Project cost risk analysis: A Bayesian networks approach for modeling dependencies between cost items

Khodakarami, Vahid; Abdi, Abdollah

doi:10.1016/j.ijproman.2014.01.001

Cited by 137 publications

(77 citation statements)

References 53 publications

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“…Uncertainty of cost items is an important aspect of complex projects, and its analysis help decision makers understand and model different factors affecting funding exposure and ultimately estimate the cost of the project [32]. The quantification of these uncertainties is a basic premise to determine the risk of the investment project.…”

Section: Resultsmentioning

confidence: 99%

Multibody Dynamic Stress Simulation of Rigid-Flexible Shovel Crawler Shoes

Frimpong

Thiruvengadam

2016

Minerals

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Abstract:The uncertainty in road transportation of wood is inherent to its operational costs, to the amount of transported wood, to the traveled distance, to its revenue, and more. Although it is not possible to measure this uncertainty fully, it can be quantified by the investment risk, which is the probability and degree of financial loss. The objective of this study is to quantify the financial risk of the investment in wood transportation through Monte Carlo simulation, which uses realistic situations to estimate the operational cost of vehicles used for road transportation of wood. We quantify these uncertainties by assessing financial risk and building pseudorandom scenarios with the Monte Carlo simulation method, in addition to the Net Present Value techniques, the Modified Internal Rate of Return, and the Profitability Index, all commonly used in financial investment projects. The results show that the estimated operational costs are equivalent to the actual ones, along with the evidence that the cost of fuel, the driver's manpower, and tires are components that mainly increase the degree of financial risk for an investment project in road transportation of wood. In contrast, optimizing the amount of transported wood and maximizing wood transportation cost have a significant and positive correlation with the volume of transported wood and the average price of wood transportation, leading to a reduction in the degree of financial risk.

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Section: Resultsmentioning

confidence: 99%

Multibody Dynamic Stress Simulation of Rigid-Flexible Shovel Crawler Shoes

Frimpong

Thiruvengadam

2016

Minerals

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“…Some researchers used the Bayesian Network (BN) modelling framework for assessing project risk and uncertainty [21][22][23][24]. Nguyen et al [25] developed ProRisk methodology which serves as a decisionmaking tool to choose the best risk treatment strategy.…”

Section: Methods For Assessment and Management Of Risk And Uncertaintymentioning

confidence: 99%

Simulating the Impact of Activity Uncertainties and Risk Combinations in R&D Projects

Chenarani¹,

Дружинин²,

Kritskiy³

2017

JESTR

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The quantifiable consequences of uncertainty are simulated and analyzed for R&D projects. These consequences include variation in duration and cost of activities and the presence of risk factors in project. Categorizing risks into four types is proposed according to their impact on the main constraints of the project. The most prominent type of risk in R&D projects is the rework risk but common software packages for project management are not able to simulate them. A discrete event based Monte Carlo simulation method is employed for project simulation. The required project and risk data for simulation are represented by means of dependency structure matrix (DSM). For calculating the effects of rework risk, BFS graph traversal algorithm is used. A simulation program is developed and implemented in a small turbojet engine preliminary design project. The results of this research can improve the effectiveness of decision-making process by project manager where choosing appropriate method for reducing risk effects under uncertainty conditions is required.

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“…The search for 'multi-project' and 'programme' also yielded some results. Lytvyn and Rishnyak [39] presented a decision-making algorithm that can be used when the multi-project environment influences a project. This, however, did not attempt to simulate and identify risks on a portfolio or programme level.…”

Section: Surveymentioning

confidence: 99%

Using Monte Carlo Simulation to Create a Ranked Check List of Risks in a Portfolio of Railway Construction Projects

Joubert

Pretorius

2017

SAJIE

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When introducing quantified risk assessments at a South African state-owned freight company, a skills gap was found during the identification and quantified risk analysis. To help with risk identification, a checklist of risks for railway construction projects was developed. The basis of this checklist was 38 individual railway construction project risk registers that were collated into a single risk register. After the risks had been cleaned up and classified, a Monte Carlo simulation using @Risk software was done that produced a ranked check list of risks. This list of risks is valuable because subject matter experts developed it, and can be used as a risk identification checklist by stakeholders in similar projects. The simulation results also showed that project scope is an influencing factor on the ranking of risks. OPSOMMINGTydens die bekendstelling van gekwantifiseerde risikowaardebepalings by 'n Suid-Afrikaanse vervoermaatskappy in staatsbesit, is daar 'n tekort aan vaardighede gevind tydens risikoidentifisering en 'n gekwantifiseerde risiko-analise. Om hierdie probleem te beredder, is 'n lys van spoorbaankonstruksie risikos ontwikkel. Die basis van hierdie lys was 38 individuele spoorbaankonstruksie projek risiko registers wat opgeneem is in 'n enkele risiko register. Na afloop hiervan is die risiko register skoongemaak, geklassifiseer, en 'n Monte Carlo-simulasie met behulp @Risk sagteware gedoen wat 'n ranglys van risikos geproduseer het. Hierdie ranglys wys dat die projek omvang 'n invloed het op die posisie van risikos in die ranglys. INTRODUCTIONThis article presents, as a case study, a way in which quantified risk registers are used to identify and rank the risks in a portfolio of railway construction projects. It starts by presenting the context in which the case study risk registers were collected. It continues with a concise literature survey on risks that can be found on railway construction projects. The model used during the simulation is then broadly discussed, referring in more detail to the use of MS Excel and to Monte Carlo simulation software such as @Risk. The article continues with the simulation results, where a list of ranked risks found on rail construction projects is presented. The simulation results provide some evidence that project scope is a factor determining the ranking of risk in similarly-scoped projects.

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Project cost risk analysis: A Bayesian networks approach for modeling dependencies between cost items

Cited by 137 publications

References 53 publications

Multibody Dynamic Stress Simulation of Rigid-Flexible Shovel Crawler Shoes

Multibody Dynamic Stress Simulation of Rigid-Flexible Shovel Crawler Shoes

Simulating the Impact of Activity Uncertainties and Risk Combinations in R&D Projects

Using Monte Carlo Simulation to Create a Ranked Check List of Risks in a Portfolio of Railway Construction Projects

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