Print Email Facebook Twitter Data-driven enterprise risk management of critical infrastructures Title Data-driven enterprise risk management of critical infrastructures: The Dutch railway sector Author Chün, Jean-Paul (TU Delft Technology, Policy and Management) Contributor de Bruijn, J.A. (mentor) Papadimitriou, E. (graduation committee) Krzeminski, J. (graduation committee) Degree granting institution Delft University of Technology Programme Engineering and Policy Analysis Project Master Thesis Date 2021-09-14 Abstract The Dutch rail sector is one of the most heavily used rail networks in Europe. It plays a critical role in the domestic transportation of passengers. Disruptions in the operation of one organisation can have a cascading effect on others within the sector. Risk management needs to identify and address these weaknesses to prevent largescale disruptions. The risks a rail organisation faces are diverse, ranging from financial risks to strategic risks. The enterprise risk management methodology addresses all the risks of an organisation to reduce the negative effect and seize opportunities. In recent years, there has been increased emphasis on data-driven work, including within the risk management domain. This research explores the implementation of data-driven work in the enterprise risk management of the Dutch passenger transporting rail sector. This research uses a comparative case study to explore the novel research field. The case study data is collected from interviews and desk research. This research concludes that datadrivenwork adds value to the enterprise risk management of Dutch rail organisations. Data-driven enterprise risk management improves the predictive capabilities of rail organisations. In addition, it enables real-time monitoring of risks. Hence, it supports the decision-making process more precisely and accurately. Subject Enterprise risk managementData-driven workDutch rail sectorCase Study ResearchInterviewDesk researchReal-time risk monitoringRisk prediction To reference this document use: http://resolver.tudelft.nl/uuid:1d94922a-0bbf-4fd6-b27e-f65c3ee12582 Part of collection Student theses Document type master thesis Rights © 2021 Jean-Paul Chün Files PDF Master_thesis_Jean_Paul_Ch_n.pdf 1.53 MB Close viewer /islandora/object/uuid:1d94922a-0bbf-4fd6-b27e-f65c3ee12582/datastream/OBJ/view