Bibliographic Details
| Title: |
Construction supply chain risk management. |
| Authors: |
Baghalzadeh Shishehgarkhaneh, Milad1 (AUTHOR), Moehler, Robert C.1,2 (AUTHOR) robert.moehler@unimelb.edu.au, Fang, Yihai1 (AUTHOR), Aboutorab, Hamed3 (AUTHOR), Hijazi, Amer A.4 (AUTHOR) |
| Source: |
Automation in Construction. Jun2024, Vol. 162, pN.PAG-N.PAG. 1p. |
| Subjects: |
Supply chain management, Construction project management, Bibliometrics, Technological innovations, Artificial intelligence |
| Abstract: |
Risk management in construction projects requires effective construction supply chain risk management (CSCRM). To gain insights into CSCRM research, this paper conducts a systematic literature review and bibliometric analysis covering the period from 1999 to 2023. The findings of this comprehensive analysis shed light on various aspects, including risk management phases, classification of micro or macrolevel risks, traditional approaches, and the emergence of artificial intelligence (AI) applications. Through an extensive database search, relevant articles on CSCRM were identified for analysis. The review reveals that while traditional techniques such as surveys, case studies, and statistical tools remain prominent, there is an increasing adoption of AI methods. Initially focused on risk identification, assessment, and analysis; the CSCRM phases have expanded over time to include risk allocation, prioritization, and recovery. Analysis of publication trends shows a rise in the use of AI techniques since 2016 alongside persistent utilization of traditional approaches. Moreover, influential authors, journals, and collaborative networks are highlighted to provide valuable insights into the field's development. Overall visualization contributes to advancing both research and practice in CSCRM by presenting a holistic overview of theories, methods, and emerging technologies within the field along with critical risk management approaches and publication trends. [Display omitted] • Systematic analysis of CSCRM research from 1999 to 2023. • Insights on risk management phases and risk classifications. • Traditional vs. AI approaches in CSCRM. • Identification of influential authors, journals, and networks. • Evolving trends: Increased AI adoption in CSCRM research. [ABSTRACT FROM AUTHOR] |
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| Database: |
Engineering Source |
