Developing a tool for project contingency estimation in Eskom Distribution Western Cape Operating Unit

Van Niekerk, Mariette (2012-12)

Thesis (MScEng)--Stellenbosch University, 2012.

Thesis

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.

AFRIKAANSE OPSOMMING: Konstruksieprojekte het van nature 'n ho e risiko omdat hulle uitsette deur baie veranderlikes gea ekteer word. Gebeurlikheidsreserwes vir koste en tyd word toegeken aan die begroting en skedule van 'n projek om voorsiening te maak vir die moontlike gevolge van risiko's. Om die bestuur van projekverwante risiko op 'n portefeulje-vlak te vergemaklik, moet die beraming van gebeurlikheidsreserwes op 'n konsekwente en objektiewe manier uitgevoer word. Die huidige beramingsmetode vir projek gebeurlikheidsreserwes in die kapitaal programbestuur departement van Eskom Distribusie Wes-Kaap Bedryfseenheid is nie gestandardiseer nie, en word slegs gebaseer op deskundige opinie. Die doel van hierdie studie was om 'n gebeurlikheidsreserwe beramingsinstrument te ontwikkel wat die invloed van subjektiwiteit op beramingsmetodes verminder deur die hele projeklewensiklus, en sodoende die konsekwente weerspie eling van projekrisiko op 'n portefeulje-vlak, te bewerkstellig. Vanuit 'n studie van bestaande literatuur oor gebeurlikheidsreserwe-beraming, is 'n hibriede metode wat neurale netwerk analise van sistemiese risiko's en verwagte waarde analise van projek-spesi eke risiko's kombineer, gekies. Onderhoude is gevoer met projekbestuurders (rakende netwerk batekonstruksieprojekte wat voltooi is in die afgelope twee nansi ele jare) om te onderskei tussen die impak van sistemiese en projek-spesi eke risiko's op koste- en duurgroei. Uitsette van 22 onderhoude het drie datapatrone vir elk van 89 projekte verskaf. Na onderhouddata verwerk is, het 138 datapatrone vanuit 85 projekte oorgebly vir neurale netwerk opleiding, validasie en toetsing. Ses moontlike neurale netwerk insette (sistemiese risikodrywers) is gekies as projek de nisievlak, koste, duur, besigheidskategorie, spanningskategorie en werkskategorie. 'n Multi-laag vooruitvoerende neurale netwerk is deur 'n opleidingonder- toesig benadering opgelei { 'n multi-doelwit gesimuleerde uitgloei ngsalgoritme gekombineer met die standaard agteruit-propagerende algoritme. Die resultate van die neurale netwerk is oorweeg vir verskillende scenario's rakende moontlike kombinasies van die aantal versteekte nodes en model insetveranderlikes. Die beste scenario (uitsluiting van besigheidskategorie inset met nege versteekte nodes) is gekies op grond van opleidings- en validasiefoute. Validasie foutvlakke is vergelykbaar met di e van soortgelyke studies in die projekbestuur veld. Daar is gewys dat die gekose scenario meervoudige line^ere regressie klop, maar met laer R2 waardes as wat verwag is. Dit word verwag dat die neurale netwerk beter sal presteer soos bykomende opleidingsdatapatrone beskikbaar word. Die opgeleide neurale netwerk is gekombineer met 'n verwagte waarde analise instrument (risiko-register formaat) om gebeurlikheidsreserwes as gevolg van sistemiese risiko's hand-aan-hand met gebeurlikheidsreserwes as gevolg van projekspesi eke risiko's, te beraam. Die projek-spesi eke verwagte waarde metode is aangepas deur gebeurlikheidsreserwe-beraming te baseer op die aantal verwagte gerealiseerde risiko's volgens 'n binomiaal scenario. 'n Totale koste-verdeling is ingesluit in modeluitsette deur aan te neem dat die gebeurlikheidsreserwe vir koste gelyk is aan die standaardafwyking van die kosteberaming. Om die besigheidsintegrasie van die ontwikkelde instrument te vergemaklik, het studie uitsette die punte in die projek lewensiklus waarby die instrument toegepas moet word, en die proses waardeur instrument uitsette omgesit word na insette vir die risikobestuur sisteem op ondernemingsvlak, ingesluit. Deur hierdie benadering te volg, word sistemiese en projek-spesi eke risiko's omvat in een instrument wat gebeurlikheidsreserwes vir koste en tyd op projekvlak verskaf. Die integrasie met verslagdoening op program-, portefeulje- en ondernemingsvlak word ook bewerkstellig.

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