University of Twente Student Theses


Predicting Arrival Times of Container Vessels : A Machine Learning Application

Bussmann, N.H. (2019) Predicting Arrival Times of Container Vessels : A Machine Learning Application.

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Abstract:A Dutch Logistic Service Provider (LSP) currently applies a reactive attitude towards arrival time information that is solely based on the carrier’s sailing schedule. However, this sailing schedule historically appears to be unreliable: 20% of the orders that the LSP executed last 2.5 years, did not arrive on time. Since LSPs remain dependent on carriers from the container shipping industry, a platform capable of delivering and processing accurate information is essential for increasing efficiency, visibility and customer service. Not being able to exactly know when an order will arrive, negatively affects the businesses of both the LSP and the customer in terms of decreased efficiency and increased costs. We therefore propose a more proactive attitude towards arrival times by means of a predictive model based on historical order data. We applied the Random Forest technique to this end. The model is able to predict the deviation in the arrival time that is provided by the carrier in their sailing schedule in advance of actual shipment. Deployment of the actual prediction algorithm is expected to lead to improved business processes in terms of increased efficiency and decreased costs for both the LSP and the customer.
Item Type:Essay (Master)
CAPE Groep, Enschede, Netherlands
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:30 exact sciences in general, 50 technical science in general
Programme:Industrial Engineering and Management MSc (60029)
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