Thesis (MEng)--Stellenbosch University, 2003. / ENGLISH ABSTRACT: Whenever projects fail, analysis of the causes has shown that risks were present from day one. Often individuals at some level in the project team have knowledge of these risks and they could have been identified and appropriate remedial action taken. Risk, whether identified or not, generally results in some increase in financial exposure on behalf of the organisation, but, if managed well, offers a potential that could lead to increased profits. There has been a tremendous explosion regarding the amount of data that organisations generate, collect and store. Managers are beginning to recognize the value of this asset and are increasingly relying on intelligent systems to access, analyse, summarise and interpret information from large and multiple data sources. These systems help them to make critical decisions at a faster rate or with a greater degree of confidence. Data mining is a promising new technology that helps bring intelligence into these systems. The purpose of this thesis is to present a methodology that integrates a data mining technique with a decision support system in order to form an intelligent decision support system. The implementation of such an intelligent decision support system will enable project and project risk managers to improve the management of and reduce risk within a project. This thesis consists of two sections. The first section describes the processes and characteristics of project management, project risk management, data mining and decision support systems. The aim is to provide the reader with a background about these four management methodologies. The second section describes the methodology of how the processes of project and project risk management can benefit from the integration of a data mining technique and a decision support system. An application that uses the case-based reasoning approach as a data mining technique to intelligently profile a project according to its risks is demonstrated. / AFRIKAANSE OPSOMMING: Wanneer projekte misluk, toon 'n analise van die oorsake dat risiko's vanuit die staanspoor daar teenwoordig was. Individuele persone op verskillende vlakke in die projekspan is dikwels daarvan bewus. Hierdie risiko's kon geïdentifiseer gewees het en regstellende stappe kon geneem gewees het. Risiko, hetsy geïdentifiseer of nie, loop gewoonlik uit op 'n sekere mate van toename in finansiële blootstelling namens die organisasie, maar wanneer dit goed bestuur word, bied dit 'n potensiaal vir verhoogde wins. Daar is 'n geweldige vermeerdering in die hoeveelheid data wat organisasies genereer, versamel en berg. Bestuurders begin alreeds die onskatbare waarde van hierdie bate besef en steun toenemend op intelligensiestelsels vir toegang, analise, opsomming en interpretasie van inligting van omvangryke en veelsoortige databronne. Hierdie sisteme stel hulle in staat om kritieke besluite vinniger of met 'n groter mate van vertroue te neem. Dataontginning is 'n belowende nuwe tegnologie wat daartoe bydra dat intelligensie in hierdie sisteme ingebring word. Die doel van hierdie tesis is om 'n metodologie wat 'n dataontginningstegniek met 'n besluitnemingsondersteuningsisteem integreer sodat 'n intelligente besluitnemingsondersteuningsisteem gevorm kan word. Die implementering van so 'n intelligensie besluitnemingsondersteuningsisteem sal projekbestuurders en projekrisikobestuurders in staat stelom die bestuur van 'n projek te verbeter en die risiko binne die projek te verminder. Hierdie tesis word in twee dele aangebied. Die eerste deel beskryf die prosesse en karakteristieke van projekbestuur, projekrisikobestuur, dataontginning en besluitondersteuningsisteme. Sodoende word aan die leser agtergrondinligting van hierdie vier bestuursmetodologieë verskaf. Die tweede deel beskryf die metodologie en hoe die prosesse van projekbestuur en projekrisikobestuur voordeel kan trek uit die integrasie van 'n dataontginningstegniek en 'n besluitondersteuningsisteem. 'n Toepassing is ontwikkel wat die gevallebasis beredeneringsbenadering as 'n dataontginingstegniek gebruik om 'n projek op 'n intelligente wyse volgens sy risiko's uit te beeld.

This research explores the theory and practice of risk assessment and project evaluationand proposes novel alternatives. Reviewing literature revealed a continuous endeavourfor better project risk modelling and analysis. A number of proposals for improving theprevailing Probability-Impact (P-I) risk model can be found in literature. Moreover,researchers have investigated the feasibility of different theories in analysing projectrisk. Furthermore, various decision support systems (DSSs) are available for aidingpractitioners in risk assessment and decision making. Unfortunately, they are sufferingfrom a low take-up. Instead, personal judgment and past experience are mainly used foranalysing risk and making decisions.In this research, a new risk model is proposed through extending the P-I risk model toinclude a third dimension: probability of impact materialisation. Such an extensionreflects the characteristics of a risk, its surrounding environment and the ability ofmitigating its impact. A new assessment methodology is devised. Dempster-ShaferTheory of Evidence (DST) is researched and presented as a novel alternative toProbability Theory (PT) and Fuzzy Sets Theory (FST) which dominate the literature ofproject risks analysis. A DST-based assessment methodology was developed forstructuring the personal experience and professional judgment of risk analysts andutilising them for risk analysis. Benefiting from the unique features of the EvidentialReasoning (ER) approach, the proposed methodology enables analysts to express theirevaluations in distributed forms, so that they can provide degrees of belief in apredefined set of assessment grades based on available information. This is a veryeffective way for tackling the problem of lack of information which is an inherentfeature of most projects during the tendering stage. It is the first time that such anapproach is ever used for handling construction risk assessment. Monetary equivalent isused as a common scale for measuring risk impact on various project success objectives,and the evidential reasoning (ER) algorithm is used as an assessment aggregation toolinstead of the simple averaging procedure which might not be appropriate in allsituations. A DST-based project evaluation framework was developed using projectrisks and benefits as evaluation attributes. Monetary equivalent was used also as acommon scale for measuring project risks and benefits and the ER algorithm as anaggregation tool.The viability of the proposed risk model, assessment methodology and projectevaluation framework was investigated through conducting interviews with constructionprofessionals and administering postal and online questionnaires. A decision supportsystem (DSS) was devised to facilitate the proposed approaches and to perform therequired calculations. The DSS was developed in light of the research findingsregarding the reasons of low take-up of the existing tools. Four validation case studieswere conducted. Senior managers in separate British construction companies tested thetool and found it useful, helpful and easy to use.It is concluded that the proposed risk model, risk assessment methodology and projectevaluation framework could be viable alternatives to the existing ones. Professionalexperience was modelled and utilised systematically for risk and benefit analysis. Thismay help closing the gap between theory and practice of risk analysis and decisionmaking in construction. The research findings recommend further exploration of thepotential applications of DST and ER in construction management domain.