Browsing by Author "Van Niekerk, Mariette"

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    Developing a tool for project contingency estimation in a large portfolio of construction projects
    (Southern African Institute for Industrial Engineering, 2014-11) Van Niekerk, Mariette; Bekker, James
    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.
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    Developing a tool for project contingency estimation in Eskom Distribution Western Cape Operating Unit
    (Stellenbosch : Stellenbosch University, 2012-12) Van Niekerk, Mariette; Bekker, James F.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: Construction projects are risky by nature, with many variables a ecting their outcome. A contingency cost and duration are allocated to the budget and schedule of a project to provide for the possible impact of risks. To enable the management of project-related risk on a portfolio level, contingency estimation must be performed consistently and objectively. The current project contingency estimation method used in the capital program management department of Eskom Distribution Western Cape Operating Unit is not standardised, and is based solely on expert opinion. The aim of the study was to develop a contingency estimation tool to decrease the in uence of subjectivity on contingency estimation methods throughout the project lifecycle so as to enable consistent project risk re ection on a portfolio level. From a review of contingency estimation approaches in literature, a hybrid method combining neural network analysis of systemic risks and expected value analysis of project-speci c risks was chosen. Interviews were conducted with project managers (regarding network asset construction projects completed in the last two nancial years) to distinguish systemic and project-speci c risk impact on cost and duration growth. Outputs from 22 interviews provided three data patterns for each of 89 projects. After interview data processing, 138 training patterns pertaining to 85 projects remained for neural network training, validation and testing. Six possible neural network inputs (systemic risk drivers) were selected as project de nition level, cost, duration, business category, voltage category and job category. A multilayer feedforward neural network was trained using a supervised training approach combining a multi-objective simulated annealing algorithm with the standard backpropagation algorithm. Neural network results were evaluated for di erent scenarios considering possible combinations of model input variables and number of hidden nodes. The best scenario (exclusion of business category input with nine hidden nodes) was chosen based on training and validation errors. Validation error levels are comparable to those of similar studies in the project management eld. The chosen scenario was shown to outperform multiple linear regression, but calculated R2 values were lower than anticipated. It is expected that neural network performance will further improve as additional training patterns become available. The trained neural network was combined with an expected value analysis tool (risk register format) to estimate contingency due to systemic risks alongside an estimation of contingency due to project-speci c risks. The project-speci c expected value method was modi ed by basing the contingency estimation on the expected number of realised risks according to a binomial scenario. A total cost distribution was included in tool outputs by assuming the contingency cost equal to the standard deviation of the cost estimate. To aid business integration of the developed tool, study outputs included the points in the project lifecycle model at which the tool should be applied, and the process by which tool outputs become inputs to the enterprise risk management system. By following this approach, systemic and project-speci c risks are contained in a single tool providing contingency cost and duration output on project level, while enabling integration with reporting on program, portfolio and enterprise level.