A pan-European success story, the Danobat challenge

The Danobat challenge called for startups to provide an artificial intelligence-based solution for modelling, analysing and processing data extracted from machines and production systems. It turned from an initiative with regional focus to a pan-European challenge. Let’s hear what the protagonists of this success story had to say!

Danobat is a Basque manufacturer of high value-added solutions in the field of grinding, turning, punching and bending in the aerospace, railway, automotive, energy, oil and gas, and metal forming sectors. The challenge they forumalted consisted in incorporating artificial intelligence-based solution for modelling, analysing, and processing data extracted from machines and production systems to be able to provide their clients with a sophisticated service as well as to develop a new business model around it.

Gala Maturana, Senior Business Creation Manager at EIT Manufacturing West, supported the challenge from the EIT Manufacturing side. How did the challenge evolve?

GM: During the first stage of the challenge, ieTeam, which is a Basque strategy consulting firm and EIT Manufacturing’s network partner, worked with Danobatgroup to outline their innovation priorities and to elaborate something specific enough to warrant an open call to solve it. The second stage of the project involved publishing the open call for startups: this was accomplished by the three organisations – with EIT Manufacturing granting the elevation of the call at a European level.

My colleagues from all over Europe reached out to the relevant start-ups they work with in order to attract their attention to the challenge. We, at the same time, opened the call to any other European startups that wanted to offer their solution. This is, in essence, how the regional challenge went pan-European and achieved a much vaster outreach than it could have done if managed locally.

Danobatgroup belongs to the EIT Manufacturing community as part of Mondragon Group. In fact, this was the first time they worked with us to address one of their challenges. Was it hard to convince them to try a pan-European approach?

GM: No, not at all. Danobatgroup is focused since long ago on international markets and, therefore, it is quite natural for them to look everywhere for technologies, in Europe and worldwide. Their open mindset and hunger for innovative initiatives, led by Nerea Aranguren, makes them the perfect organisation to work with.

Nerea Aranguren, Innovation Director at DanobatGroup, was the driving force behind this initiative. Nerea, how was the overall experience working with ieTeam and EIT Manufacturing in tandem?

NA: Undoubtedly, the formula used has been valid, however, the open nature of the defition of the challenge might have somwehat complicated the selection process. The bearing technology on which this initiative has hinged (Artificial Intelligence) is blooming, it’s enjoying a state of rapid growth and innovation, which has made our approach constantly evolve as we acquire new knowledge and encounter more companies. Overall, this experience has been highly enriching both in terms of the knowledge gained about the innovative AI ecosystem and the valuable experience of collaborating in Open Innovation with external partners.

Do you have a key take-away from the challenge?

NA: The experience of working together was highly satisfactory. IeTeam provided experience in the management of such kind of initiatives, whereas EIT Manufacturing’s involvement was twofold: it contributed to raising awareness of the challenge and to generating a powerful attraction effect by giving the startups international recognition and nurturing interest in the call. The success of this project can be attributed to our collaboration with skilled professionals with well-defined roles.

Could you share your views on how the project evolved so far, Gala?

GM: Firstly, I would like to highlight the quality of the start-ups that applied for the challenge. A wide range of solutions was presented, that offered a variety of alternatives for Danobat to choose from. Some were adding to the already existing internal capabilities, while some brought completely different angles to the challenge, making it a bit riskier but also a greater chance to learn from it. And secondly, it was great to see the Business Creation team working together, gathering technologies from all over Europe to propose the best solutions out there. I really admired everebody’s eagerness to work together to support Danobatgroup in their scouting effort. I very much enjoyed the collaboration!

Felix Kraft, Co-Founder of ai-omatic, what does winning the Danobat challenge mean for you?

FK: It means a lot to us that a such a big company like Danobat has chosen us – a young German startup, among the top 3 technologies to be implemented in their premises. This partnership is a great opportunity to work in the Basque region, a highly industrialised area with many important manufacturing companies. This could, in fact, be the first of many projects to come in the region. In the end, being part of EIT Manufacturing brought us a cross border opportunity of cooperation between Germany and Spain which we would not have found by ourselves, and that is a great gain for us!

How has the process been?

FK: The Challenge process itself has so far been great. Everything is really structured and transparent. That was a very positive experience which we appreciate a lot. We are looking forward to working with the actual data available, because this may be a point of departure for our learning curve to propose a high-end solution that can be implemented and further escalated.

Is there anything in particular you have learned so far?

FK: We have acknowledged that these processes take time, but in the end are worth the effort. On top of that, we learnt to question our ambition. Previously, we had contemplated developing ourselves a hardware which could offer all of this as a full-service. Our interaction with Ideko, however, helped us discover that, tipically, comapanies already have data-generating sensors, so there is no point in going into that direction. The conclusion is that we will focus on the algorithm to provide our customers a ready-to-use digital maintenance assistant software to prevent downtimes in the future.