Participated Challenges

  • Kuehne & Nagel
  • Here Maps


  • Kuehne & Nagel's real problem of planning huge amounts of shipments all around the world
  • problem of sending different couriers to the same location to ship goods to customers which is a waste of resources
  • problem of inconsistency when handling location data provided by customers ->Huge Optimizations Possible!

What it does

  • normalize location data from different customer sources
  • provide additional meta information about courier routes and destinations (minimal street width, maximal curviness, building information, opening times, ...) using the here maps API
  • helps Kuehne & Nagel to plan shipments with route information and offers consolidations instead of using multiple half empty Excel files with sometimes 200.000

How we built it

  • The data was first cleaned and normalized using Python (with numpy and pandas).
  • At the same time we developed a Web application to simplify the way the end user interacts with our data.
  • The Web app is served via a Flask Server with a beautiful Front-End.
  • We used an informal Scrum approach by having multiple iterative sync meetings and brainstorming sessions.

Challenges we ran into

  • The Data Cleaning and Data Integration task was tough, since every customer uses another data format.
  • Unfortunately, we were not able to receive an extended here API key. Thus, it wasn't possible to retrieve other information than location data from the here API. In production, of course, the data can simply be retrieved using the here API and the production key.

Accomplishments that we're proud of


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