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

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.