Cargo distribution analysis: a process mining approach

Process Science, vol. 3

Abstract

Abstract Air cargo terminals are indispensable hubs in the global trade network, yet inefficiencies in their cargo handling processes can trigger significant disruptions, manifesting as costly delays, inflated operational expenses, and a decline in service quality, thereby jeopardizing intricate supply chains. To gain a comprehensive understanding of operational efficiency, we leverage process mining to analyze event data that track the transportation journey of each cargo item from event-driven systems. Following the process mining project methodology, we present an end-to-end pipeline that diagnoses bottlenecks within the cargo distribution process and predicts future distribution flows based on historical event data. Real-world event data are often characterized by inherent complexities, presenting challenges in data quality and scalability. Addressing these challenges, our standardized effort focuses on processing event data from supporting information systems to automatically detect and diagnose the root causes of inefficiencies to deliver actionable, data-driven insights. These insights are integrated into an interactive GUI that supports decision-making, enabling proactive measures and streamlining operations.

Authors 8

1. Chiao-Yun Li corresponding Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Ahornstraße 55, 52074, Aachen, Germany

2. Tsung-Hao Huang Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Ahornstraße 55, 52074, Aachen, Germany

3. Marco Pegoraro Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Ahornstraße 55, 52074, Aachen, Germany

4. Sean Shing Fung Lau
   Affiliation as printed

   Hong Kong Industrial Artificial Intelligence and Robotics Centre Limited, Kowloon Tong, NT, Hong Kong

5. Morgan Xian Biao Hiew
   Affiliation as printed

   Hong Kong Industrial Artificial Intelligence and Robotics Centre Limited, Kowloon Tong, NT, Hong Kong

6. Ivy Yan Nei Law
   Affiliation as printed

   Hong Kong Industrial Artificial Intelligence and Robotics Centre Limited, Kowloon Tong, NT, Hong Kong

7. Benny Drescher
   Affiliation as printed

   Hong Kong Industrial Artificial Intelligence and Robotics Centre Limited, Kowloon Tong, NT, Hong Kong

   INC Innovation Center GmbH, Munich, Germany

8. Wil M. P. van der Aalst Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science, RWTH Aachen University, Ahornstraße 55, 52074, Aachen, Germany