An Approach Based on Bayesian Network for Improving Project Management Maturity: An Application to Reduce Cost Overrun Risks in Engineering Projects

Sanchez, Felipe; Bonjour, Éric; Micaëlli, Jean-Pierre; Monticolo, Davy

doi:10.1016/j.compind.2020.103227

Cited by 42 publications

(18 citation statements)

References 47 publications

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“…So, the considered value engineering session led to a dynamic increase in project efficiency and a decrease in the risk that the funds invested in the project will bring insufficient benefits per unit of cost throughout the entire project cycle. And as emphasized in [45], project management in general aims to increase the likelihood of project success.…”

Section: Tablementioning

confidence: 99%

Development of conceptual model of project value dynamics analysis in conditions of uncertainty

Bugrov

Бугрова

2021

EEJET

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A conceptual model for analyzing the dynamics of the value of the project, achieved as a result of engineering, under conditions of uncertainty has been developed. In the methodological context, the proposed approach is based on an array of isovalues, each of which corresponds to its own level of optimism in forecasting the cash flow for the project. With the increase in the efficiency of the project due to engineering, the entire array of iso-value lines’ changes its geometrical position, moving further from the origin (in the four-dimensional space "time-benefit-cost-risk"). The proposed model includes three stages. At the first stage, input information is collected and the corresponding analysis is initiated. The result of the second stage is a multivariate cash flow forecast and calculation of the benefit-cost ratio (BCR) and its changes for each scenario. The third stage provides the calculation of the expected BCR and its change, an assessment of the risk of making an erroneous decision and changing this risk as a result of the engineering session. The model makes it possible to calculate the achieved proportion of the static and dynamic vectors of change in the value of the project, which is one of the key manifestations of the scientific novelty of the work. In the example considered, the share of the dynamic vector of growth in the value of the project was found to be 35.47 %. The model has an environmental property - the assessment of the success of value engineering under conditions of uncertainty is carried out on the basis of the annual total benefits and the annual total costs throughout the project cycle. Thus, the analysis takes into account the impact of the project on the environment, which is reflected in the risk assessment. The given case testifies to the feasibility of applying the model in the practice of engineering the value of construction projects.

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Section: Tablementioning

confidence: 99%

Development of conceptual model of project value dynamics analysis in conditions of uncertainty

Bugrov

Бугрова

2021

EEJET

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“…Ghazal and Hammad [28] proposed a Knowledge Discovery in Databases (KDD) model, which may supplement the traditional estimation methods and provide more reliable final cost forecasting to overcome the cost overrun problem. In [29], the authors developed a method for estimating the impact of project management maturity (PMM) on project performance. The proposed method uses Bayesian networks to formalize the knowledge of project management experts and to extract knowledge from a database of previous projects.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptation of a Cost Overrun Risk Prediction Model to the Type of Construction Facility

Plebankiewicz

Wieczorek

2020

Symmetry

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To assess the risk of project cost overrun, it is necessary to consider large amounts of symmetric and asymmetric data. This paper proposes a cost overrun risk prediction model, the structure of which is based on the fuzzy inference model of Mamdani. The model consists of numerous inputs and one output (multi-input-single-output (MISO)), based on processes running consecutively in three blocks (the fuzzy block, the interference block, and the block of sharpening the representative output value). The input variables of the model include the share of element costs in the building costs (SE), predicted changes in the number of works (WC), and expected changes in the unit price (PC). For the input variable SE, it is proposed to adjust the fuzzy set shapes to the type of building object. Single-family residential buildings, multi-family residential buildings, office buildings, highways, expressways, and sports fields were analyzed. The initial variable is the value of the risk of exceeding the costs of a given element of a construction investment project (R). In all, 27 rules were assumed in the interference block. Considering the possibility of applying sharpening methods in the cost overrun risk prediction model, the following defuzzification methods were investigated: the first of maxima, middle of maxima, and last of maxima method, the center of gravity method, and the bisector area method. Considering the advantages and disadvantages, the authors assumed that the correct and basic defuzzification method in the cost overrun risk prediction model was the center of gravity method. In order to check the correctness of the assumption made at the stage of designing the rule database, result diagrams were generated for the relationships between the variable (R) and the input variables of individual types of buildings. The results obtained confirm the correctness of the assumed assumptions and allow to consider the input variable (SE), adjusted individually to the model for each type of construction object, as crucial in the context of the impact on the output value of the output variable (R).

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“…Bhattacharjee applied a Bayesian model that can handle death data analysis without stratifying in the presence of competing risks [21]. Sanchez et al develop a general framework and a method to estimate the impact of project management maturity on project performance by using Bayesian networks to formalize project management experts' knowledge [22]. Masmoudi et al used a discrete Bayesian network with a latent variable to model the payment default of loan subscribers [23].…”

Section: Related Workmentioning

confidence: 99%

A Bayesian Network under Strict Chain Model for Computing Flow Risks in Smart City

et al. 2020

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Risk management is a key factor for smart city running. There are many risk events in a strict process like transportation management of a smart city or a medical surgery in a smart hospital, and every step may lead to one kind of risk or more. In view of the fact that the occurrence of the flow risks follows the sequence formed by each process step, this paper presents a Bayesian network under strict chain (BN_SC) to model this situation. In this model, the probabilistic reasoning formula is given according to the sequence of process steps, and the probabilities given by the model can do risk factor analysis to support the system to find an effective way to improve the process like machine manufacturing or a medical surgery. Finally, an example is analyzed based on the information given by doctors according to the situation of LC in their hospital located in Sichuan Province of China, which shows the effectiveness and rationality of the proposed BN_SC model.

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An Approach Based on Bayesian Network for Improving Project Management Maturity: An Application to Reduce Cost Overrun Risks in Engineering Projects

Cited by 42 publications

References 47 publications

Development of conceptual model of project value dynamics analysis in conditions of uncertainty

Development of conceptual model of project value dynamics analysis in conditions of uncertainty

Adaptation of a Cost Overrun Risk Prediction Model to the Type of Construction Facility

A Bayesian Network under Strict Chain Model for Computing Flow Risks in Smart City

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