The Lufthansa Group is about much more than just transporting passengers and air freight from A to B. The subsidiary company I work for is called “Lufthansa Industry Solutions” (LHIND for short). As well as dealing with the exciting challenges faced by the airline industry within the Lufthansa Group, we also work on external projects. Our customers come from a wide range of industries, including air travel, logistics and transport. They come from the manufacturing and automotive industries, and are active in publishing and tourism, or in the energy and healthcare sectors. But whatever their industry, they all face the same, huge challenge of our time: They have to structure their IT along the entire value chain so that it reduces costs and simultaneously increases revenues and efficiency in the long run. In short: It is about companies’ future viability.

LHIND helps companies to digitize and automate their business processes – from medium-sized to DAX-listed companies. In doing so, we do not just focus on the IT needed, but on our customer’s business as a whole, bearing in mind the internal and external challenges it faces. This is because digital transformation affects a company’s entire structure and culture, and reaches beyond company borders to collaboration with partners, customers and suppliers. We combine the project experience and industry expertise we have gained with our comprehensive services and technology portfolio. This is why we work together in interdisciplinary teams. Whether you need a cloud, SaaS or data analytics – as an IT consultancy and systems integrator, LHIND covers the entire spectrum of IT services.

We always place the highest demands on security and quality – especially because our roots are in aviation, a highly digitized, security-sensitive industry. 

This is also the reason I really enjoy working for LHIND: Here at LHIND, people from many different areas of Data Science come together and made it possible for me to learn about many different aspects of machine learning such as statistical learning theory, Bayesian Networks, Gaussian Processes, the EM algorithm & GMMs and Bayesian A/B tests.

During an upcoming presentation at the Data Natives Conference in Berlin, I will talk about the use of analytics cases on several projects that I have been working on for theLufthansa Group. This includes an analysis of the new business class seat and the prediction and optimization of the fuel and time an aircraft will need. Finally, we also discuss passenger data and the need to predict whether or not a passenger will show up. The motivation behind most of these projects is to prevent delays. During the presentation, however, I will talk in detail about an even more technically appealing case, where we predict the arrival time of an aircraft at the moment of departure. The model has already been rolled out and is under evaluation.  We plan to extend it using worldwide aircraft geodata.

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