LOGISTAR (“Enhanced data management techniques for real-time logistics planning and scheduling”) is an EU H2020 project that aims at allowing effective planning and optimizing of transport operations in the supply chain by taking advantage of horizontal collaboration relying on the increasingly real-time data gathered from the interconnected environment.
LOGISTAR is a decision support system for companies in the logistics sector. The system helps logistics companies to make better decisions regarding the planning of their orders to transport goods. This is achieved with the development of software algorithms to recognize disruptive events in real-time, predict delivery times, and optimize the planned orders globally and locally.
It provides a real-time information tool to support the decision-making process when the goods are in motion, allowing monitoring of their location and status using IoT, AI, Open Data, Network Modelling.
DunavNET contributes to the LOGISTAR project by working on mechanisms, techniques, and enablers to allow large-scale, multi-faceted, real-time data in real logistics networks.
As the use of IoT in logistics generates a lot of data coming from different sources and require big data analytics, DunavNET works on Big Data Analytics to enable the business to predict the likelihood of an event and take timely business decisions based on data volume, value, velocity, and veracity.
In addition, DunavNET is working on providing insight into the transportation process, by using results from the innovative food supply chain monitoring process.
The efforts will result in enabling a new comprehensive set of use cases and sharing of assets with different actors in the ecosystem providing new market opportunities in the logistic information services sector. Food supply management and functional ink technology applied to other stakeholders in the project develop new business models focused on data and high-value services delivery allowing “sharing” of transport assets to find the most profitable exploitation strategy for the actors.