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Modelling global risk factors affecting construction cost performance
International Journal of Project Management 21 (2003) 261–269 www.elsevier.com/locate/ijproman
Modelling global risk factors affecting construction cost performance Daniel Baloia,*, Andrew D.F. Priceb a
Universidade Eduardo Mondlane, Campus Universitario—GIU, PO Box 257, Maputo, Mozambique Department of Civil and Building Engineering, Loughborough University, Leicestershire LE11 3TU, UK
b
Received 19 September 2001; received in revised form 28 November 2001; accepted 12 February 2002
Abstract This paper discusses the core issues of global risk factors modelling, assessment and management. The research reported upon forms part of a larger study that aims to develop a fuzzy decision framework for contractors to handle global risk factors affecting construction cost performance at a project level. Major global risk factors affecting cost performance were identified through an extensive literature review and preliminary discussions with construction contractors. The main decision perspectives namely normative and behavioural were explored. Different decision-making technologies, both classical and emergent, such as classical management science techniques and DSSs, KBSs were explored and evaluated. Preliminary indications show that Fuzzy Set Theory is a viable technology for modelling, assessing and managing global risk factors affecting construction cost performance and thus a fuzzy decision framework for risk management can be successfully developed. # 2003 Elsevier Science Ltd and IPMA. All rights reserved. Keywords: Cost performance; Decision Support System; Fuzzy Set Theory; Risk management; Uncertainty
1. Introduction This paper discusses the core issues of risk modelling, assessment and management with emphasis on global risk factors that affect cost performance. The research reported upon forms part of a larger study that aims to develop a fuzzy decision framework for contractors to handle global risk factors affecting construction cost performance. Major risk factors affecting cost performance were identified through an extensive literature review complemented by a workshop with construction contractors in Mozambique. A questionnaire survey will be conducted at a later date to confirm the factors identified earlier and to determine the most critical. The findings of the questionnaire will form the basis for structured interviews using the Repertory Grid technique to elicit relevant knowledge so as to develop a knowledge based decision support system. Poor cost performance of construction projects has been a major concern for both contractors and clients. Despite the large number of reported cases, it seems that * Corresponding author. Tel.: +258-1-490088; fax: +258-1-491025. E-mail addresses: [email protected] (D. Baloi), a.d.f.price@ lboro.ac.uk (A.D.F. Price).
construction ranging from the simplest to more complex projects such as nuclear plants, environmental restoration, transport systems and oil and gas platforms have increasingly faced cost overruns. Raftery [1] pointed out that construction projects tend to have poor reputation for excessive time and cost overruns. Morris and Hough [2], during a study of records for different types of projects funded by the World Bank between 1974 and 1988, found that 63% out of 1778 projects had experienced significant cost overruns. Kaming et al. [3] studied factors influencing time and cost performance on high-rise projects in Indonesia and concluded that cost and time overruns were very frequent. Therefore, poor cost performance of construction projects seems to be the norm rather than the exception particularly in most developing countries where the problem is more acute. Various factors significantly influence construction costs from the estimating stage to project completion. Some factors are intrinsically related to construction organisations which are solely responsible for managing them, whereas others are closely related to the socio-cultural, economic, technological and political environments within which such organisations operate. The latter are usually called global risk factors. As a principle, contractors are not normally responsible for risk factors
0263-7863/03/$30.00# 2003 Elsevier Science Ltd and IPMA. All rights reserved. doi:10.1016/S0263-7863(02)00017-0
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outside their control and traditional forms of contracts should provide a fair and sensible allocation of risks between the various parties. However, contractors in developing countries often have to bear most of the construction risks including those for which they have little control. Unfortunately, many contractors are unfamiliar with these risk factors and do not have the experience and knowledge to manage them effectively. As a consequence, conflicts, poor quality, late completion, poor cost performance and business failures are commonplace in the construction industry. Risk management is nowadays a critical factor to successful project management, as projects tend to be more complex and competition increasingly tougher. There is a direct relationship between effective risk management and project success since risks are assessed by their potential effect on the objectives of the project. Contractors have traditionally used high mark-ups to cover risk but as their margins have become smaller this approach is no longer effective. In addition, the construction industry has witnessed significant changes particularly in procurement methods with clients allocating greater risks to contractors. Evidence shows that there is a gap between the existing risk management techniques and their practical application by construction contractors [4–6]. Many reasons have been put forward to explain why this is the case but it seems that assessment and analysis are the most controversial issues. There is thus a need to explore new directions in risk management so as to respond to the expectations of construction industry towards an effective and efficient modelling, assessment and management of risk.
3. Definition of risk Risk has different meanings to different people; that is, the concept of risk varies according to viewpoint, attitudes and experience. Engineers, designers and contractors view risk from the technological perspective; lenders and developers tend to view it from the economic and financial side; health professionals, environmentalists, chemical engineers take a safety and environmental perspective. Risk is therefore generally seen as an abstract concept whose measurement is very difficult [1]. The Oxford Advanced Learner’s Dictionary—1995 ed. defines risk as the: ‘‘chance of failure or the possibility of meeting danger or of suffering harm or loss’’. In construction projects, risk may be defined as the likelihood of a detrimental event occurring to the project. Since the objectives of construction projects are usually stated as targets established for function, cost, time and quality, the most important risks in construction are the failure to meet these targets. However, risks are not always associated with negative outcomes. Risks may represent opportunities as well, but the fact that most risks usually have negative outcomes has led individuals to consider only their negative side.
4. Risk management Risk management is a process comprising the following main steps: risk management planning, risk identification, risk assessment, risk analysis, risk response, risk monitoring and risk communication [1,6].
5. Taxonomy of risks 2. Objectives The overall aim of the research discussed in this paper is to develop a fuzzy decision framework for a systematic modelling, analysis and management of global risk factors affecting construction cost performance from contractor’s perspective and at a project level. The study is concerned with financial risk rather than hazard. In order to achieve the overall objective the research will: review literature on risk modelling, analysis and management; review management science theories and techniques; determine the most critical risk factors affecting construction cost performance; elicit relevant knowledge related to the most critical factors; and formalise such knowledge, develop, test-evaluate a fuzzy decision framework for handling global risk factors.
Many different classifications of risk have been developed over the years, however, most of these have considered the source criteria as the most important. Following this criteria, a broad classification of construction project risks could be: technical, construction, legal, natural, logistic, social, economic, financial, commercial and political [5,6]. However, apart from the source criteria, there have been other forms of classifying risks, which take different perspectives. A classification taking into account the location of the impact of risks in the elements of the project was suggested by Tah [7]. It is also usual to categorise risks into dynamic/static, corporate/individual, internal/external, positive/negative, acceptable/unacceptable and insurable/non-insurable.
6. Developing countries As stated by Ofori [8], the ‘‘structural problems of construction industry in developing countries are more
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fundamental, more serious, more complex, and, overall, much more pressing than those confronting their counterparts elsewhere’’. Common problems affecting construction industry in developing countries include lack of management skills, shortage of skilled labour, low productivity, shortage of supplies, bad quality of supplies and lack of equipment. Apart from technical issues, management-related problems are one of the most important aspects facing construction contractors since they have to deal with substantial constraints such as incomplete information, unpredictable client behaviour, and uncertain project circumstances.
7. Research into risk factors affecting cost performance Research into the poor cost performance of construction projects has pointed out several variables as risk drivers. The cost performance of a single project is often in terms of cost growth, i.e. the percentage difference between the final contract amount and the contract award amount [9]. The final contract amount includes all additions, alterations and deductions resulting from project changes. More often than not, variations and claims are inherent in construction projects because uncertainties lead, invariably, to the need for adjustments. However, variations and claims have been the main reasons for disputes due to both conflicting interests of the parties and the complexity of contractual provisions when dealing with the valuation of variations and settlement of claims. Conflicting interests that lead to adversarial relationships between clients and contractors have their economic roots namely within clients’ costs and contractors’ profits. Many studies on poor cost performance of construction projects have focused on change-order rate, which can be defined as the ratio between the amount of changeorders and the contract award amount. Factors found to influence change-order rate include level of competition, project size and type. Okpala and Aniekwu [10] argued that delay and cost overruns could be reduced through the increase of human efficiency. In addition, they identified price fluctuations, fraudulent practices and ‘kickbacks’ as the major factors of poor cost performance in construction projects in Nigeria. Further, research by Jahren and Ashe, Elinwa and Buba, [11, 12] found similar variables as the most influencing factors of project cost overruns. Risk factors associated with political instability, fluctuations in currency, corruption, interest rates and material availability were considered the main causes of additional costs in privatised infrastructure projects in developing countries [13]. Kangari and Lucas [14] mentioned that all government-funded projects in developing countries were political in nature. Political risk, in turn, invariably leads to bribery and corruption. Ashley and Bonner [15] developed a model for political risk assessment in international construc-
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tion, which focused on the costs and revenue variables. Cost-related variables included labour, material and overheads whereas revenue-related variables included taxation, repatriation restrictions and foreign exchange rates. Kaming [3] studied factors influencing construction time and cost overruns on high-rise projects in Indonesia and concluded that inflation, inaccurate estimates, project complexity, weather conditions, project location and local regulation were the main contributors. The factors affecting cost performance of construction projects seem to be well appreciated in the literature, however, there are some gaps. Firstly, most of the current techniques and tools are founded on statistical decision theory models. Contractors rarely use these techniques and tools in practice. In this regard Mulholland and Christian [16] asserted that there was a lack of an accepted method of risk assessment and management in the construction industry. More often than not construction contractors and other practitioners rely on assumptions, rules of thumb, experience and intuitive judgement which can not be fully described by prescriptive or normative models. Individual knowledge and experience, however, need to be accumulated and structured to facilitate the analysis and retrieval by others. Secondly, considerable research into risk management has mainly focused on the easily quantifiable variables and has neglected other important factors. There is thus a need to combine both prescriptive and behavioural models in the modelling, assessment and management of risks since construction contractors do not only rely on prescriptive models to make decisions but they also make an extensive use of experience, rules of thumb and intuitive judgement. It is also suggested that global risk factors pose more challenges to contractors than others categories of risks particularly in developing countries. The recommendations and models produced so far need to be complemented in order to account for the complexity and dynamism surrounding construction projects. Although some sources of uncertainties are very difficulty to assess, analyse and to communicate, reliance on arbitrary assumptions may be misleading and controversial. Furthermore, overlooking or ignoring risks does not make them go away.
8. Global risk factors Construction organisations operate within an environment and not a vacuum. They are inevitably influenced by and constantly interacting with their environment. As such, construction organisations and, consequently construction projects, are open systems rather than closed systems [17]. Closed systems are rigid and do not adapt to changing environments. An example of a closed system is computer that is designed to perform specific and repetitive tasks within specific conditions. Construction
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organisations’ efficiency and effectiveness largely depend upon how managers scan the external project environment, identify the critical factors and accordingly adapt their organisations. The project environment can be subdivided as follows: inner layer or internal environment; operational environment; and outer layer or general environment. Both general and operational environments are external environments. The general environment is broad in scope and comprises five basic domains, namely technological, social, physical, economic and political [17]. Risk factors affecting cost performance are classified in this study as Organisation-specific, Global and Acts of God. These risk categories can be broken down to achieve a more detailed and comprehensive picture. Organisation-specific risks are internal risks related to an organisation’s resources and management. Organisation-specific risks include factors that are considered to be under contractor’s control, for example risks related to labour skills and availability, materials delivery and quality, equipment reliability and availability, and management efficiency [18]. In principle, contractors are solely responsible for managing these risks.
Global risk, on the other hand, refer to risks factors that are not directly present in cost estimates yet they may lead to significant financial disasters. Global risks are called so because they transcend the boundaries of the organisation yet they have large impact on it. Contractors have less control over these risk factors and contracts should provide fair and sensible allocation of these risks between the parties. However, in practice, contractors in developing countries have to bear most of construction risks. Acts of God represent risks with extremely low probability of occurrence, but can have huge negative impacts on projects if they occur. This category of risk come under force majeure under the terms of contract and includes events such as heavy floods, landslide, earthquake and hurricanes. These risks are excusable and generally insurable but non-compensatable risks. The unique and main groups of global risks factors, identified from the extensive literature search and preliminary discussions with construction contractors, include economic, political, design, level of competition, fraudulent practices and construction-specific risk. Several authors, such as Jahren and Ashe, Ashley and Bonner, and Akinci and Fisher [11,15,19] have highlighted the importance of these risk factors, which are shown in Fig. 1.
Fig. 1. Main groups of global risk factors.
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9. Current practices in risk management Risk management is beneficial if implemented in a systematic manner from the planning stage through project completion so as to make better and more informed decisions. The unsystematic and arbitrary management of risks can endanger the success of the project since most risks are very dynamic throughout the project lifetime. Indeed, risks may vary from appraisal, design, tendering, construction and commissioning. To meet these demands, human and organisational dimensions play a key role in the whole process of risk management. In fact, risk management is both an art and a science. However sophisticated the analytical tools are, they are only a complement to the process. Despite the large body of knowledge and continuous developments of the risk management discipline, it seems that practitioners have not fully appreciated its importance. Apart from the high-risk sectors such as oil exploration and petrochemical, there is a significant gap between the existing theory and practice of risk management in the construction industry [4–6].
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data, explanation capacity, difficulty at development and appropriate domain.
11. Handling uncertainty The main objective of risk management is to reduce uncertainty and thus improve decision-making. Uncertainty has many different sources and different types, [21]. The main types of uncertainty include error, imprecision, variability, vagueness, ambiguity and ignorance. The diversity in terms for different types of uncertainty makes the modelling process a very difficult task because the information concerning each specific uncertainty is scarce. Several formal techniques for managing the different types of uncertainty have been developed but there has not been any consensus on the appropriateness of such techniques so far. It seems that there is no best theory of uncertainty and therefore the choice of the most appropriate technique depends upon the specific problem [22]. Four main approaches have been used for handling uncertainty in KB-DSS, namely Probability Theory, Certainty Factor Theory, Dempster–Shafer Theory and Fuzzy Set Theory.
10. Risk decision-making and uncertainty Decision-making problems are broadly categorised into deterministic, stochastic/risk and uncertain. Deterministic problems are those in which data are known with certainty; whereas stochastic problems are those in which data are not known with certainty but can be represented by a probability distribution; and uncertain refer to those problems in which data are not known. Most risk management problems fall into the last two categories because they are poorly structured problems for which few algorithms or mechanical methods exist. Indeed, risk management relies heavily on experience, subjectivity and human judgement. Poorly structured problems can not be formulated at the desired level of precision due to the surrounding uncertainty. However, recent developments in information technologies have made it possible to improve the formulation of poorly structured problems so as to aid decision making. In this regard much research effort has put on the development of decision support systems (DSS) and, particularly, knowledge based systems (KBS). According to Sprague and Watson [20] a DSS has the following main features: computer-based system helps decision-makers; confronts poorly structured problems; and has direct interaction. There are several DSS development techniques for applications in the construction industry and they include: rule-based expert systems; case-based expert systems; model-based expert systems; neural network based systems; and genetic algorithm based systems. The choice of an appropriate technique depends mainly upon the difficulty at knowledge acquisition, required
12. Probability Theory The classical approach to address uncertainty is the Bayesian theory of probability. Probability Theory has been used to model precisely described, repetitive experiments with observable but uncertain outcomes. The basic assumption in the classical theory of probability is that all uncertainties are measures of randomness or subjective measures of confidence. The main concern of random processes is the statistics. The heavy reliance on the Probability Theory as the only effective and reliable methodology to deal with uncertainty has historical roots. Probability Theory has well-established and sound scientific foundations and has been widely used for centuries. Probabilitics argue that random methods are the only effective methods for dealing with uncertainty. Deterministic approaches are commonplace in ‘hard’ sciences and the pursuit for precision in such areas is more favoured than qualitative models.
13. Certainty Theory Certainty Theory was formulated by Buchanan and Shortliffe [23] to handle uncertainty in the medical expert system (an expert system which diagnoses microbial infections) MYCIN. It was developed in attempt to overcome some of the weaknesses of Probability Theory. Certainty Theory fundamentals are the concepts of ‘‘certainty measures’’ which are associated with ‘‘factual
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statements’’. Certainty measures comprise numbers ranging from 1 to +1 where 1 represents complete certainty that a proposition is false and +1 represents complete certainty that a proposition is true. The factual statements are rules comprising antecedents and consequents.
14. Dempster–Shafer Theory of Evidence Dempster–Shafer Theory of Evidence [24] is usually called epistemic probability because it provides an alternative model for the assessment of numerical degrees of belief. Dempster–Shafer attempted to distinguish between uncertainty and ignorance and instead of probabilities, belief functions are used. The main distinctions between Bayesian models of numerical degrees and Dempster–Shafer model are the following: belief functions of Dempster–Shafer are set functions rather than point values; rejection of the law of additivity for belief in disjoint propositions; and Dempster–Shafer Theory has an operation for the pooling of evidence from various sources.
15. Fuzzy Set Theory Fuzzy Set Theory is a branch of modern mathematics that was formulated by Zadeh [25] to model vagueness intrinsic to human cognitive processes. Since then, it has been used to tackle ill-defined and complex problems due to incomplete and imprecise information that characterise the real-world systems. It is, therefore, suitable for uncertain or approximate reasoning that involve human intuitive thinking. Fuzzy Set Theory uses linguistic variables and membership functions with varying grades to model uncertainty inherent in natural language.
16. Appropriateness of the approaches Probability Theory considers all uncertainty random, however, not all types of uncertainty are random. A great deal of management issues in construction does not comply with randomness properties. They are mainly cognitive and thus do not lend themselves to precise measurement. Several authors have highlighted some serious shortcomings related to the Bayesian statistics. Zadeh [25] suggested that the assumptions associated with Bayesian function namely, mutual exclusivity of events, conditional independence and exhaustivity of events do not always hold. In addition, there is no distinction between randomness and ignorance. Probabilistic approaches are best suited to mechanistic systems
where the accuracy and precision are considered important. Certainty Factor Theory provides a simple method for handling uncertainty but it is said as lacking an adequate theoretical underpinning [26]. The Dempster– Shafer Theory is richer in terms of semantics since it allows an expression of partial knowledge. Its main shortcoming is the elicitation and interpretation of belief functions, [21]. Fuzzy Set Theory has proved to be a powerful technology in modelling unstructured problems particularly in the electronic industry. In recent years there has been a significant increase of its application in the construction industry, particularly for modelling uncertainty in KBSs.
17. Uncertainty and risk management in construction It has already been mentioned that construction risk management is mainly based upon experience, assumptions and human judgement. Consequently, risk management is mainly cognitive in nature. Kahneman and Tversky [27] uncovered important aspects of cognitive and non-cognitive types of uncertainty. Klir and Foger [28] discussed two types of uncertainty namely ambiguity and vagueness. Ambiguity is generally due to a non-cognitive root causes. Ambiguity is a state in which an expression or word may have a number of distinct meanings and only the context may help clarifying the real meaning. Evidence theory has proved to be the most suitable mathematical framework to handle ambiguity type of uncertainty. Vagueness arises when the outcome of an experiment can not be properly observed. A vague expression is ill defined and lacks preciseness or sharpness. For example, the global risk factor ‘‘Strong competition’’ does not have an exact meaning, because the qualifier ‘‘strong’’ may assume several degrees of intensity. ‘‘Strong competition’’ may involve a wide spectrum of human perceptions, as there are no rigorous definition of what ‘‘Strong competition’’ is. A particular type of vagueness is fuzziness. Fuzziness is a kind of imprecision where the transition from a membership state of an element to a set is gradual. It is, nevertheless, most frequent in situations where human judgement is an essential feature such as reasoning, learning and decision-making process.
18. Fuzzy Set Theory Fuzzy Set Theory has been used to tackle ill-defined and complex problems due to incomplete and imprecise information that characterise the real-world systems. Zadeh stated that ‘‘as the complexity of a system increases, human ability to make precise yet significant
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statements about its behaviour diminishes until a threshold is reached beyond which precision and significance become mutually exclusive’’—the Principle of Incompatibility. Then, it follows that modelling complex or ill-defined systems can not be made precisely. Fuzzy Set Theory is not intended to replace Probability Theory but rather to provide solutions to problems that lack mathematical rigour inherent to Probability Theory. Essentially, Fuzzy Set Theory is an extension of the classical Boolean or binary logic. In Classical Set theory, a set is a collection of objects having a general property, for example, a set of clients. In Classical Logic, an element is, therefore, either or not a member of a set. The boundaries of concepts are very rigid or ‘crisp’ and there is no room for grey or ‘in between’ states. There are no intermediate grades of membership between full and non-membership. This deterministic ‘yes-or-no’ or ‘dichotomous’ approach is nowadays widespread practice in systems modelling, reasoning process and computing. The main problem with binary approach is that it fails to convey information effectively, that is, the states between full and non-membership are ignored yet they are very important. Meanwhile, most real-world systems are very complex and ill defined to be well understood and modelled precisely. The essence of fuzziness, in contrast to binary or dual logic, is that the transition from a membership to nonmembership state of an element of a set is gradual rather than abrupt. Thus, Fuzzy Set Theory allows a generalisation of the classical set concept to model complex or ill-defined systems. The main concepts associated with Fuzzy Set Theory, as applied to decision systems are membership functions, linguistic variable, natural language computation, linguistic approximation, fuzzy set arithmetic operations, set operations and fuzzy weighted average. Details concerning these topics can be found in [25,29].
19. Linguistic variable Research in cognitive psychology suggests that individuals base their thinking on conceptual patterns and mental images rather than on any quantity or numbers. Although natural language is imprecise it conveys valuable information and despite the vagueness inherent in natural language humans can understand each other perfectly. The concept of linguistic variables lies at the core of Fuzzy Set Theory, since the basics of Fuzzy Set Theory is the manipulation of linguistic expressions instead of numbers. The values assumed by linguistic variables are words. A linguistic variable differs from a numerical variable in that its values are not numbers but words or sentences in a natural or artificial language. Since words in general are less precise than numbers, the concept of linguistic variables serves the
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purpose of providing a means of approximate characterisation of phenomena that are too complex or too ill defined to be amenable to description in conventional quantitative terms. Any risk factor presented in Fig. 1 can be characterised using linguistic variables through its likelihood of occurrence and its severity or impact. Examples of linguistic variables are expressions such as, ‘‘need for job’’, ‘‘number of bidders’’, and ‘‘market conditions’’. These linguistic variables may assume different values such as ‘‘very high’’, ‘‘high’’, ‘‘moderate’’, ‘‘low’’ and ‘‘very low’’, which are fuzzy sets (membership functions) and represent the perception of the decision-maker on the magnitude of any risk factor.
20. Membership functions A fuzzy set is a set whose elements have varying degrees of membership. The degrees of membership of an element are expressed by a membership function. Membership functions in Fuzzy Set Theory play a similar role to that of probability distribution functions in Probability Theory, that is, membership functions are used to represent uncertainty. A membership function is a function that maps a universe of objects, X, onto the unit interval [0, 1]. The universe of objects represents the elements of the set and the interval corresponds to the set of grades. The grades of membership in fuzzy sets may fall anywhere in the interval [0, 1]. A degree of 0 (zero) means that an element is not a member of the set at all. A degree of 1 (one) represents full membership. In contrast with ‘crisp’ sets that have only one membership function, fuzzy sets have a large number of membership functions.
21. Modelling global risk factors using Fuzzy Set Theory Global risk factors that affect construction cost performance can be modelled using Fuzzy Set Theory and a fuzzy decision support system can be developed. For that purpose it is necessary: to determine the most significant risk factors; define the different linguistic variables, which correspond to the constructs used by construction professionals to describe risk factors; define the membership functions for each linguistic variable and build a knowledge base. Significant risk factors, linguistic variables, membership functions and knowledge base can be determined through literature search, questionnaires and interviews with construction industry practitioners. The knowledge base is a repository of experts’ knowledge and experience. The knowledge in the knowledge base can be represented using ‘‘production rules’’, which is a knowledge representation system that seems to capture well risk knowledge, because a great deal of knowledge and heuristics used
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by construction contractors in risk decision-making is in the form of rules (for example, IF subcontractor A is very experienced, THEN performance is good; IF inflation is medium and exchange rate is medium THEN economic risk is medium). The main advantage of production rules are that they are easy to understand, provide modularity in design, which enables addition, deletion and change of rules independently. In addition to the knowledge base, databases and analytical models can be incorporated in the decision support system in order to enhance its performance. The inputs to the decision support system are the assessments of the different global risk factors specific to a project in linguistic terms (high, medium, low). The system checks the knowledge base and databases and performs natural language computations and produces the risk impact for each group of risk factors as well as the overall risk (combination of partial risk impacts) and the corresponding likelihood in linguistic terms. For example the overall risk impact can be ‘‘low’’ with ‘‘high’’ likelihood. The system can also provide recommendations on the most appropriate risk response strategies in the light of risk analysis results. The decision-maker can then make her/his judgement and take appropriate measures to mitigate project risks and thus improve the likelihood of project success.
22. Conclusions This paper discussed the core issues of global risk factors modelling, assessment and management. Evidence shows that poor cost performance of construction projects seems to be the norm rather than the exception, and both clients and contractors suffer significant financial losses due to cost overruns. It is suggested that global risk factors pose more challenges to construction contractors, which are less familiar with them. In addition, they lack effective techniques and tools to handle these risks. Risk management has traditionally been largely based on experience and subjective judgement; that is, it features humanistic systems that are not characterised by precision. Unlike mechanistic systems, humanistic systems have proved impervious to mathematical analysis and computer simulation. A humanistic system is a system that is heavily dependent on human judgement and perceptions. As such, there is a need to explore new directions towards risk modelling, assessment and management. Different techniques for handling uncertainty were evaluated and preliminary results indicate that global risk factors affecting construction cost performance can be successfully modelled, assessed and managed using Fuzzy Set Theory and Decision Support System technologies. For that purpose it is necessary to determine significant global risk factors, define linguistic variables,
devise reliable membership functions, and build the knowledge base, as important ingredients of a fuzzy decision support system. Both the inputs and outputs of the system are qualitative and in natural language, which facilitates its use and understanding. The decision framework would be an important tool for contractors to increase their awareness, identify global risk factors affecting cost performance, assess their impact and likelihood and take appropriate measures in order to reduce their impact on cost performance.
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Modelling global risk factors affecting construction cost performance Daniel Baloia,*, Andrew D.F. Priceb a
Universidade Eduardo Mondlane, Campus Universitario—GIU, PO Box 257, Maputo, Mozambique Department of Civil and Building Engineering, Loughborough University, Leicestershire LE11 3TU, UK
b
Received 19 September 2001; received in revised form 28 November 2001; accepted 12 February 2002
Abstract This paper discusses the core issues of global risk factors modelling, assessment and management. The research reported upon forms part of a larger study that aims to develop a fuzzy decision framework for contractors to handle global risk factors affecting construction cost performance at a project level. Major global risk factors affecting cost performance were identified through an extensive literature review and preliminary discussions with construction contractors. The main decision perspectives namely normative and behavioural were explored. Different decision-making technologies, both classical and emergent, such as classical management science techniques and DSSs, KBSs were explored and evaluated. Preliminary indications show that Fuzzy Set Theory is a viable technology for modelling, assessing and managing global risk factors affecting construction cost performance and thus a fuzzy decision framework for risk management can be successfully developed. # 2003 Elsevier Science Ltd and IPMA. All rights reserved. Keywords: Cost performance; Decision Support System; Fuzzy Set Theory; Risk management; Uncertainty
1. Introduction This paper discusses the core issues of risk modelling, assessment and management with emphasis on global risk factors that affect cost performance. The research reported upon forms part of a larger study that aims to develop a fuzzy decision framework for contractors to handle global risk factors affecting construction cost performance. Major risk factors affecting cost performance were identified through an extensive literature review complemented by a workshop with construction contractors in Mozambique. A questionnaire survey will be conducted at a later date to confirm the factors identified earlier and to determine the most critical. The findings of the questionnaire will form the basis for structured interviews using the Repertory Grid technique to elicit relevant knowledge so as to develop a knowledge based decision support system. Poor cost performance of construction projects has been a major concern for both contractors and clients. Despite the large number of reported cases, it seems that * Corresponding author. Tel.: +258-1-490088; fax: +258-1-491025. E-mail addresses: [email protected] (D. Baloi), a.d.f.price@ lboro.ac.uk (A.D.F. Price).
construction ranging from the simplest to more complex projects such as nuclear plants, environmental restoration, transport systems and oil and gas platforms have increasingly faced cost overruns. Raftery [1] pointed out that construction projects tend to have poor reputation for excessive time and cost overruns. Morris and Hough [2], during a study of records for different types of projects funded by the World Bank between 1974 and 1988, found that 63% out of 1778 projects had experienced significant cost overruns. Kaming et al. [3] studied factors influencing time and cost performance on high-rise projects in Indonesia and concluded that cost and time overruns were very frequent. Therefore, poor cost performance of construction projects seems to be the norm rather than the exception particularly in most developing countries where the problem is more acute. Various factors significantly influence construction costs from the estimating stage to project completion. Some factors are intrinsically related to construction organisations which are solely responsible for managing them, whereas others are closely related to the socio-cultural, economic, technological and political environments within which such organisations operate. The latter are usually called global risk factors. As a principle, contractors are not normally responsible for risk factors
0263-7863/03/$30.00# 2003 Elsevier Science Ltd and IPMA. All rights reserved. doi:10.1016/S0263-7863(02)00017-0
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outside their control and traditional forms of contracts should provide a fair and sensible allocation of risks between the various parties. However, contractors in developing countries often have to bear most of the construction risks including those for which they have little control. Unfortunately, many contractors are unfamiliar with these risk factors and do not have the experience and knowledge to manage them effectively. As a consequence, conflicts, poor quality, late completion, poor cost performance and business failures are commonplace in the construction industry. Risk management is nowadays a critical factor to successful project management, as projects tend to be more complex and competition increasingly tougher. There is a direct relationship between effective risk management and project success since risks are assessed by their potential effect on the objectives of the project. Contractors have traditionally used high mark-ups to cover risk but as their margins have become smaller this approach is no longer effective. In addition, the construction industry has witnessed significant changes particularly in procurement methods with clients allocating greater risks to contractors. Evidence shows that there is a gap between the existing risk management techniques and their practical application by construction contractors [4–6]. Many reasons have been put forward to explain why this is the case but it seems that assessment and analysis are the most controversial issues. There is thus a need to explore new directions in risk management so as to respond to the expectations of construction industry towards an effective and efficient modelling, assessment and management of risk.
3. Definition of risk Risk has different meanings to different people; that is, the concept of risk varies according to viewpoint, attitudes and experience. Engineers, designers and contractors view risk from the technological perspective; lenders and developers tend to view it from the economic and financial side; health professionals, environmentalists, chemical engineers take a safety and environmental perspective. Risk is therefore generally seen as an abstract concept whose measurement is very difficult [1]. The Oxford Advanced Learner’s Dictionary—1995 ed. defines risk as the: ‘‘chance of failure or the possibility of meeting danger or of suffering harm or loss’’. In construction projects, risk may be defined as the likelihood of a detrimental event occurring to the project. Since the objectives of construction projects are usually stated as targets established for function, cost, time and quality, the most important risks in construction are the failure to meet these targets. However, risks are not always associated with negative outcomes. Risks may represent opportunities as well, but the fact that most risks usually have negative outcomes has led individuals to consider only their negative side.
4. Risk management Risk management is a process comprising the following main steps: risk management planning, risk identification, risk assessment, risk analysis, risk response, risk monitoring and risk communication [1,6].
5. Taxonomy of risks 2. Objectives The overall aim of the research discussed in this paper is to develop a fuzzy decision framework for a systematic modelling, analysis and management of global risk factors affecting construction cost performance from contractor’s perspective and at a project level. The study is concerned with financial risk rather than hazard. In order to achieve the overall objective the research will: review literature on risk modelling, analysis and management; review management science theories and techniques; determine the most critical risk factors affecting construction cost performance; elicit relevant knowledge related to the most critical factors; and formalise such knowledge, develop, test-evaluate a fuzzy decision framework for handling global risk factors.
Many different classifications of risk have been developed over the years, however, most of these have considered the source criteria as the most important. Following this criteria, a broad classification of construction project risks could be: technical, construction, legal, natural, logistic, social, economic, financial, commercial and political [5,6]. However, apart from the source criteria, there have been other forms of classifying risks, which take different perspectives. A classification taking into account the location of the impact of risks in the elements of the project was suggested by Tah [7]. It is also usual to categorise risks into dynamic/static, corporate/individual, internal/external, positive/negative, acceptable/unacceptable and insurable/non-insurable.
6. Developing countries As stated by Ofori [8], the ‘‘structural problems of construction industry in developing countries are more
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fundamental, more serious, more complex, and, overall, much more pressing than those confronting their counterparts elsewhere’’. Common problems affecting construction industry in developing countries include lack of management skills, shortage of skilled labour, low productivity, shortage of supplies, bad quality of supplies and lack of equipment. Apart from technical issues, management-related problems are one of the most important aspects facing construction contractors since they have to deal with substantial constraints such as incomplete information, unpredictable client behaviour, and uncertain project circumstances.
7. Research into risk factors affecting cost performance Research into the poor cost performance of construction projects has pointed out several variables as risk drivers. The cost performance of a single project is often in terms of cost growth, i.e. the percentage difference between the final contract amount and the contract award amount [9]. The final contract amount includes all additions, alterations and deductions resulting from project changes. More often than not, variations and claims are inherent in construction projects because uncertainties lead, invariably, to the need for adjustments. However, variations and claims have been the main reasons for disputes due to both conflicting interests of the parties and the complexity of contractual provisions when dealing with the valuation of variations and settlement of claims. Conflicting interests that lead to adversarial relationships between clients and contractors have their economic roots namely within clients’ costs and contractors’ profits. Many studies on poor cost performance of construction projects have focused on change-order rate, which can be defined as the ratio between the amount of changeorders and the contract award amount. Factors found to influence change-order rate include level of competition, project size and type. Okpala and Aniekwu [10] argued that delay and cost overruns could be reduced through the increase of human efficiency. In addition, they identified price fluctuations, fraudulent practices and ‘kickbacks’ as the major factors of poor cost performance in construction projects in Nigeria. Further, research by Jahren and Ashe, Elinwa and Buba, [11, 12] found similar variables as the most influencing factors of project cost overruns. Risk factors associated with political instability, fluctuations in currency, corruption, interest rates and material availability were considered the main causes of additional costs in privatised infrastructure projects in developing countries [13]. Kangari and Lucas [14] mentioned that all government-funded projects in developing countries were political in nature. Political risk, in turn, invariably leads to bribery and corruption. Ashley and Bonner [15] developed a model for political risk assessment in international construc-
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tion, which focused on the costs and revenue variables. Cost-related variables included labour, material and overheads whereas revenue-related variables included taxation, repatriation restrictions and foreign exchange rates. Kaming [3] studied factors influencing construction time and cost overruns on high-rise projects in Indonesia and concluded that inflation, inaccurate estimates, project complexity, weather conditions, project location and local regulation were the main contributors. The factors affecting cost performance of construction projects seem to be well appreciated in the literature, however, there are some gaps. Firstly, most of the current techniques and tools are founded on statistical decision theory models. Contractors rarely use these techniques and tools in practice. In this regard Mulholland and Christian [16] asserted that there was a lack of an accepted method of risk assessment and management in the construction industry. More often than not construction contractors and other practitioners rely on assumptions, rules of thumb, experience and intuitive judgement which can not be fully described by prescriptive or normative models. Individual knowledge and experience, however, need to be accumulated and structured to facilitate the analysis and retrieval by others. Secondly, considerable research into risk management has mainly focused on the easily quantifiable variables and has neglected other important factors. There is thus a need to combine both prescriptive and behavioural models in the modelling, assessment and management of risks since construction contractors do not only rely on prescriptive models to make decisions but they also make an extensive use of experience, rules of thumb and intuitive judgement. It is also suggested that global risk factors pose more challenges to contractors than others categories of risks particularly in developing countries. The recommendations and models produced so far need to be complemented in order to account for the complexity and dynamism surrounding construction projects. Although some sources of uncertainties are very difficulty to assess, analyse and to communicate, reliance on arbitrary assumptions may be misleading and controversial. Furthermore, overlooking or ignoring risks does not make them go away.
8. Global risk factors Construction organisations operate within an environment and not a vacuum. They are inevitably influenced by and constantly interacting with their environment. As such, construction organisations and, consequently construction projects, are open systems rather than closed systems [17]. Closed systems are rigid and do not adapt to changing environments. An example of a closed system is computer that is designed to perform specific and repetitive tasks within specific conditions. Construction
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organisations’ efficiency and effectiveness largely depend upon how managers scan the external project environment, identify the critical factors and accordingly adapt their organisations. The project environment can be subdivided as follows: inner layer or internal environment; operational environment; and outer layer or general environment. Both general and operational environments are external environments. The general environment is broad in scope and comprises five basic domains, namely technological, social, physical, economic and political [17]. Risk factors affecting cost performance are classified in this study as Organisation-specific, Global and Acts of God. These risk categories can be broken down to achieve a more detailed and comprehensive picture. Organisation-specific risks are internal risks related to an organisation’s resources and management. Organisation-specific risks include factors that are considered to be under contractor’s control, for example risks related to labour skills and availability, materials delivery and quality, equipment reliability and availability, and management efficiency [18]. In principle, contractors are solely responsible for managing these risks.
Global risk, on the other hand, refer to risks factors that are not directly present in cost estimates yet they may lead to significant financial disasters. Global risks are called so because they transcend the boundaries of the organisation yet they have large impact on it. Contractors have less control over these risk factors and contracts should provide fair and sensible allocation of these risks between the parties. However, in practice, contractors in developing countries have to bear most of construction risks. Acts of God represent risks with extremely low probability of occurrence, but can have huge negative impacts on projects if they occur. This category of risk come under force majeure under the terms of contract and includes events such as heavy floods, landslide, earthquake and hurricanes. These risks are excusable and generally insurable but non-compensatable risks. The unique and main groups of global risks factors, identified from the extensive literature search and preliminary discussions with construction contractors, include economic, political, design, level of competition, fraudulent practices and construction-specific risk. Several authors, such as Jahren and Ashe, Ashley and Bonner, and Akinci and Fisher [11,15,19] have highlighted the importance of these risk factors, which are shown in Fig. 1.
Fig. 1. Main groups of global risk factors.
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9. Current practices in risk management Risk management is beneficial if implemented in a systematic manner from the planning stage through project completion so as to make better and more informed decisions. The unsystematic and arbitrary management of risks can endanger the success of the project since most risks are very dynamic throughout the project lifetime. Indeed, risks may vary from appraisal, design, tendering, construction and commissioning. To meet these demands, human and organisational dimensions play a key role in the whole process of risk management. In fact, risk management is both an art and a science. However sophisticated the analytical tools are, they are only a complement to the process. Despite the large body of knowledge and continuous developments of the risk management discipline, it seems that practitioners have not fully appreciated its importance. Apart from the high-risk sectors such as oil exploration and petrochemical, there is a significant gap between the existing theory and practice of risk management in the construction industry [4–6].
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data, explanation capacity, difficulty at development and appropriate domain.
11. Handling uncertainty The main objective of risk management is to reduce uncertainty and thus improve decision-making. Uncertainty has many different sources and different types, [21]. The main types of uncertainty include error, imprecision, variability, vagueness, ambiguity and ignorance. The diversity in terms for different types of uncertainty makes the modelling process a very difficult task because the information concerning each specific uncertainty is scarce. Several formal techniques for managing the different types of uncertainty have been developed but there has not been any consensus on the appropriateness of such techniques so far. It seems that there is no best theory of uncertainty and therefore the choice of the most appropriate technique depends upon the specific problem [22]. Four main approaches have been used for handling uncertainty in KB-DSS, namely Probability Theory, Certainty Factor Theory, Dempster–Shafer Theory and Fuzzy Set Theory.
10. Risk decision-making and uncertainty Decision-making problems are broadly categorised into deterministic, stochastic/risk and uncertain. Deterministic problems are those in which data are known with certainty; whereas stochastic problems are those in which data are not known with certainty but can be represented by a probability distribution; and uncertain refer to those problems in which data are not known. Most risk management problems fall into the last two categories because they are poorly structured problems for which few algorithms or mechanical methods exist. Indeed, risk management relies heavily on experience, subjectivity and human judgement. Poorly structured problems can not be formulated at the desired level of precision due to the surrounding uncertainty. However, recent developments in information technologies have made it possible to improve the formulation of poorly structured problems so as to aid decision making. In this regard much research effort has put on the development of decision support systems (DSS) and, particularly, knowledge based systems (KBS). According to Sprague and Watson [20] a DSS has the following main features: computer-based system helps decision-makers; confronts poorly structured problems; and has direct interaction. There are several DSS development techniques for applications in the construction industry and they include: rule-based expert systems; case-based expert systems; model-based expert systems; neural network based systems; and genetic algorithm based systems. The choice of an appropriate technique depends mainly upon the difficulty at knowledge acquisition, required
12. Probability Theory The classical approach to address uncertainty is the Bayesian theory of probability. Probability Theory has been used to model precisely described, repetitive experiments with observable but uncertain outcomes. The basic assumption in the classical theory of probability is that all uncertainties are measures of randomness or subjective measures of confidence. The main concern of random processes is the statistics. The heavy reliance on the Probability Theory as the only effective and reliable methodology to deal with uncertainty has historical roots. Probability Theory has well-established and sound scientific foundations and has been widely used for centuries. Probabilitics argue that random methods are the only effective methods for dealing with uncertainty. Deterministic approaches are commonplace in ‘hard’ sciences and the pursuit for precision in such areas is more favoured than qualitative models.
13. Certainty Theory Certainty Theory was formulated by Buchanan and Shortliffe [23] to handle uncertainty in the medical expert system (an expert system which diagnoses microbial infections) MYCIN. It was developed in attempt to overcome some of the weaknesses of Probability Theory. Certainty Theory fundamentals are the concepts of ‘‘certainty measures’’ which are associated with ‘‘factual
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statements’’. Certainty measures comprise numbers ranging from 1 to +1 where 1 represents complete certainty that a proposition is false and +1 represents complete certainty that a proposition is true. The factual statements are rules comprising antecedents and consequents.
14. Dempster–Shafer Theory of Evidence Dempster–Shafer Theory of Evidence [24] is usually called epistemic probability because it provides an alternative model for the assessment of numerical degrees of belief. Dempster–Shafer attempted to distinguish between uncertainty and ignorance and instead of probabilities, belief functions are used. The main distinctions between Bayesian models of numerical degrees and Dempster–Shafer model are the following: belief functions of Dempster–Shafer are set functions rather than point values; rejection of the law of additivity for belief in disjoint propositions; and Dempster–Shafer Theory has an operation for the pooling of evidence from various sources.
15. Fuzzy Set Theory Fuzzy Set Theory is a branch of modern mathematics that was formulated by Zadeh [25] to model vagueness intrinsic to human cognitive processes. Since then, it has been used to tackle ill-defined and complex problems due to incomplete and imprecise information that characterise the real-world systems. It is, therefore, suitable for uncertain or approximate reasoning that involve human intuitive thinking. Fuzzy Set Theory uses linguistic variables and membership functions with varying grades to model uncertainty inherent in natural language.
16. Appropriateness of the approaches Probability Theory considers all uncertainty random, however, not all types of uncertainty are random. A great deal of management issues in construction does not comply with randomness properties. They are mainly cognitive and thus do not lend themselves to precise measurement. Several authors have highlighted some serious shortcomings related to the Bayesian statistics. Zadeh [25] suggested that the assumptions associated with Bayesian function namely, mutual exclusivity of events, conditional independence and exhaustivity of events do not always hold. In addition, there is no distinction between randomness and ignorance. Probabilistic approaches are best suited to mechanistic systems
where the accuracy and precision are considered important. Certainty Factor Theory provides a simple method for handling uncertainty but it is said as lacking an adequate theoretical underpinning [26]. The Dempster– Shafer Theory is richer in terms of semantics since it allows an expression of partial knowledge. Its main shortcoming is the elicitation and interpretation of belief functions, [21]. Fuzzy Set Theory has proved to be a powerful technology in modelling unstructured problems particularly in the electronic industry. In recent years there has been a significant increase of its application in the construction industry, particularly for modelling uncertainty in KBSs.
17. Uncertainty and risk management in construction It has already been mentioned that construction risk management is mainly based upon experience, assumptions and human judgement. Consequently, risk management is mainly cognitive in nature. Kahneman and Tversky [27] uncovered important aspects of cognitive and non-cognitive types of uncertainty. Klir and Foger [28] discussed two types of uncertainty namely ambiguity and vagueness. Ambiguity is generally due to a non-cognitive root causes. Ambiguity is a state in which an expression or word may have a number of distinct meanings and only the context may help clarifying the real meaning. Evidence theory has proved to be the most suitable mathematical framework to handle ambiguity type of uncertainty. Vagueness arises when the outcome of an experiment can not be properly observed. A vague expression is ill defined and lacks preciseness or sharpness. For example, the global risk factor ‘‘Strong competition’’ does not have an exact meaning, because the qualifier ‘‘strong’’ may assume several degrees of intensity. ‘‘Strong competition’’ may involve a wide spectrum of human perceptions, as there are no rigorous definition of what ‘‘Strong competition’’ is. A particular type of vagueness is fuzziness. Fuzziness is a kind of imprecision where the transition from a membership state of an element to a set is gradual. It is, nevertheless, most frequent in situations where human judgement is an essential feature such as reasoning, learning and decision-making process.
18. Fuzzy Set Theory Fuzzy Set Theory has been used to tackle ill-defined and complex problems due to incomplete and imprecise information that characterise the real-world systems. Zadeh stated that ‘‘as the complexity of a system increases, human ability to make precise yet significant
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statements about its behaviour diminishes until a threshold is reached beyond which precision and significance become mutually exclusive’’—the Principle of Incompatibility. Then, it follows that modelling complex or ill-defined systems can not be made precisely. Fuzzy Set Theory is not intended to replace Probability Theory but rather to provide solutions to problems that lack mathematical rigour inherent to Probability Theory. Essentially, Fuzzy Set Theory is an extension of the classical Boolean or binary logic. In Classical Set theory, a set is a collection of objects having a general property, for example, a set of clients. In Classical Logic, an element is, therefore, either or not a member of a set. The boundaries of concepts are very rigid or ‘crisp’ and there is no room for grey or ‘in between’ states. There are no intermediate grades of membership between full and non-membership. This deterministic ‘yes-or-no’ or ‘dichotomous’ approach is nowadays widespread practice in systems modelling, reasoning process and computing. The main problem with binary approach is that it fails to convey information effectively, that is, the states between full and non-membership are ignored yet they are very important. Meanwhile, most real-world systems are very complex and ill defined to be well understood and modelled precisely. The essence of fuzziness, in contrast to binary or dual logic, is that the transition from a membership to nonmembership state of an element of a set is gradual rather than abrupt. Thus, Fuzzy Set Theory allows a generalisation of the classical set concept to model complex or ill-defined systems. The main concepts associated with Fuzzy Set Theory, as applied to decision systems are membership functions, linguistic variable, natural language computation, linguistic approximation, fuzzy set arithmetic operations, set operations and fuzzy weighted average. Details concerning these topics can be found in [25,29].
19. Linguistic variable Research in cognitive psychology suggests that individuals base their thinking on conceptual patterns and mental images rather than on any quantity or numbers. Although natural language is imprecise it conveys valuable information and despite the vagueness inherent in natural language humans can understand each other perfectly. The concept of linguistic variables lies at the core of Fuzzy Set Theory, since the basics of Fuzzy Set Theory is the manipulation of linguistic expressions instead of numbers. The values assumed by linguistic variables are words. A linguistic variable differs from a numerical variable in that its values are not numbers but words or sentences in a natural or artificial language. Since words in general are less precise than numbers, the concept of linguistic variables serves the
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purpose of providing a means of approximate characterisation of phenomena that are too complex or too ill defined to be amenable to description in conventional quantitative terms. Any risk factor presented in Fig. 1 can be characterised using linguistic variables through its likelihood of occurrence and its severity or impact. Examples of linguistic variables are expressions such as, ‘‘need for job’’, ‘‘number of bidders’’, and ‘‘market conditions’’. These linguistic variables may assume different values such as ‘‘very high’’, ‘‘high’’, ‘‘moderate’’, ‘‘low’’ and ‘‘very low’’, which are fuzzy sets (membership functions) and represent the perception of the decision-maker on the magnitude of any risk factor.
20. Membership functions A fuzzy set is a set whose elements have varying degrees of membership. The degrees of membership of an element are expressed by a membership function. Membership functions in Fuzzy Set Theory play a similar role to that of probability distribution functions in Probability Theory, that is, membership functions are used to represent uncertainty. A membership function is a function that maps a universe of objects, X, onto the unit interval [0, 1]. The universe of objects represents the elements of the set and the interval corresponds to the set of grades. The grades of membership in fuzzy sets may fall anywhere in the interval [0, 1]. A degree of 0 (zero) means that an element is not a member of the set at all. A degree of 1 (one) represents full membership. In contrast with ‘crisp’ sets that have only one membership function, fuzzy sets have a large number of membership functions.
21. Modelling global risk factors using Fuzzy Set Theory Global risk factors that affect construction cost performance can be modelled using Fuzzy Set Theory and a fuzzy decision support system can be developed. For that purpose it is necessary: to determine the most significant risk factors; define the different linguistic variables, which correspond to the constructs used by construction professionals to describe risk factors; define the membership functions for each linguistic variable and build a knowledge base. Significant risk factors, linguistic variables, membership functions and knowledge base can be determined through literature search, questionnaires and interviews with construction industry practitioners. The knowledge base is a repository of experts’ knowledge and experience. The knowledge in the knowledge base can be represented using ‘‘production rules’’, which is a knowledge representation system that seems to capture well risk knowledge, because a great deal of knowledge and heuristics used
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by construction contractors in risk decision-making is in the form of rules (for example, IF subcontractor A is very experienced, THEN performance is good; IF inflation is medium and exchange rate is medium THEN economic risk is medium). The main advantage of production rules are that they are easy to understand, provide modularity in design, which enables addition, deletion and change of rules independently. In addition to the knowledge base, databases and analytical models can be incorporated in the decision support system in order to enhance its performance. The inputs to the decision support system are the assessments of the different global risk factors specific to a project in linguistic terms (high, medium, low). The system checks the knowledge base and databases and performs natural language computations and produces the risk impact for each group of risk factors as well as the overall risk (combination of partial risk impacts) and the corresponding likelihood in linguistic terms. For example the overall risk impact can be ‘‘low’’ with ‘‘high’’ likelihood. The system can also provide recommendations on the most appropriate risk response strategies in the light of risk analysis results. The decision-maker can then make her/his judgement and take appropriate measures to mitigate project risks and thus improve the likelihood of project success.
22. Conclusions This paper discussed the core issues of global risk factors modelling, assessment and management. Evidence shows that poor cost performance of construction projects seems to be the norm rather than the exception, and both clients and contractors suffer significant financial losses due to cost overruns. It is suggested that global risk factors pose more challenges to construction contractors, which are less familiar with them. In addition, they lack effective techniques and tools to handle these risks. Risk management has traditionally been largely based on experience and subjective judgement; that is, it features humanistic systems that are not characterised by precision. Unlike mechanistic systems, humanistic systems have proved impervious to mathematical analysis and computer simulation. A humanistic system is a system that is heavily dependent on human judgement and perceptions. As such, there is a need to explore new directions towards risk modelling, assessment and management. Different techniques for handling uncertainty were evaluated and preliminary results indicate that global risk factors affecting construction cost performance can be successfully modelled, assessed and managed using Fuzzy Set Theory and Decision Support System technologies. For that purpose it is necessary to determine significant global risk factors, define linguistic variables,
devise reliable membership functions, and build the knowledge base, as important ingredients of a fuzzy decision support system. Both the inputs and outputs of the system are qualitative and in natural language, which facilitates its use and understanding. The decision framework would be an important tool for contractors to increase their awareness, identify global risk factors affecting cost performance, assess their impact and likelihood and take appropriate measures in order to reduce their impact on cost performance.
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