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Construction supply chain risk management.

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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]
Copyright of Automation in Construction is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 176865519
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Construction supply chain risk management.
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  Data: 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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  Data: <i>Copyright of Automation in Construction is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1016/j.autcon.2024.105396
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      – Code: eng
        Text: English
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      – SubjectFull: Construction project management
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      – SubjectFull: Bibliometrics
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      – SubjectFull: Technological innovations
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      – SubjectFull: Artificial intelligence
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      – TitleFull: Construction supply chain risk management.
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            NameFull: Baghalzadeh Shishehgarkhaneh, Milad
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            NameFull: Fang, Yihai
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            – D: 01
              M: 06
              Text: Jun2024
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              Y: 2024
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              Value: 162
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