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

  • from appropriate data logging and event data extraction techniques from logistics systems
  • and appropriate conceptual modeling of logistics processes and systems
  • via process discovery and process replay techniques for multi-dimensional event data
  • for online and offline deviation detection and process comparison,
  • to predictions of process outcomes and online recommendations based on event data.

Publications

  • Unbiased, fine-grained description of processes performance from event data - Denisov, V.V., Fahland, D. & van der Aalst, W.M.P. (2018). Unbiased, fine-grained description of processes performance from event data. Business Process Management – 16th International Conference, BPM 2018, Sydney, NSW, Australia, September 9-14, 2018, Proceedings. (pp. 139-157). (Lecture Notes in Computer Science, No. 11080). Springer. Abstract Performance is central to processes management and event data Read More ...
  • The performance spectrum miner : visual analytics for fine-grained performance analysis of processes - Denisov, Vadim, Belkina, Elena, Fahland, Dirk & van der Aalst, Wil M.P. (2018). The performance spectrum miner : visual analytics for fine-grained performance analysis of processes. CEUR Workshop Proceedings, 2196, 96-100. Abstract We present the Performance Spectrum Miner, a ProM plugin, which implements a new technique for fine-grained performance analysis of processes. The technique uses Read More ...
  • Predictive performance monitoring of material handling systems using the performance spectrum - Denisov, V., Fahland, D., & van der Aalst, W. M. P. (2019). Predictive performance monitoring of material handling systems using the performance spectrum. In Proceedings – 2019 International Conference on Process Mining, ICPM 2019 (pp. 137-144). [8786068] Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/ICPM.2019.00029 Abstract Predictive performance analysis is crucial for supporting operational Read More ...

Staff

  • Boudewijn van Dongen - Boudewijn’s research focusses on conformance checking. Conformance checking is considered to be anything where observed behavior, needs to be related to already modeled behavior. Conformance checking is embedded in the larger contexts of Business Process Management and Process Mining. Boudewijn aims to develop techniques and tools to analyze databases and logs of large-scale information systems Read More ...
  • Dirk Fahland - Dirk is Associate Professor (UHD) in the PA group. He completed his PhD with summa cum laude at Humboldt-Univeristät zu Berlin and Eindhoven University of Technology in 2010. His research interests include distributed processes and systems built from distributed components for which he investigates modeling systems (using process modeling languages, Petri nets, or scenario-based techniques), Read More ...
  • Vadim Denisov - Position: PhD Student Room: MF 7.109 Tel (internal): 4065 Links: Courses Presentations Projects Publications External links: TU/e page Recent courses Recent presentations Recent projects Recent publications
  • Zahra Toosinezhad - Position: PhD Student Room: MF 7.109 Tel (internal): 6318 Links: Courses Presentations Projects Publications External links: TU/e page Courses Presentations Projects Publications

Former staff

  • Wil van der Aalst - Prof.dr.ir. Wil van der Aalst is a full professor of the Process and Data Science (PADS) group at the RWTH in Aachen (Germany) and a part-time professor in the PA group. His personal research interests include process mining, business process management, workflow management, Petri nets, process modeling, and process analysis. Position: HGL Room: MF 7.064 Read More ...
  • Hilda Fabiola Bernard - Position: PhD Student Room: MF 7.109 Tel (internal): Links: TU/e employee page Projects

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