyd, Led Zeppelin or Queen would be simply amazing. This kind of well-crafted music, where instruments play the main role, that's what I love. I also play the guitar myself, maybe that's why (laughs).
Dr. Vincent Dekker (30) discovered mathematics as a play area for himself at an early age. While his parents eagerly watched the Formula 1 races on TV, the teenager compared the lap times and thought about what the perfect pit stop strategy could look like based on data. Born in Alkmaar in the Netherlands, the fan of numbers decided to study in Germany and first completed his bachelor's degree in economics followed by his master's degree in economic science at the University of Hohenheim, Stuttgart. After his studies, he remained at the university as a doctoral student and research assistant, until starting at Daimler in 2019 in the Data Analysis and AI Powertrain team. Today, when Dr. Vincent Dekker is not finding opportunities to use the full potential of data streams for Daimler, he enjoys being involved as a youth leader at his tennis club and lives his passion for cooking. "If it doesn't work out with the data, I'll open a restaurant," he jokes.