The Resilience of Asset Systems to the Operational Risk of Obsolescence: Using Fuzzy Logic to Quantify Risk Profiles
Total Page：16
File Type：pdf, Size：1020Kb
UNIVERSITY COLLEGE LONDON THE RESILIENCE OF ASSET SYSTEMS TO THE OPERATIONAL RISK OF OBSOLESCENCE: USING FUZZY LOGIC TO QUANTIFY RISK PROFILES A THESIS SUBMITTED TO UNIVERSITY COLLEGE LONDON FOR THE DEGREE OF DOCTOR OF ENGINEERING KIERAN MULHOLLAND DEPARTMENT OF CIVIL, ENVIRONMENTAL AND GEOMATIC ENGINEERING UNIVERSITY COLLEGE LONDON 2017 0 I, Kieran Mulholland confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. SIGNATORIES ……………………………………. Kieran Mulholland Research Engineer, Author ……………………………………. Michael Pitt 1st Academic Supervisor ……………………………………. Peter McLennan 2nd Academic Supervisor ……………………………………. Wayne Partington Industry Supervisor ……………………………………. Paul Francis Industry Supervisor ACKNOWLEDGEMENTS Firstly, I would liKe to thanK my two supervisors who have helped and supported my research from the beginning; Professor Michael Pitt and Course Director Peter McLennan have both been a consistent source of Knowledge and guidance. Equally, the insight and exposure required to undertake this research would not be possible without the continued bacKing of the industry sponsor. Specifically, Managing Director Paul Francis and Head of Asset Management Wayne Partington have been unequivocally available for advice and have provided clarity to every step of this project. To all of the above and my friends and family, I would liKe to thanK you all; without your help during the last four years, this would not have been possible. 1 ABSTRACT 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. 2 IMPACT STATEMENT This thesis contains three key elements that could be extracted and applied within an industrial application. These elements contain sub-elements that in themselves provide individual benefit to the user, however, for more detail please see Discussion chapter 5.0. The Obsolescence Assessment Tool (OAT) is an indexing methodology that has been continually tested within this thesis, following a pilot study within a Master of Research (MRes) dissertation. This decision aiding tool enables an Asset Management or Facilities Management team to quicKly use the data they already possess to provide insight information regarding their asset registers. OAT will empirically evaluate which components require attention due to a weaKness to obsolescence, as a result of unavailable mitigation options. This tool holds the potential to strategically assign resources, whilst avoiding unforeseen lifecycle investments into systems that have reactively identified obsolescence. The Obsolescence Impact Tool (OIT) is a risK assessment tool that was entirely developed and tested within this thesis, adopting a FuzZy Logic architecture to account for ambiguous or imprecise input data. OIT gives an enhanced insight into an asset system’s components with the added content of probability and impact. The most important and vulnerable components will be identified using a wider array of traditional onsite datasets. This tool has shown the potential to produce data-driven evidence, upon which, a Cost Benefit Analysis can be drawn for obsolescence mitigation strategies. The reverse engineering of OEM sales catalogues has produced a range of evidence of the effect of obsolescence on component cost and availability. Probability density profiles can be extracted directly from the Results chapter of this thesis and applied into BMS systems across the industry. The results validate a new methodology and how it can be applied within an Obsolescence Management Plan (OMP) for an Asset Management or Facilities Management team. The results enable data-driven decision making and a more holistic approach to proactively managing obsolescence within the Built Environment. The above three Key elements in practice would combine within a single OMP document. The implementation of these three steps alone will enable proactive and ultimately evidential based decision making. In their respective components and collective whole, they contribute to several pieces of new Knowledge. The challenge is reapplying such steps to new systems, or similar systems in a new context, the author strongly highlights that whilst the results are practical and insightful, they are not all necessarily directly transferable. However, there are profiles and conclusions deduced from the results of this thesis that will be useful on other sites, the methodology and models would need to be reiterated with the new localised datasets. This is just the beginning of incorporating more data-driven decision aiding tools into an industry that faces growing risK profiles from complex problems. 3 CONTENTS 1 INTRODUCTION ............................................................................................ 16 1.1 Problem ............................................................................................................ 17 1.2 Obsolescence .................................................................................................... 18 1.3 Long-Life Asset Systems .................................................................................... 24 1.4 Systems Resilience ............................................................................................ 28 1.5 Operational and Financial Risk .......................................................................... 31 1.6 Drivers for research .......................................................................................... 31 2 LITERATURE REVIEW .................................................................................... 32 2.1 Introduction ..................................................................................................... 32 2.1.1 Problem Statement ......................................................................................... 33 2.1.2 Literature Review Structure ............................................................................. 34 2.2 Obsolescence .................................................................................................... 38 2.2.1 Obsolescence Management ............................................................................ 39 2.2.2 Obsolescence Mitigation ................................................................................. 43 2.2.2.1 Monitoring Techniques ............................................................................................ 45 2.2.2.2 Forecasting Techniques ............................................................................................ 49 2.2.3 Chapter Summary ............................................................................................ 54 2.3 Systems Resilience ............................................................................................ 55 2.3.1 Operational Resilience ..................................................................................... 58 2.3.1.1 Maintenance and Spares .........................................................................................