Real-time truck tracking is crucial in logistics: to enable accurate planning and provide reliable estimation of delivery times, operators build detailed profiles of loading stations, providing expected durations of truck loading and unloading, as well as resting times. Yet, how to derive an exact truck status based on mere GPS signals?

So far in the series we have built a trivial logistics simulation runtime. At this point it is only capable of finding the fastest route between two locations. This is implemented as a form of A* algorithm that uses predefined travel times.
Let’s extend the implementation and demonstrate how we can “plug” different models into the simulation runtime.

In the previous article (Part 1: Trustbit Logistics Hackathon - Behind the Scenes) we talked about organizing our Logistics Hackathon on Sustainability - how it came to be.
Let’s get to the technical details now.

A look behind the scenes of our Hackathon on Sustainable Logistic Simulation in May 2022. This was a hybrid event, running on-site in Vienna and remotely. Participants from 12 countries developed smart agents to control cargo delivery truck fleets in a simulated Europe.

On May 13th 2022 the hybrid "Trustbit Hackathon: Sustainable Logistics Simulation" was held at the beautiful Parkhotel Schönbrunn and was also open for remote participants from all over the world. We, a team of three data scientists, took part in it on-site and we want to share a bit about the great experience we had.

Emissions. Assume you own a truck company: your company takes transport orders from customers who can goods delivered from point A to point B. How would that work?

Within the framework of our Innovation Incubator, the idea arose to develop some tools to facilitate the post-processing of Event Storming sessions conducted in Miro. In this blog post, I will show you which text preparation steps are necessary to be able to analyse sticky notes from event stormings and how this analysis can be carried out with the help of the spaCy library.

Machine Learning (ML) is often mentioned in the same breath as big data. Yet, it can do much more than just exploring large datasets. This article gives you an overview of the most important techniques to use ML even if you actually don't have enough data.