Real-time spatio-temporal data analysis for monitoring logistics networks

B04 considers logistics distribution networks and investigates methods for prediction and uncertainty quantification, which integrate different data types such as barcode scans, arrival confirmations, weather, and tracing data. In the long run, different modeling frameworks (based on agents, flow/density/speed, or (stochastic) partial differential equations) will be integrated, so that the best prediction method and the data needed can be chosen in a dynamic, adaptive fashion for the specific situation and a given prediction accuracy.

In complex logistics and supply chain networks, the acquisition of tracking data representing the flow of entities through the networks has become - due to increased connectivity and cheaper hardware - state of the art. In addition to pure spatio-temporal data, related event data such as barcode scans at logistics hubs or arrival confirmations of airplanes is acquired. 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 such a context, optimal designs for data collection are not easily found.

This project will combine existing prediction methods and develop new methods for predicting arrival times in complex logistics networks (e.g., multi-modal transport networks). The novelty of the methods compared to the state of the art is (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. We will furthermore develop a new framework to determine the value of information for modeling arrival times. With this framework, users will be able to calculate the time and the locations for acquiring event or tracing data in the network as well as the temporal and spatial resolution that is necessary to provide arrival times of sufficient quality for their respective business needs. Thus, it supports the design of data collection in logistics networks.