Thesis (MScEng)--Stellenbosch University, 2012. / 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.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/71653 |
| Date | 12 1900 |
| Creators | Van Niekerk, Mariette |
| Contributors | Bekker, J., Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. |
| Publisher | Stellenbosch : Stellenbosch University |
| Source Sets | South African National ETD Portal |
| Language | en_ZA |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 203 p. |
| Rights | Stellenbosch University |
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