Big Data-Driven Implementation in International Construction Supply Chain Management: Framework Development, Future Directions, and Barriers
Abstract
1. Introduction
- (1)
- Data-related barriers
- (2)
- Technology-related barriers
- (3)
- Organizational and human resource barriers
- (4)
- Regulatory barriers
- (5)
- Economic and business barriers
- Comprehensive identification and classification of barriers through 62 rigorously screened studies from 2014 to 2024 using PRISMA 2020.
- An implementation framework tailored to construction projects, offering a practical roadmap for embedding BDA across project-based supply chain segments.
- A new barrier taxonomy of 21 specific obstacles grouped into five dimensions (data, technology, organizational and human, regulatory, economic) as well as targeted mitigation strategies for each.
- A forward-looking research agenda highlighting gaps in data governance, scalable infrastructure, real-time performance systems, skill development, and supportive policy frameworks, thereby steering future BD-CSCM investigations.
2. Research Methods
2.1. Review Methodology
2.2. Trends and Thematic Evolution in Big Data-Driven Supply Chain Research
2.3. A Review Database Bibliometric Analysis
2.3.1. Analysis of Countries’ Contribution
2.3.2. Analysis of Journals’ Contribution
2.3.3. Analysis of Keywords
3. Results
3.1. The Framework for Implementing BD for Construction Supply Chain
3.2. Barriers Towards BD for Construction Supply Chain
3.2.1. Data-Related Barriers
3.2.2. Technology-Related Barriers
3.2.3. Organizational and Human Resource Barriers
3.2.4. Regulatory-Related Barriers
3.2.5. Economic-Related Barriers
4. The Applications of Big Data-Driven in Supply Chain Management
4.1. Risk Management and Resilience
4.2. Supplier Management
4.3. Operations Optimization
4.4. Sustainability
4.5. Logistics and Transportation
5. Research Gaps and Future Directions
5.1. Research Gaps
5.1.1. Data-Related Limitations
5.1.2. Technology-Related Limitations
5.1.3. Organizational and Human-Related Limitations
5.1.4. Regulatory-Related Limitations
5.1.5. Economic-Related Limitations
5.2. Potential Research Directions
5.2.1. Data Security, Privacy, and Quality
5.2.2. Scalable Infrastructure and Tools for CSCM
5.2.3. Advancing Performance Measurement Systems
5.2.4. Organizational Transformation and Skill Development
5.2.5. Government Policies and Regulatory Frameworks
5.2.6. Economic Feasibility
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AI | Artificial intelligence |
BD | Big data |
BDA | Big data analysis |
BDAC | Big data analysis capabilities |
BDD | Big data-driven |
BI | Business intelligence |
BIM | Building information modeling |
CSCM | Construction supply chain management |
CSCMP | Supply chain management professionals |
DQAC | Data quality assessing and controlling |
EIDP | Enterprise integrated data platform |
GI | Green innovation |
GSCI | Green supply chain integration |
GSCM | Green supply chain management |
HDFS | Hadoop distributed file system |
IoT | Internet of Things |
IPD | Integrated project delivery |
IPFS | Interplanetary file system |
IT | Information technology |
KPIS | Key performance indices |
PRISMA | Preferred reporting items for systematic reviews and meta-analyses |
RFID | Radio-frequency identification |
SCA | Supply chain analytics |
SCM | Supply chain management |
SMES | Small- and medium-sized enterprises |
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Ref. | Authors | Journal | Year | Citations |
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[20] | Rafael Sacks, Ioannis Brilakis, Ergo Pikas, Haiyan Sally Xie, and Mark Girolami | Data-Centric Engineering | 2020 | 340 |
[21] | Junhu Ruan, Yuxuan Wang, Felix Tung Sun Chan, Xiangpei Hu, Minjuan Zhao, Fangwei Zhu, Baofeng Shi, Yan Shi, and Fan Lin | IEEE Communications Magazine | 2019 | 163 |
[22] | Jannik Giesekam, John R. Barrett, and Peter Taylor | Building Research and Information | 2016 | 159 |
[23] | Xiao Li, Weisheng Lu, Fan Xue, Liupengfei Wu, Rui Zhao, Jinfeng Lou, and Jinying Xu | Journal of Construction Engineering and Management | 2022 | 142 |
[24] | Zhijia You and Chen Wu | Advanced Engineering Informatics | 2019 | 77 |
[25] | Zhaojing Wang, Hao Hu, and Wei Zhou | Computer-Aided Civil and Infrastructure Engineering | 2017 | 71 |
[26] | Zehua Xiang and Minli Xu | Journal of Cleaner Production | 2019 | 70 |
[27] | Qian Chen, Bryan T. Adey, Carl Haas, and Daniel M. Hall | Construction Innovation | 2020 | 69 |
[28] | Hisham Said | Journal of Construction Engineering and Management | 2015 | 53 |
[29] | Vian Ahmed, Algan Tezel, Zeeshan Aziz, and Magda Sibley | Facilities | 2020 | 51 |
Barriers Category | Barriers |
---|---|
Data barriers | 1. Data security and privacy; |
2. Performance and scalability; | |
3. Complexity of data integration; | |
4. Data collection and sharing; | |
5. Data quality; | |
Technology barriers | 6. Lack of infrastructural for storing and transferring big data; |
7. Lack of availability of specific BDD tools; | |
8. Lack of performance measurements for evaluating BDD in companies; | |
Organizational and human barriers | 9. Lack of planning to develop strategies towards using big data-driven; |
10. Time constraints; | |
11. No policy to share data among organizations; | |
12. Weakness of data-driven decision-making in organizations; | |
13. Lack of training facilities; | |
14. Lack of skilled personnel; | |
15. Weak support of managers in implementing new technologies; | |
Regulatory barriers | 16. Weakness of governance policies in support of BD development; |
17. Weakness of government policies for new Technology Adoption; | |
18. Weak laws in the areas of privacy, data security and intellectual property; | |
Economic barriers | 19. High cost of investment; |
20. Lack of sufficient knowledge about the economic benefits of big data-driven; | |
21. Weak compliance of business models with the subject of data-driven. |
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© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Elkliny, A.; Mahmoudi, A.; Deng, X. Big Data-Driven Implementation in International Construction Supply Chain Management: Framework Development, Future Directions, and Barriers. Buildings 2025, 15, 2167. https://doi.org/10.3390/buildings15132167
Elkliny A, Mahmoudi A, Deng X. Big Data-Driven Implementation in International Construction Supply Chain Management: Framework Development, Future Directions, and Barriers. Buildings. 2025; 15(13):2167. https://doi.org/10.3390/buildings15132167
Chicago/Turabian StyleElkliny, Ali, Amin Mahmoudi, and Xiaopeng Deng. 2025. "Big Data-Driven Implementation in International Construction Supply Chain Management: Framework Development, Future Directions, and Barriers" Buildings 15, no. 13: 2167. https://doi.org/10.3390/buildings15132167
APA StyleElkliny, A., Mahmoudi, A., & Deng, X. (2025). Big Data-Driven Implementation in International Construction Supply Chain Management: Framework Development, Future Directions, and Barriers. Buildings, 15(13), 2167. https://doi.org/10.3390/buildings15132167