Invernizzi, DC orcid.org/0000-0001-8178-9557, Locatelli, G orcid.org/0000-0001-9986-2249 and Brookes, NJ (2018) Applying Statistics to Improve the Performance of Nuclear Decommissioning Projects. In: Proceedings of the 2018 26th International Conference on Nuclear Engineering. ICONE26: 26th International Conference on Nuclear Engineering, 22-26 Jul 2018, London, UK. ASME ISBN 978-0-7918-5151-7
Abstract
Nuclear Decommissioning Projects and Programmes (NDPs) are characterized by significant risks, long schedules, and high costs that keep rising. Additionally, due to the NDP complexity and variety, it is extremely hard to understand which are the NDP characteristics that are associated with the NDP performance. This research takes the project management perspective, collects empirical information on NDPs and investigates this relationship between NDP characteristics and NDP performance, both through qualitative cross-comparison and quantitative statistical analysis based on the Fisher’s Exact Test (FET). In this paper, the results from the implementation of the FET applied on a pool of European NDPs are presented and discussed. Key takeaways are that some project characteristics present a stronger relationship with the project performance, while others do not show a significant association. Ultimately, qualitative and quantitative analyses complement each other to support the development of guidelines and improve the selection, planning and delivery of future NDPs.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds) |
Funding Information: | Funder Grant number National Nuclear Laboratory Limited NNL/UA/002 |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Apr 2018 10:17 |
Last Modified: | 07 May 2019 14:03 |
Status: | Published |
Publisher: | ASME |
Identification Number: | 10.1115/ICONE26-81428 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:129891 |