The Internet of Things (IoT), a frequently used term, is the basis of many new technological developments. IoT consists of worldwide and everyday internet connected devices and sensors, which all exchange information. It is estimated that a small 25 billion(!) devices and sensors are connected to the internet which exchange data continuously. All these connected devices and sensors (such as cameras, iBeacons, telephones but also smart speakers, wearables and thermostats) generate an explosion of raw data. The next link after IoT to bundle this collected raw data is Artificial Intelligence (AI). Artificial Intelligence is a collective name for every technique that allows a system to perform almost human actions. Indirectly this is the merit of Machine Learning (ML), a technology that focuses on specific sub-algorithms of AI to achieve optimizations in a process.

The human as a metaphor

  • Internet of Things is raw data just like our entire body that performs all kind of physical actions.
  • Artificial Intelligence provides insight into these data and determines which actions must be performed exactly just like our brain does.
  • Machine Learning is the learning method behind training a specific task as we teach the brain to make the right choice or to improve a certain action.


  • Artificial Intelligence has grown through Machine Learning. Apple with Siri is a good example of this.
  • Artificial Intelligence is not possible without Machine Learning, Machine Learning can do without A.I.
  • The moment Machine Learning reaches the point, that the system can interact in a convincing way with people and can make independent choices, is when we call it Artificial Intelligence.

Machine learning for logistics and warehouse operations

But let’s go back to logistics and warehouse operations and the key question: what’s in IT for us? Changing priorities within the warehouses require a different approach. Priorities nowadays need to be adapted in no time and can be used based on their own formulated business protocol. Technological developments can make the difference and contribute to improvement throughout the entire supply chain. Few WMS suppliers have implemented this technology, but we are still more or less at the beginning of a long and fascinating challenge with the current WMS applications. Aratus A-Core WMS enters this challenge with confidence, with our network portfolio Supply Chain Apps. Some examples of optimization within the entire warehouse operation are:
  • Stock management
  • Inventory optimization
  • Predictive stock
  • Route and transport optimization
  • Waveless order picking
  • Location Intelligence (Put Away)
In concrete terms, these new technological developments yield significant savings in stock management. The clever combination of data and algorithms ultimately yields an unbreakable formula. The outcome of that formula says that almost every stock, for a third or more, consists of ‘dead stock’. This is inventory that in a certain period is regarded as surplus stock or stock that has not been used during a certain, predetermined period. With Aratus A-Core WMS we say; give us a little more DATA please! Do you want to know more about AI and machine learning and how this can help your logistic operations? Call us or send us an email and we will be in touch soon.