Improving Parcel Delivery Reliability and Flexibility Using Machine Learning Algorithms bij Bright Cape



E-commerce obtains a more and more prominent place in consumer sales over the last years. This results in an increasing pressure on supply chains to be flexible, fast and reliable. Especially, last mile deliveries, often done by parcel delivery companies like DHL, UPS and DPD, are important to live up to the ever increasing demands of the e-shopper. Currently, over 10% of deliveries are unsuccessful at the first attempt resulting in extra delivery costs and unnecessary traffic and carbon emissions. With Neighborhood Drop Off (NDO) Bright Cape, in collaboration with its partners, develops a solution that helps to increase first time hit rate in parcel deliveries. With the help of a local network of trusted neighbors missed deliveries will become a problem of the past.

What will you do?

For the development of the NDO ML module three core components have to be developed:•Develop a neighbour recommender system, which predicts the most preferred neighbors based on, among other things, time, location, parcel characteristics and previous drop offs and handovers

  • Develop a reward system(dynamic pricing)which determines the score (and thereby reward) for a neighbor per parcel drop off. The score should depend on, among other things,time, location, parcel characteristics and delivery company and e-shopper satisfaction
  • Develop APIs that connect the above mentioned algorithms to the external hosted platform

Both systems should be made in such way that the modules can be used on real time data(i.e. not only on historical data), especially in the case of the recommender system, performance is a key feature. Challenges during the assignment can be:

  • Representative datasets may not be available for all use cases at the start of the project. Generating fictitious data or adjusting sampled real-world data will probably be needed
  • The platform is developed by four partners collaborating on European level. Continuous alignment and feedback loops are required to make sure all solution components work well together

Do you have a data driven mindset and want to put this into practice in an energetic and vigorous team? Stop searching, because we have a match.

You have

  • Pragmatic and theoretical knowledge about statistics and machine learning algorithms
  • Excellent programming skills, preferably in Python, including strong awareness of computational complexity
  • Experience in developing REST APIs and with database management
  • A background in industrial engineering, econometrics, mechanical engineering, applied mathematics, physics, electrical engineering, computer science, or artificial intelligence

We offer

  • An entrepreneurial environment where you’ll be heard
  • Freedom in doing what you love
  • An innovative environment. We’re using data science to rethink and reshape many domains
  • The possibility to keep learning
  • Amazing colleagues and a young dynamic team
  • An international network
  • Yearly ski trip (that we count down to the moment we get back)