Contributions from:. Dr R. J. Kirkham (Liverpool John Moores University).
Dr A. H. Boussabaine (The University of Liverpool).
Professor Roger Flanagan (The University of Reading).
Dr Mohammed Kishk (Robert Gordon University).
Mr Anthony Waterman (MACE, formerly BRE).
Mr Tim Grindley (Faithful and Gould).
Professor John Kelly (Glasgow Caledonian University).
Mr Antonio Pimenta (Faculty of Engineering-University of Porto).
Professor Antonio Fernandez (Faculty of Engineering-University of Porto).
Mr Matthew Kirkham (Halcrow).
Mr Bob Lewis (Taylor Woodrow).
Dr Muthena Alisa (Halcrow).
Mr. Jakob Brondsted (Implement, Denmark).
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Part I.
Theory and methodology in application.
1. This aspect of the text will consider method and methodological problems associated with WLCC, with a strong emphasis upon handling complexity and risk/uncertainty quantification. The former will be considered in the context of the "age old" paradox of WLCC - that it is most effective at the early stage but plagued by the lack of cost and performance data. The content here will be reinforced by early work being conducted in CIB TG-62 Complex Systems and the Built Environment, which is co-ordinated by Dr Boussabaine and Dr Kirkham. The remit of the task group involves consideration of "measuring complexity" such as Deterministic, statistical and inferential complexities. The text will explore the appropriateness of latent complexity tools with WLCC such as dynamic complexity.
2. Hunter and Kelly will produce a commentary on the practical application of methodology with reference to a SCQS project, specifically examining:.
i. An Introduction to an Industry Funded Research Project (SCQS).
ii. Definitions of Whole Life Costing.
iii. Compilation of the OBC and FBC and the relationship to the application points of Value Management.
iv. The Research Process (will include the research methodology and the four stages of work).
v. The Project Outputs (Framework Document & Whole Life Costing Input Tool).
vi. The Whole Life Costing Input Tool (will include screenshots of the tool).
vii. Feedback and Updates to the Tool (an explanation on how the tool has developed and satisfied increasing expectations).
viii. Challenges (i.e. programming, timescale, public sector target market).
ix. Future Developments.
Part II.
Computational and mathematical aspects of WLCC.
The industry perception of WLCC is that of a "politically driven albatross hung around the necks of practitioners at all levels". Reliability of the forecasts and any inferences drawn lies at that heart of the WLCC paradox, this is most critical in the case of maintenance cost and performance prediction; present methodologies applied in WLCC are correctly perceived as weak and lacking in sufficient rigour to be relied upon. This is crucial in built asset acquisition, where investment decisions involve substantial capital commitment. For WLCC to be successfully adopted, practitioners need to be persuaded to adopt it at an early stage, rather than retrospectively - a tick box procedure, as it were, done purely to satisfy procurement regulations and nothing else. The mathematics underpinning the methodology described in this chapter will be reported on within the complexity context, examining the translation of these metrics into an environment accessible to non-expert engineers has not been successfully addressed, particularly within a WLCC context.
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Part III.
Economic theory and WLCC.
This part will explore some of the fundamental issues surrounding Economic theory and WLCC. This is an important section since the basis of WLCC lies in economics, yet it is given a rather superficial treatment in most publications to press. Antonio Pimenta de Silva and Professor Antonio Fernandes will contribute to this chapter from the University of Porto with Anthony Waterman, Head of Economic Modelling at the BRE. Issues to be addressed include:.
x. What is discounting.
1. The principles.
2. Why discount future values?.
xi. Real and nominal discount rates.
1. How do you calculate a discount rate.
2. Time preference of money.
3. Risk.
4. Selecting the discount rate - How do I work out my discount rate.
xii. What about inflation.
1. Should I include inflation.
2. How do I include inflation.
xiii. The (cost) significance of discounting in WLC models.
xiv. Uplifting historic prices.
xv. Adjusting prices based on location.
xvi. Financial indicators.
1. Some useful financial indicators.
2. Simple and discounted payback periods.
3. Net Savings.
4. Savings to Investment Ratio.
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Part IV.
Strategic whole life cycle cost and risk management
There will be a heavy emphasis in this section of the text on WLCC and risk, particularly in the sense of the development of agent-based methods for whole life cycle risk assessment based on a "cradle to grave" paradigm Work by Akintoye (2001, 2003) has already identified the importance of risk management practices within PPP/PFI projects and this is crystallised in other research sponsored by EPSRC such as GR/S18373/01 SUE: Water Cycle Management for New Developments: WaND and GR/R98617/01 A Whole Life costing Approach to Sewerage focusing upon whole life cycle costing methodologies. It is important to note the distinction however between whole life costing and whole life risk, the latter encompassing a holistic approach to understanding and modelling cost and non-cost risk events within a project scenario. Whole life risks must be quantified, analysed and presented as part of the strategic decision-making process in today's business environment (Kirkham and Boussabaine 2000). Risk assessment across the whole life cycle is a major factor in decision-making in project procurement and value engineering in today's business environment. Building asset costs, design and operational decision-parameters are often established very early in the life of a project. Often these parameters are chosen based on the owner's and the project team's personal experiences or on an ad hoc, static, economic analysis of the anticipated asset costs/revenues. While these approaches are common, they do not provide a robust framework for dealing with the risks and decisions that are taken in the evaluation process. Nor do they allow for a systematic evaluation of all the parameters that are considered important in the examination of whole life aspects of an asset. Whole life cycle decisions are complex, and there are thus, many factors affecting the ultimate decisions. Whole life cycle decisions generally have multiple objectives and alternatives, long term impacts, multiple stakeholders in the procurement of construction projects, involve multiple disciplines and multiple decisions makers, and always involve various degrees of risk and uncertainty.
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Most stakeholders recognise the benefits of making decisions on the basis of risk assessment measures. Risk analysis procedures have been used to gauge the economic and technical risk complexity that are inherent in many aspects of entire development process cycle (Boussabaine and Kirkham 2004). In whole life risk there will be a myriad of variables influencing the risk, but the impacts of which are not explicitly included in the mathematical risk model. Risk-analysis procedures have been available for many years. The most common risk analysis procedures are sensitivity analyses and Monte Carlo methods. While these techniques do provide a basis for making risk assessment, they often do not account for many of the parameters that may affect the actual asset value. These methods are based on top-down approaches and are unable to take into account the low-level interactions between risk variables that lead to losses. Mechanisms underlying risk may exist at lower level in the hierarchy (Johnson 2005). Since these low-level interactions are excluded from the analysis, the current methods used to model whole life risk may lead to unsatisfactory results. Another problem with existing risk assessment procedures is that those methods that are simple enough for use by risk analysts are too simplistic to capture the subtlety of risk situations. Those that are complex enough to capture the essence and subtlety of whole life risk situations are so complex that they require experts to use them. Several authors have suggested approaches that attempt to make the implicit variables more explicit. These approaches include hierarchical representations, the definitions of local and global random variables, and the aggregation of variables. Even with all of these contributions, standard risk-analysis procedures have no straightforward way of including the impacts of explicit correlations or the implicit variables in the risk formulation. It is important to be able to model circumstances in which the causal influences between variables are the feature, or, at least, one of the features, of importance. To address this problem, one needs risk analysis procedures that are able to model risk complexity, but that hide their inherent computational complexity from the user. New analysis tools are emerging that have the potential to allow complex risk analysis to be performed simply. By utilizing agent-based complex system models, this shortcoming may be eliminated and may lead to powerful, yet simple, approaches to the representation of risky problems.
