Commissioned by postal company Sandd, we developed an intelligent system for automatic diagnosis and optimization of the logistics postal chain. In the chain of collection, distribution, sorting and delivery of mail items, many factors can disrupt the process. Instead of waiting for complaints from senders or recipients, a self-learning control network anticipates unforeseen situations.
These situations relate, for example, to deviations from the planning with regard to the supply of mail, equipment and personnel. Smart algorithms compare the expected logistics chain with the digitally observed chain. Both mail and equipment are tracked using geotracking. In the case of excessive deviations, escalation takes place.