ARCOM

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Adams, J (2019) Dynamic criticality analysis of industrial assets and system, Unpublished PhD Thesis, Institute of Manufacturing, University of Cambridge.

Al Asali, M W (2020) Craft-inclusive construction: design strategies for thin-tile vaulting, Unpublished PhD Thesis, , University of Cambridge.

Anagnostopoulos, I (2018) Generating as-is BIMs of existing buildings: from planar segments to spaces, Unpublished PhD Thesis, Department of Engineering, University of Cambridge.

Ariyachandra, M F (2021) Automating the generation of geometric information models to support digital twinning of existing rail infrastructure, Unpublished PhD Thesis, , University of Cambridge.

Bartlett, H V (2006) Understanding the implementation of sustainability principles in UK educational building projects, Unpublished PhD Thesis, Centre for Sustainable Development, University of Cambridge.

Baumgärtner, C E (2000) Collaboration between engineering consultants and their clients: characteristics of success, Unpublished PhD Thesis, , University of Cambridge.

Busic-Sontic, A (2019) Energy efficiency investments in residential buildings: does personality matter?, Unpublished PhD Thesis, , University of Cambridge.

Jimoh, I (2021) What explains the efficiency of major public project delivery in Nigeria?, Unpublished PhD Thesis, , University of Cambridge.

Jin, Y (2018) Supervised learning for back analysis of excavations in the observational method, Unpublished PhD Thesis, , University of Cambridge.

Konstantinou, E (2018) Vision-based construction worker task productivity monitoring, Unpublished PhD Thesis, Department of Engineering, University of Cambridge.

Lloyd, C A (2020) Modular manufacture and construction of small nuclear power generation systems, Unpublished PhD Thesis, , University of Cambridge.

Mándoki, R (2022) The social sustainability of standardisation in the Hungarian residential building sector, Unpublished PhD Thesis, , University of Cambridge.

Montali, J (2019) Digitised engineering knowledge for prefabricated fac?ades, Unpublished PhD Thesis, Department of Engineering, University of Cambridge.

O'Brien, S (2022) Critical infrastructure organisation management: an analysis of the transition to the Industry 4.0 era, Unpublished PhD Thesis, , University of Cambridge.

• Type: Thesis
• Keywords: personnel; security; organisational theory; systems theory; automation; data management; decision making; governance; information management; organisational performance; case study; document analysis; workshops; culture; technological change; interview
• ISBN/ISSN:
• URL: https://doi.org/10.17863/CAM.96902
• Abstract:
  Critical infrastructure systems (CISs) provide the services that are vital for the economic prosperity, security, and well-being of society. These include power transmission and distribution, telecommunications, transport, and water distribution networks, to name a few. CISs are comprised of both physical and digital assets that provide infrastructure services but require interaction with people to operate. Through sociotechnical systems theory, we can model CISs through the interactions between social and technical factors that influence organisational performance, which is directly linked to the quality of the infrastructure services provided. The task of managing CISs involves not only the effective monitoring of physical assets, system interdependencies, and emerging risks and trends, but also the management of organisational aspects including people, processes, and working cultures. The interaction of these aspects during CIS operations is formalised within the field of organisational theory. Developed society is currently experiencing a technological revolution, often termed as Industry 4.0, whereby the operational and management capabilities of CISs and organisations are being transformed through the use of bespoke technologies and system analytics. These technological advancements are allowing organisations to gain new insights into how they can operate, maintain, and protect their systems and assets more effectively. The idealised Industry 4.0 model envisions that all data capture, storage, and analysis processes will be fully automated to enable automated decision-making with minimal human intervention. However, advancements of this type will change how critical infrastructure (CI) organisations are structured and managed. At present, organisational theory lacks the ability to capture and provide governance on the evolving relationships between technologies, people, and processes in CIS management. This research addresses this shortcoming by critically analysing data management structures and practices in CI organisations and developing an analytical framework that captures the interdependencies between technical and social elements in complex systems. It introduces the Data and Information Flow (DIF) model, which provides a framework that surpasses the capability of existing methods by capturing the movement and interactions between data, information, management systems, people, and processes in organisations. The research approach and subsequent development of the DIF model design involved a combination of reviewing existing organisational theory methods and engaging with five case study organisations from the energy sector to investigate and comparatively assess their organisational resource and data management strategies. The research also sought to understand the extent to which the case study organisations have adopted Industry 4.0 advancements in practice. Qualitative data collection methods including interviews, group workshops and webinars, and document analysis were applied. The research findings provide unique insight into how organisations structure both technical - i.e., data, information, and management systems - and social - i.e., personnel and processes - entities. The DIF model visually captures and characterizes the organisational practices, resources, and inherent relationships that influence and determine management decisions. The case study assessments show clear evidence of the existence of and potential for further automation in data capture and analysis processes to support system management and decision-making in CI organisations. However, they highlight inefficiencies in system operations due to the vast amount of human input and influence required in management processes, data quality challenges, and insufficient technical capability in existing management systems and software. These inefficiencies, in addition to social and cultural disruptions caused by technological changes, are limiting the extent of the digital transition and realisation of the Industry 4.0 ideal in CI organisations. This can only be achieved if an organisation's structure and the interactions between all available resources can be assessed and optimised in a combined manner. The DIF model offers the ability to map these wider interactions.

Pelenur, M (2014) Retrofitting the domestic built environment: Investigating household perspectives towards energy efficiency technologies and behaviour, Unpublished PhD Thesis, , University of Cambridge.

Robertson, B (2020) On-site installation flexibility for disruption management in modular off-site construction systems, Unpublished PhD Thesis, , University of Cambridge.

Tomašević, V (2004) Developing productive relationships in the construction industry, Unpublished PhD Thesis, Department of Engineering, University of Cambridge.

Vick, S (2018) Automated spatial progress monitoring for asphalt road construction projects, Unpublished PhD Thesis, Department of Engineering, University of Cambridge.

Zomer, T (2021) Institutional pressures and decoupling in projects: the case of BIM Level 2 and coercive isomorphism in the UK's construction sector, Unpublished PhD Thesis, , University of Cambridge.