CTAN for Risk Assessments Using Multilevel Stochastic Networks

Moussa, Mohamed; Ruwanpura, Janaka Y.; Jergeas, George

doi:10.1061/(asce)0733-9364(2007)133:1(96)

Cited by 8 publications

(4 citation statements)

References 4 publications

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“…Includes the antecedent's influencing uncertainty at the different stages of projects, for instance, type and nature of the project, project management approaches, project planning, project selection (Thiry, 2002;Howell et al 2010;Shenhar, 2001;Chapman et al 1985;Dean 1986;Ghapanchi et al 2012). Factors such as project risk (Acebes et al, 2014(Acebes et al, , 2015 project duration, size, estimation (Lee et al 2009;Moussa et al 2007;Chapman 2006;Arashpour et al 2016;Barraza, 2011;Caron et al 2016), project portfolio (Martinsuo et al 2014;Petit, 2012), project operational goals (Tatikonda and Montoya-Weiss, 2001), project design (Ramasesh and Browning, 2014), project delays and overlaps, (Mitchell and Nault 2007;Dehghan, and Ruwnapura, 2014). Characteristics such as information technology (Bardhan et al 2007), project knowledge (Ahern et al 2014;Laine et al 2016;Dikmen et al 2012), profit (Gopal et al 2003), rate of return (Nobelius, 2004), project cost, (Elkjaer, 2000;Farshchian et al 2018), over-head cost (Goh and Hall, 2013) project budget (Günhan and Arditi, 2007), disruptions (Schatteman, 2008;Aaltonen, 2011), rescheduling cost (Bordley et al 2019;Xiang et al 2012), accounting information (Van Der Velde et al 2002, pe...…”

Section: Project Specificitiesmentioning

confidence: 99%

A Review and Classification of the Uncertainties in Projects: The Way Forward

Anand

Castello

Lecoeuvre

2021

RFGI

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This article aims to summarize existing research on various sources of uncertainty and to classify them based on the determinants and antecedents addressed in projects. A systematic review is conducted using a total of 140 research published in peer-reviewed scientific journals during the last three decades. We classify various uncertainty based on their individual, relational, group, organizational, project-oriented, and managerial characteristics. Additionally, we uncovered a few gaps such as, how uncertainties differ based on the size of the organization, the need for exploring uncertainty from a more cross-disciplinary perspective, differentiating the concept of complexity and risk with uncertainty and the role of chaos theory, that require future study. By leveraging significant findings, this is study contributes from the perspective of theory and practice to academic, project, and industrial management discipline.

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Section: Project Specificitiesmentioning

confidence: 99%

A Review and Classification of the Uncertainties in Projects: The Way Forward

Anand

Castello

Lecoeuvre

2021

RFGI

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“…In each layer, the modeler adds elements and connects them using links to draw a horizontal network of the elements representing the logic, the sequence of operation, and/or the flow of information among the elements. The multilayer is a parent/children hierarchy that permits decomposing elements (parents) to smaller elements (children) in a containment relationship (i.e., the parent element contains the children elements) (see, e.g., Moussa et al [34]). Each element has input parameters whose values are assigned by the modeler at the modeling time.…”

Section: Overview Of the Simulation Environmentmentioning

confidence: 99%

Hybrid Simulation Environment for Construction Projects: Identification of System Design Criteria

Moussa

Ruwanpura

Jergeas

et al. 2014

Journal of Construction Engineering

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Large construction projects are complex, dynamic, and unpredictable. They are subject to external and uncontrollable events that affect their schedule and financial outcomes. Project managers take decisions along the lifecycle of the projects to align with projects objectives. These decisions are data dependent where data change over time. Simulation-based modeling and experimentation of such dynamic environment are a challenge. Modeling of large projects or multiprojects is difficult and impractical for standalone computers. This paper presents the criteria required in a simulation environment suitable for modeling large and complex systems such as construction projects to support their lifecycle management. Also presented is a platform that encompasses the identified criteria. The objective of the platform is to facilitate and simplify the simulation and modeling process and enable the inclusion of complexity in simulation models.

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“…Many of them have developed risk assessment tools using various theories, e.g. network modeling (Moussa et al 2007), fuzzy concept (Choi et al 2004), and utility theory (Hsueh et al 2007). However, since industry practitioners are not familiar with these concepts they may not be useful in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Predicting Project Cost Performance Level by Assessing Risk Factors of Building Construction in South Korea

Sung

Shin

2011

Journal of Asian Architecture and Building Engineering

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There exists a strong relationship between the level of planning effort and overall project performance. Project stakeholders are concerned about various project objectives among which cost performance has long been regarded as one of the most crucial. Recognizing the lack of a systematic approach in assessing building project risk in association with cost performance prediction, this study identified 49 risk factors from various sources and quantified the relative impact of each factor. By normalizing these values, a Project Risk Score (PRS) has been developed for the purpose of predicting cost performance on a particular project. The 13 real case studies showed a statistically significant relationship between the PRS and cost performance. Predicting cost performance is not simple. Project managers must consider not only factors that are innate to the project, but also project-independent circumstances. This study provides straightforward guidance in assessing a particular project in order to recognize the level of cost performance before project execution. This risk-based performance prediction can improve early-stage project evaluation and enhances the possibility of achieving a sustainable business in a challenging environment.

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CTAN for Risk Assessments Using Multilevel Stochastic Networks

Cited by 8 publications

References 4 publications

A Review and Classification of the Uncertainties in Projects: The Way Forward

A Review and Classification of the Uncertainties in Projects: The Way Forward

Hybrid Simulation Environment for Construction Projects: Identification of System Design Criteria

Predicting Project Cost Performance Level by Assessing Risk Factors of Building Construction in South Korea

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