Data-driven enterprise risk management of critical infrastructures

The Dutch railway sector

Master thesis (2021)

Authors

J.S. Chün Technology, Policy and Management

Contributors

J.A. de Bruijn Organisation & Governance - TPM (supervisor 1)
E. Papadimitriou Safety and Security Science - TPM (supervisor 2)
J. Krzeminski EY Netherlands (supervisor 2)
Faculty
Technology, Policy and Management
Copyright: © 2021 Jean-Paul Chün
Case Study Research Interview Enterprise risk management Data-driven work Dutch rail sector Desk research Real-time risk monitoring Risk prediction
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Published Date

14-09-2021

Language

English

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Faculty

Technology, Policy and Management

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 datadriven
work 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.