Considerations on Project Quantitative Risk Analysis

Purnus, Augustin; Bodea, Constanţa-Nicoleta

doi:10.1016/j.sbspro.2013.03.031

Cited by 25 publications

(10 citation statements)

References 1 publication

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“…Cost contingency can be determined employing deterministic and probabilistic approaches. Both approaches are applicable to discretely decide the costs and time contingencies (Bakhshi and Touran 2014;Eldosouky et al 2014;Pawan and Lorterapong 2015;Purnus and Bodea 2013), or the combination of time and cost (Purnus and Bodea 2013). The biggest difference between the two approaches is that the deterministic approach is based on deterministic and point-estimate values, whereas the probabilistic approach is based on stochastic values.…”

Section: Literature Reviewmentioning

confidence: 99%

Cost Contingency and Cost Evolvement of Construction Projects in the Preconstruction Phase

Hoseini

Bosch‐Rekveldt

Hertogh

2020

J. Constr. Eng. Manage.

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The current literature discusses the methods to estimate the costs and cost contingency. The literature also distinguishes "known unknowns" and "unknown unknowns" contingencies. Little is written, however, about the evolvement of total project cost estimates during the preconstruction phase of construction projects. Moreover, not many studies are investigating the "known unknowns" and "unknown unknowns" contingencies in real construction projects. Practice expressed the need for getting more insight into the development of the estimated costs of the projects in the preconstruction phase. This paper, therefore, discusses the estimate of the total project costs (and cost contingency) in the preconstruction phases of 29 Dutch flood defense projects using a case study approach. Altogether, the projects have experienced an 11.51% increase in the estimated costs compared to the initial estimates, which is low compared to previous studies. This increase in the cost estimates of the flood defense projects can be explained by "technical" reasons. The investigation of "known unknowns" and "unknown unknowns" contingencies shows that the percentage of the "unknown unknowns" contingency has increased in the preconstruction phase while a reduction was expected. This increase suggests that the projects were not confident about their estimates and the increase can be explained by a lack of experience, organizations' culture, or the phenomenon of "pessimistic bias." Practitioners can avoid "pessimistic bias" behavior by asking for opinions about their estimates and using historical project data. Further research is suggested into realized cost contingency after project execution.

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Section: Literature Reviewmentioning

confidence: 99%

Cost Contingency and Cost Evolvement of Construction Projects in the Preconstruction Phase

Hoseini

Bosch‐Rekveldt

Hertogh

2020

J. Constr. Eng. Manage.

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“…Hulett [6] stated that CPM scheduling tools, which include manual and software-based systems, cannot handle the uncertainty that exists in the real world regarding project activity durations because these tools assume that activity durations are with certainty as singlepoint numbers. A stochastic risk analysis technique called Monte Carlo simulation can be applied to evaluate project uncertainties [14][15][16][17]. Monte Carlo simulation is suitable for determining the project completion date because the date is determined by the uncertainty in the duration of many activities that have already been linked logically in the CPM schedule [6].…”

Section: Monte Carlo Simulationmentioning

confidence: 99%

“…Formula 14is an objective function that requires minimizing the total project crash cost. Formulas (15) and (16) are constraints stipulating that the starting time of each node must not be earlier than that of the forward node plus the time of the activity between the two nodes minus the required activity crash time between the two nodes, with the starting time of the initial node being larger than or equal to zero. Formula (17) indicates that the crash time of activity for all segments should not be larger than the difference between the optimistic time or most likely time or pessimistic time minus its expected time, that is, the upper limit of the crash time of each activity.…”

Section: Model 2: Integer Linear Programming Model For Projectmentioning

confidence: 99%

A Hybrid Approach for Project Crashing Optimization Strategy with Risk Consideration: A Case Study for an EPC Project

Ouyang

Chen

2019

Mathematical Problems in Engineering

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This study aims to develop and provide a comprehensive evaluation strategy for schedule-related variations and time-cost analysis for an engineering–procurement–construction (EPC) project. Time-cost analysis is an important aspect of project scheduling, particularly in long-term and costly EPC projects. In this study, a hybrid method is proposed for the time-cost optimization strategy evaluation of a project. Monte Carlo simulation is applied to determine contingency plans and realize the effective management of estimated schedule uncertainties. A mathematical integer linear programming optimization model coded using CPLEX is developed to assess appropriate strategies for project execution under time and cost constraints. A set of project evaluation optimization models considering risk and project crash plan and the relationship between crash cost and delay penalty is also developed for assessing project feasibility. The correlation between project risk and crashing strategy has seldom been evaluated simultaneously in previous research. This work fills this research gap by quantifying the feasibility of a project, with combined data on risk, schedule, and cost as evaluation indicators. It allows project managers to consider management issues and strategies before they implement projects. A practical example with numerical applications is presented to illustrate the contribution of the decision-making support mechanism, and several managerial insights are provided.

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“…Purnus and Bodea [11] noted that project quantitative risk analysis is the hardest part of project risk management and that, although many software solutions to implement risk management methods have been developed, these are usually not used or are improperly applied.…”

Section: Introductionmentioning

confidence: 99%

Some Aspects of the Use and Usefulness of Quantitative Risk Analysis Tools in Project Management

Hugo

Pretorius

Benade

2018

SAJIE

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Central to project management is risk management-and the need to develop better and more specific tools to manage risk. This is driven by the desire to reduce uncertainty and risk on projects. Quantitative tools such as Monte Carlo simulation are sometimes used to analyse risk, but it is not clear how widespread, accepted, or useful such tools are for practising project personnel. This research was undertaken to gain feedback from practising project and risk management personnel about the use of risk management tools. Special attention was given to the levels of use of quantitative risk management tools and the benefits gained from their use. A conceptual framework was created linking the factors that affect tool use to the actual tool use levels, and ultimately to project performance. Research questions reported on in this paper addressed levels of tool use, factors that affect tool usage, and the benefit aspect of tool use on project performance. Data were gathered via an on-line questionnaire that was statistically analysed, and a number of hypotheses were tested. The results of this study show that the levels of use of both qualitative and quantitative risk management tools are low compared with general project management tools. Correlations were found between a number of factors that affect tool use.

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Considerations on Project Quantitative Risk Analysis

Cited by 25 publications

References 1 publication

Cost Contingency and Cost Evolvement of Construction Projects in the Preconstruction Phase

Cost Contingency and Cost Evolvement of Construction Projects in the Preconstruction Phase

A Hybrid Approach for Project Crashing Optimization Strategy with Risk Consideration: A Case Study for an EPC Project

Some Aspects of the Use and Usefulness of Quantitative Risk Analysis Tools in Project Management

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