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Title: The resilience of asset systems to the operational risk of obsolescence : using fuzzy logic to quantify risk profiles
Author: Mulholland, Kieran
ISNI:       0000 0004 7224 4995
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2017
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This thesis sets out to explore possible methodologies to enable proactive obsolescence management for end users within the Built Environment. Obsolescence has shown to be a growing operational and financial risk as technology is further embedded into our buildings, seeking enhanced performance and connectivity. Obsolescence directly impacts the supportability of an asset system, manifesting into obsolescence-driven investments, which are typically managed reactively causing lifecycle costing complications. Gaps within academic literature and industry guidance have been identified herein and will be directly addressed by the research questions. The challenge of researching into obsolescence surrounds the commercial value of the required datasets, requiring a novel methodology to address the research problem. Further to this, the multi-stakeholder nature of supply chains, along with the unknown nature of obsolescence, has created a level of ambiguity within the datasets. Fuzzy Logic was adopted, above other options, to create an Obsolescence Impact Tool (OIT) that would enable the user to quantify the risk profile of obsolescence within asset systems. This model, along with an enhanced Obsolescence Assessment Tool (OAT), were both developed and tested within a two-year case study environment. Additional research questions were answered by analysing the reverse engineered original equipment manufacturers (OEM) sales catalogues. Through the combination of both the results from OIT and OAT, along with the analysis of OEM catalogues, a visualisation of the resilience of asset systems in respect to obsolescence is presented. The findings found herein provide evidence for the use of OIT and OAT for industrial application through the insights provided by data-driven models. The two models formulate a methodology that enables decision-making and proactive obsolescence management under uncertainty. The results of the OEM analysis provide explicit evidence that can immediately be used by the reader to enhance their obsolescence management plan (OMP). Evidence of the impact of sales strategies and how an end-user could utilise and reverse engineer the findings, hold potential for all Facilities Management teams. The findings culminate in a wide range of contributions that further the understanding of obsolescence within the Built Environment and importantly bridge some of the existing gaps. The Future Works chapter covers both observations made by the author and alternative methodologies that would provide further insight i.e. Type 2 Fuzzy Sets, Adaptive Learning Techniques, and Markov Chains.
Supervisor: Pitt, Michael ; McLennan, Peter Sponsor: Not available
Qualification Name: Thesis (Eng.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available