Cargo distribution analysis: a process mining approach - Chiao-Yun Li; Anton Antonov; Tsung-Hao Huang; Marco Pegoraro; Sean Shing Fung Lau; Morgan Xian Biao Hiew; Ivy Yan Nei Law; Benny Drescher; Wil M. P. van der Aalst (Process Science, 2026)
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.
Keywords: 650; Process mining project methodology; Root cause analysis; Data processing; Next activity prediction; Logistics