Influence of cost contingency management on project estimates at completion

Marco, Alberto De; Narbaev, Timur; Ottaviani, Filippo Maria; Vanhoucke, Mario

doi:10.1080/15623599.2023.2239487

Cited by 3 publications

(2 citation statements)

References 53 publications

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“…Specifically, the studies calculate duration and cost estimates at completion by fitting theoretical models to the EV and AC data, respectively, and projecting the resulting models forward. The method was tested in both cost forecasting [33,34] and duration forecasting [35,36].…”

Section: Other Forecasting Methodsmentioning

confidence: 99%

Improving Project Estimates at Completion through Progress-Based Performance Factors

Ottaviani,

De Marco,

Narbaev

et al. 2024

Buildings

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From a managerial perspective, project success hinges on estimates at completion as they allow tailoring response actions to cost and schedule overruns. While the literature is moving towards sophisticated approaches, standard methodologies, such as Earned-Value Management (EVM) and Earned Schedule (ES), are barely implemented in certain contexts. Therefore, it is necessary to improve performance forecasting without increasing its difficulty. The objective of this study was twofold. First, to guide modeling and implementing project progress within cost and to schedule Performance Factors (PFs). Second, to test several PFs utilized within EVM and ES formulae to forecast project cost and duration at completion. Progress indicators dynamically adjust the evaluation approach, shifting from neutral to conservative as the project progresses, either physically or temporally. This study compared the performance of the progress-based PFs against EVM and ES standard, combined, and average-based PFs on a dataset of 65 real construction projects, in both cost and duration forecasting. The results show that progress-based PFs provide more accurate, precise, and timely forecasts than other PFs. This study allows practitioners to select one or more of the proposed PFs, or even to develop one, following the guidelines provided, to reflect best their assumptions about the future course of project performance.

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Section: Other Forecasting Methodsmentioning

confidence: 99%

Improving Project Estimates at Completion through Progress-Based Performance Factors

Ottaviani,

De Marco,

Narbaev

et al. 2024

Buildings

Self Cite

View full text Add to dashboard Cite

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“…Their findings suggest that the CM strategy should be based on conflicting pressures to make preventive, risk-mitigating improvements or release the remaining CR as savings. The model was later improved by De Marco et al [41] and applied to improve project cost estimates at completion, highlighting how the project CR expenditure behavior influences cost forecasts. Ayub et al [42] developed a mathematical model that integrated project KPIs with future risk perception, enabling a quantitative approach to informal and subjective risk models.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Perception-Based Project Contingency Management Framework

Ottaviani,

De Marco,

Rafele

et al. 2024

Systems

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Project risk management (PRM) involves identifying risks, assessing their impact, and developing a contingency plan. A structured contingency management (CM) approach prevents subjective biases in analyzing risks and developing responses. Previous studies have either focused on improving the accuracy of risk estimates or analyzed, from a qualitative perspective, the relationships between perceived risk and project performance. This study aimed to improve PRM by providing a risk-perception-based contingency management framework (CMF). The CMF guides contingency depletion based on two short- and long-term cost overrun indicators and their respective thresholds. Thresholds and the initial contingency reserve amount are determined by applying the Monte Carlo method to a stochastic, discrete-event, finite-horizon, dynamic project simulation model. The study developed the CMF through a structured approach, validating the simulation model on eight specific project configurations. The results prove that the framework can be applied to any project, shaping the risk response strategy. This study contributes to PRM by explaining the relationships between risk perception and risk responses and providing a prescriptive CM tool.

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Assessing the impact of artificial intelligence on project efficiency enhancement

Kozhakhmetova,

Mamyrbayev,

Zhidebekkyzy

et al. 2024

Knowledge and Performance Management

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The study explores the impact of artificial intelligence (AI) technologies on project management (PM) across different industries. It aims to assess how AI adoption in PM affects project efficiency. The study surveyed 159 project supervisors and specific project managers implementing projects from 7 industries in the Republic of Kazakhstan: software, green energy, engineering, construction, science, transport, and tourism. The research used variance and linear regression analyses to evaluate the relationship between AI adoption and project efficiency level measured by the Likert scale from 1 to 5 and test the associated hypotheses. The results show that AI adoption varies among industries, with software, construction, and scientific projects being the most active users. The study also found that the use of AI differed across eight project performance domains, with the stakeholder domain using voice technologies and process automation and the uncertainty domain using fewer tools. Projects with higher AI adoption rates showed higher efficiency scores (for example, in Software projects, the AI adoption rate is 3.2; the efficiency rate is 3.3), while those with lower efficiency levels (for example, in the Tourism industry, the AI adoption rate is 1.9; the efficiency rate is 2.2) showed the worst results. Decision-making systems, process automation, and voice technologies are the three most critical AI technologies PM professionals use to improve project efficiency. Acknowledgments This research has been funded by the Committee of Science of the Ministry of Science and Higher Education of the Republic of Kazakhstan (Grant No. AP19680313).

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Influence of cost contingency management on project estimates at completion

Cited by 3 publications

References 53 publications

Improving Project Estimates at Completion through Progress-Based Performance Factors

Improving Project Estimates at Completion through Progress-Based Performance Factors

Risk Perception-Based Project Contingency Management Framework

Assessing the impact of artificial intelligence on project efficiency enhancement

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