Real-Time Spatio-Temporal Data Analysis for Monitoring Logistics Networks
In complex logistics and supply chain networks, the acquisition of tracking data representing the flow of entities through the networks has become state of the art. The goal of tracking entities is to improve transparency and predict the state of the network. An important value for operations is the estimated time of arrival of entities at different nodes of the network. The respective business goal determines the requirements for the forecasting procedure: it might be necessary to detect a delay in a container ship transport as early as possible (weeks before the arrival) to be able to send a replacement for urgent parts by air. Or it might be necessary to predict the arrival of trucks within the next hour as accurately as possible to manage the traffic at logistics sites. However, acquiring data is costly in terms of money, energy used by sensors, and required IT infrastructure.
In this project, we will develop new methods for predicting arrival times in complex logistics networks (e.g., multi-modal transport networks). Our methods will enable (a) the integration of different data types, e.g., event, weather, and tracing data, (b) the ability to cope with changes in the underlying logistics network in real-time, and (c) the ability to communicate uncertainty in predictions, especially in case of tracing data or weather forecasts of limited reliability.