1887

n South African Journal of Industrial Engineering - Developing a tool for project contingency estimation in a large portfolio of construction projects

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Abstract

To enable the management of project-related risk on a portfolio level in an owner organisation, project contingency estimation should be performed consistently and objectively. This article discusses the development of a contingency estimation tool for a large portfolio that contains similar construction projects. The purpose of developing this tool is to decrease the influence of subjectivity on contingency estimation methods throughout the project life cycle, thereby enabling consistent reflection on project risk at the portfolio level. Our research contribution is the delivery of a hybrid tool that incorporates both neural network modelling of systemic risks and expected value analysis of project-specific risks. The neural network is trained using historical project data, supported by data obtained from interviews with project managers. Expected value analysis is achieved in a risk register format employing a binomial distribution to estimate the number of risks expected. By following this approach, the contingency estimation tool can be used without expert knowledge of project risk management. In addition, this approach can provide contingency cost and duration output on a project level, and it contains both systemic and project-specific risks in a single tool.

Projek-gebeurlikheidsreserwes moet konsekwent en objektief beraam word ten einde die bestuur van projek-verwante risiko op portefeuljevlak in eienaar-organisasies moontlik te maak. Hierdie artikel bespreek die ontwikkeling van 'n gebeurlikheidsreserwe-beramer vir 'n portefeulje met baie konstruksieprojekte wat almal 'n soortgelyke aard het. Die doel met die ontwikkeling van hierdie funksie is om die invloed van subjektiwiteit op gebeurlikheidsreserwe-beramingmetodes deur die volledige projeklewensiklus te verminder en daardeur projekrisiko konsekwent op portefeuljevlak te weerspieël. Die navorsingsbydrae is die lewering van 'n hibriede funksie wat beide neurale netwerk modellering van sistemiese risiko en die verwagte waarde-ontleding van projek-spesifieke risikos insluit. Die neurale netwerk word geleer deur historiese projekdata te gebruik, tesame met ondersteunende data wat deur onderhoude met projekbestuurders verkry is. Die verwagte waarde-ontleding word deur 'n risiko-register formaat bewerkstellig, wat die aantal verwagte risikos met 'n binomiaalverdeling beraam. Deur hierdie benadering te volg kan die gebeurlikheidsreserwe-beramingfunksie gebruik word sonder diepte-kennis van risikobestuur in projekte. Dit verskaf gebeurlikheidkoste en -tydsduur beramings op projekvlak, en bevat beide sistemiese risiko en projek-spesifieke risiko in 'n enkele funksie.

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/content/indeng/25/3/EJC165163
2014-11-01
2016-12-03
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