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Developing a Value-Risk Management Model for Construction Projects

delete2024-01-01
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PRE
AI
M
Mei‐yung Leung
韦潇旖 封面图
韦潇旖 (Xiaoyi Wei)
L
Lekan Damilola Ojo *
DOI:10.1061/JCEMD4.COENG-13396delete
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摘要

摘要

En 中文
Value management (VM) and risk management (RM) both involve systematic team decision-making methodologies, but they are often used separately by construction professionals in real practices for different purposes. VM emphases the best project value, while RM is expected to minimize the project risks. The major difference between the two methodologies may lie in the function analysis phase of VM for identifying key functions of projects, and the risk analysis of RM for risk identification. Due to the complementary nature of the techniques involved in both forms of management, the integration of VM and RM has been advocated by scholars for about three decades to optimize the final outcome within limited time and resources. However, little attention has been paid to establishing the complex relationships between value-risk management (VRM) techniques and their outcomes. Therefore, this study aims to find an effective way to integrate VM and RM for enhancing final outcomes. A questionnaire survey was conducted to construction professionals with experience in VM and/or RM. Through various statistical analyses, including factor analysis, correlation, and multiple regression analysis, the findings indicate that (1) information and creativity techniques aided development techniques significantly; (2) presentation techniques are determined by information and function analysis techniques; (3) information techniques, risk analysis techniques, and development techniques enhance team and project performance in VRM workshops; and (4) function analysis techniques, risk analysis techniques, and development techniques can improve overall satisfaction on construction projects. To ensure risk minimization and value maximization in construction projects, VRM workshops should be harnessed and undertaken systematically and logically. Practical recommendations regarding sufficient information and discussion time for VRM participants are given to ensure stakeholders gain the comprehensive understanding of the peculiarity of the project at hand necessary to propel creative ideas and achieve the objectives of the workshop. In the real construction industry, it is time consuming and costly for project practitioners to conduct value management for enhancing project value and risk management to minimize impending risks separately. In this research, value management and risk management techniques were integrated in team decision-making process (value-risk management process) to help project practitioners make the best decisions with fewer risks logically for achieving the best project value. For enhancing final outcomes such as reducing project cost, facilitating team cooperation, and improving overall satisfaction, several practical recommendations are proposed for project practitioners to conduct the value-risk management process effectively: for example, allocating sufficient time for VRM practitioners to share and review information and identify possible risks, conducting function analysis training to the VRM practitioners, using risk analysis software during the VRM process, and requiring VRM practitioners work together to develop a comprehensive final report. The VRM participants need to use function analysis techniques to identify the key functions for value enhancement. The VRM workshop facilitator must be competent in all the techniques for applications on the project. In addition, the report should contain sufficient justifications such as calculations, possible cost-saving, advantages, and disadvantages of final ideas agreed upon by the VRM participants.
Keyword:
Decision-making
Risk management
Value management
Project performance
Teamwork
Satisfaction

期刊

J
Journal of Construction Engineering and Management
IF:
5.1
论文数:
5.1K
被引数:
1.4W

机构

G
guangxi university of finance & economics
学者数:
253
论文数: 234
被引数: 0
C
City University of Hong Kong
学者数:
2.3W
论文数: 3.0W
被引数: 6.1W
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