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Unveiling Bottlenecks in Logistics: A Case Study on Process Mining for Root Cause Identification and Diagnostics in an Air Cargo Terminal

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Service-Oriented Computing (ICSOC 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14420))

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Abstract

To improve processes in logistics, it is crucial to understand the factors influencing performance. To achieve this, process mining utilizes event data to extract insights into operational processes. In this paper, we present a case study conducted in an air cargo terminal, where process mining is applied to event data collected during package distribution. The primary objective is to identify the root causes of bottlenecks in the system. However, practical limitations, including noisy sensor data, scalability challenges, and abstraction limitations, require a different approach than conventional process mining projects. Building upon existing process mining techniques, we develop a two-fold approach to identify root causes at the data level and provide diagnostics at the business level. Through a comprehensive analysis of the provided datasets, we substantiate the effectiveness and practical applicability of our approach in analyzing root causes.

This work was supported by the InnoHK funding launched by Innovation and Technology Commission, Hong Kong SAR. Additionally, we thank Sebastiaan van Zelst for his support.

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Notes

  1. 1.

    For confidentiality, we pseudo-anonymized the datasets, preserving the relative relation between incidents. In this case study, we only present pertinent attributes.

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Authors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Chiao-Yun Li, Tejaswini Shinde, Aparna Joshi & Wil M. P. van der Aalst

  2. Fraunhofer FIT, Birlinghoven Castle, Sankt Augustin, Germany

    Chiao-Yun Li & Wil M. P. van der Aalst

  3. Hong Kong Industrial Artificial Intelligence and Robotics Centre Limited, Shatin, NT, Hong Kong

    Wanyi He, Sean Shing Fung Lau, Morgan Xian Biao Hiew & Nicholas T. L. Tam

Authors
  1. Chiao-Yun Li
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  2. Tejaswini Shinde
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  3. Wanyi He
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  4. Sean Shing Fung Lau
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  5. Morgan Xian Biao Hiew
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  6. Nicholas T. L. Tam
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  7. Aparna Joshi
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  8. Wil M. P. van der Aalst
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Correspondence to Chiao-Yun Li .

Editor information

Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Flavia Monti

  2. Technical University of Munich, Garching, Germany

    Stefanie Rinderle-Ma

  3. University of Seville, Seville, Spain

    Antonio Ruiz Cortés

  4. Sun Yat-sen University, Guangzhou, China

    Zibin Zheng

  5. Sapienza University of Rome, Rome, Italy

    Massimo Mecella

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Li, CY. et al. (2023). Unveiling Bottlenecks in Logistics: A Case Study on Process Mining for Root Cause Identification and Diagnostics in an Air Cargo Terminal. In: Monti, F., Rinderle-Ma, S., Ruiz Cortés, A., Zheng, Z., Mecella, M. (eds) Service-Oriented Computing. ICSOC 2023. Lecture Notes in Computer Science, vol 14420. Springer, Cham. https://doi.org/10.1007/978-3-031-48424-7_21

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  • DOI: https://doi.org/10.1007/978-3-031-48424-7_21

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