by Klaudia Zeleny, Júlia Bergmann, Dávid Gyulai, Maik Frye, Robert H. Schmitt
Abstract
Similar to other domains, logistics operations generate a huge amount of data that is valuable for process digitization. Order consolidation is the process of transforming orders into packages, and it must comply with several rules. Some of these rules rely on logistics experts’ routines and individual knowledge that is typically hard to automate. This paper conducts a framework to model logistics network from transportation log data to support data-driven decision making. The required data pre-processing steps are discussed to learn the current consolidation rules. The ultimate goal of the research is to identify efficient consolidation rules assuming stochastic parameters.
Keywords: logistics operation; temporal networks; data model
Video presentation
Presenting author
 | Name: Affiliation: Email: | Klaudia Zeleny Corvinus University of Budapest, Hungary — |
