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Process Mining in Logistics

Process Mining in Logistics is a joint project of the Data Science Center Eindhoven and Vanderlande industries.

Description

Logistics processes are notoriously difficult to design, analyze, and to improve. Where classical processes are scoped around the processing of information associated to a specific unique case, logistics deals with physical objects that are grouped and processed together with other physical objects in one process at one or more physical locations, then distributed and later on re-aggregated with other physical objects in another process at other physical locations. In essence, logistics deals with numerous processes, cases, and objects that interact with each other in a multi-dimensional fashion. On one hand, this subjects logistics processes to many external influences which can have a negative impact on process outcomes and process performance. On the other hand, when analyzing the performance of flows across networks of logistics, the multi-dimensional nature is especially prevalent and existing data-driven process analysis techniques such as process mining which assume a single viewpoint cannot be applied.

Vanderlande is the global market leader in baggage handling systems for airports and sorting systems for parcel and postal services, and also a leading supplier of warehouse automation solutions. The company recognizes the emerging trend of more data driven business models and addressed ‘big data’ a key topic on the technology roadmap. Therefore, under the umbrella of the Data Science Impuls program, the DSC/e and Vanderlande joined forces in a research project.

The project runs from September 2016 until August 2020.

Project Objectives

The goal of the joint research project of DSC/e and Vanderlande is to lift process mining to the multi-dimensional space of logistics, and to allow analyzing logistics processes and systems from all relevant angles and viewpoints. By having thorough and fast insight into logistics and business processes, improvements can be found, predicted, and implemented at Vanderlande delivered logistics solutions. We aim to achieve this lift for the entire process mining spectrum

Staff Involved