Quantitative Modelling Methods for the Incorporation of Uncertainty into Construction Project Estimates
Thesis (MEng (Civil Engineering))--University of Stellenbosch, 2006.
Most construction projects do not complete exactly as scheduled or exactly as priced. During the implementation of a project there is almost certainly some deviation from the original estimate. The implementation of a majority of projects has actually been shown to cost more and take longer than originally estimated. However, the duration and cost performance of a project’s implementation is measured against the initial estimate produced. Thus if a project is considered to have completed late or over budget then essentially the duration or cost estimated was insufficient. Due to the fact that estimates are produced in a present day environment for inherently unique projects that occur in uncertain future environments, the estimates produced will need to incorporate uncertainty to increase their likelihood of achievability. This study aims to derive methods to incorporate future uncertainty into project estimates. This uncertainty is incorporated, analysed and manipulated through the use of Probabilistic models and First Order Second Moment Reliability methods. The derived methods provide project management professionals with tools that enable them to design estimates that incorporate future uncertainty and are reliable to a specified degree. Further methods are then derived to probabilistically assess the commercial feasibility of a project in an uncertain future environment. These derived methods then provide project managers and decision makers with more reliable procedures and information which in turn should assist them in making correct, project orientated decisions and ultimately increase profit reliability and client satisfaction.