University of Twente Student Theses
Enhancing Baggage Handling Duration Predictions for KLM : A Data-driven and Machine Learning Approach Using Camera and Sensor Data
Ochoa Barnuevo, Marco Luis (2023) Enhancing Baggage Handling Duration Predictions for KLM : A Data-driven and Machine Learning Approach Using Camera and Sensor Data.
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| Abstract: | Accurate baggage loading/unloading estimation is crucial for KLM's efficiency at Schiphol Airport. However, their current estimation tool lacks data validation and needs improvement, leading to inaccuracies and inefficiencies turnaround process. In this project, we aim to address the challenges faced in KLM's baggage handling process, which impact operational efficiency and customer satisfaction. The research questions were designed to investigate how data-driven and Machine Learning methods using camera and sensor data can enhance the accuracy of baggage duration predictions at Schiphol Airport. For this, a comprehensive analysis of the current system, empirical data, and modeling techniques were covered. |
| Item Type: | Essay (Bachelor) |
| Clients: | KLM, Amsterdam, Netherlands |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Subject: | 85 business administration, organizational science |
| Programme: | Industrial Engineering and Management BSc (56994) |
| Link to this item: | https://purl.utwente.nl/essays/96204 |
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