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Model for Early Risk Identification to Enhance Cost and Schedule Performance in Construction Projects
Subject area: Science,Engineering and Technology · Area of research: Early Risk Identification Model
Abstract
Early risk identification plays a pivotal role in enhancing cost and schedule performance in construction projects, ensuring timely completion and financial stability. Construction projects are inherently complex, involving multiple stakeholders, dynamic environmental conditions, and fluctuating resource availability. Traditional risk management approaches often focus on reactive mitigation strategies, addressing issues only after they arise. However, a proactive model for early risk identification can significantly improve project outcomes by anticipating potential disruptions and implementing preventive measures. This paper presents a structured framework for integrating early risk identification methodologies into construction project planning, emphasizing predictive analytics, real-time monitoring, and data-driven decision-making. By leveraging advanced risk assessment tools, such as artificial intelligence, machine learning algorithms, and historical project data, construction managers can detect vulnerabilities before they escalate into costly delays or budget overruns. The model incorporates key risk factors, including financial uncertainties, labor shortages, material procurement challenges, and regulatory compliance issues, ensuring a comprehensive approach to risk mitigation. Additionally, the study explores the role of stakeholder collaboration and transparent communication in fostering a risk-aware project environment. Through an analysis of case studies and industry benchmarks, this research highlights best practices for integrating early risk identification into construction workflows. The findings aim to provide a strategic roadmap for project managers, engineers, and policymakers seeking to optimize cost efficiency and schedule adherence in construction projects. By adopting a proactive risk management framework, the industry can enhance resilience, minimize disruptions, and achieve sustainable project success.
Keywords
Risk identification, cost performance, schedule optimization, construction management, predictive analytics, proactive risk mitigation, project resilience
How to cite this paper
@article{1708974,
author = {Sidney Eronmonsele Okiye, Tochi Chimaobi Ohakawa, Zamathula Sikhakhane Nwokediegwu},
title = {Model for Early Risk Identification to Enhance Cost and Schedule Performance in Construction Projects},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {5},
number = {11},
pages = {394-419},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1708974.pdf},
abstract = {Early risk identification plays a pivotal role in enhancing cost and schedule performance in construction projects, ensuring timely completion and financial stability. Construction projects are inherently complex, involving multiple stakeholders, dynamic environmental conditions, and fluctuating resource availability. Traditional risk management approaches often focus on reactive mitigation strategies, addressing issues only after they arise. However, a proactive model for early risk identification can significantly improve project outcomes by anticipating potential disruptions and implementing preventive measures. This paper presents a structured framework for integrating early risk identification methodologies into construction project planning, emphasizing predictive analytics, real-time monitoring, and data-driven decision-making. By leveraging advanced risk assessment tools, such as artificial intelligence, machine learning algorithms, and historical project data, construction managers can detect vulnerabilities before they escalate into costly delays or budget overruns. The model incorporates key risk factors, including financial uncertainties, labor shortages, material procurement challenges, and regulatory compliance issues, ensuring a comprehensive approach to risk mitigation. Additionally, the study explores the role of stakeholder collaboration and transparent communication in fostering a risk-aware project environment. Through an analysis of case studies and industry benchmarks, this research highlights best practices for integrating early risk identification into construction workflows. The findings aim to provide a strategic roadmap for project managers, engineers, and policymakers seeking to optimize cost efficiency and schedule adherence in construction projects. By adopting a proactive risk management framework, the industry can enhance resilience, minimize disruptions, and achieve sustainable project success.},
keywords = {Risk identification, cost performance, schedule optimization, construction management, predictive analytics, proactive risk mitigation, project resilience},
month = {May},
}