Predictive Cost-Risk Analytics and Construction Supply Chain Resilience for Saudi Giga-Projects: A Framework for Early Warning, Forecasting and Procurement Decision-Making

References

[1] Alnaser AA, Elmousalami H. Benefits and challenges of AI-based digital twin integration in the Saudi Arabian construction industry: a correspondence analysis (CA) approach. Applied Sciences. 2025;15:4675. https://doi.org/10.3390/app15094675

[2] Alshihri S, Al-Gahtani K, Almohsen A. Risk factors that lead to time and cost overruns of building projects in Saudi Arabia. Buildings. 2022;12:902. https://doi.org/10.3390/buildings12070902

[3] Alsugair AM. Cost deviation model of construction projects in Saudi Arabia using PLS-SEM. Sustainability. 2022;14:16391. https://doi.org/10.3390/su142416391

[4] Badawy M, Alqahtani F, Hafez H. Identifying the risk factors affecting the overall cost risk in residential projects at the early stage. Ain Shams Engineering Journal. 2022;13:101586. https://doi.org/10.1016/j.asej.2021.09.013

[5] Badawy M. A hybrid approach for a cost estimate of residential buildings in Egypt at the early stage. Asian Journal of Civil Engineering. 2020;21:763–774. https://doi.org/10.1007/s42107-020-00237-z

[6] Elmousalami HH. Comparison of artificial intelligence techniques for project conceptual cost prediction: a case study and comparative analysis. IEEE Transactions on Engineering Management. 2020;68:183–196. https://doi.org/10.1109/TEM.2020.2972078

[7] Sharma V, Zaki M, Jha KN, Krishnan NMA. Machine learning-aided cost prediction and optimization in construction operations. Engineering, Construction and Architectural Management. 2022;29:1241–1257. https://doi.org/10.1108/ECAM-10-2020-0778

[8] Liao L, Yang C, Quan L. Construction supply chain management: a systematic literature review and future development. Journal of Cleaner Production. 2023;382:135230. https://doi.org/10.1016/j.jclepro.2022.135230

[9] Chen Z, Hammad AWA. Mathematical modelling and simulation in construction supply chain management. Automation in Construction. 2023;156:105147. https://doi.org/10.1016/j.autcon.2023.105147

[10] Chen Z, Hammad AWA, Alyami M. Building construction supply chain resilience under supply and demand uncertainties. Automation in Construction. 2024;158:105190. https://doi.org/10.1016/j.autcon.2023.105190

[11] Bakhshi S, Ghaffarianhoseini A, Ghaffarianhoseini A, et al. Digital twin applications for overcoming construction supply chain challenges. Automation in Construction. 2024;167:105679. https://doi.org/10.1016/j.autcon.2024.105679

[12] Badkoubeh M, Ghannadpour SF. Designing a construction supply chain model using backup supplier aiming at optimizing resiliency against disruption. Journal of Civil Engineering and Management. 2024;30:614–631. https://doi.org/10.3846/jcem.2024.21450

[13] Ekanayake EMAC, Shen G, Kumaraswamy M, Owusu EK, Xue J. Capabilities to withstand vulnerabilities and boost resilience in industrialized construction supply chains: a Hong Kong study. Engineering, Construction and Architectural Management. 2022;29:3809–3829. https://doi.org/10.1108/ECAM-05-2021-0399

[14] Osunsanmi TO, Aigbavboa CO, Thwala WDD, Molusiwa R. Modelling Construction 4.0 as a vaccine for ensuring construction supply chain resilience amid COVID-19 pandemic. Journal of Engineering, Design and Technology. 2022;20:132–158. https://doi.org/10.1108/JEDT-07-2021-0384

[15] Al Azmi N, Sweis G, Sweis R, Sammour F. Exploring implementation of blockchain for the supply chain resilience and sustainability of the construction industry in Saudi Arabia. Sustainability. 2022;14:6427. https://doi.org/10.3390/su14116427

[16] Rasheed A, San O, Kvamsdal T. Digital twin: values, challenges and enablers from a modeling perspective. IEEE Access. 2020;8:21980–22012. https://doi.org/10.1109/ACCESS.2020.2970143

[17] Fuller A, Fan Z, Day C, Barlow C. Digital twin: enabling technologies, challenges and open research. IEEE Access. 2020;8:108952–108971. https://doi.org/10.1109/ACCESS.2020.2998358

[18] Shahzad M, Shafiq MT, Douglas D, Kassem M. Digital twins in built environments: an investigation of the characteristics, applications, and challenges. Buildings. 2022;12:120. https://doi.org/10.3390/buildings12020120

[19] Madubuike OC, Anumba CJ, Khallaf R. A review of digital twin applications in construction. Journal of Information Technology in Construction. 2022;27:145–172. https://doi.org/10.36680/j.itcon.2022.008

[20] Cheng JCP, Chen W, Chen K, Wang Q. Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Automation in Construction. 2020;112:103087. https://doi.org/10.1016/j.autcon.2020.103087

[21] Zio E, Miqueles L. Digital twins in safety analysis, risk assessment and emergency management. Reliability Engineering & System Safety. 2024;246:110040. https://doi.org/10.1016/j.ress.2024.110040

[22] Alnaser AA, Ali AH, Elmousalami HH, Elyamany A, Mohamed AG. Assessment framework for BIM-digital twin readiness in the construction industry. Buildings. 2024;14:268. https://doi.org/10.3390/buildings14010268

[23] Daniel EI, Oshodi OS, Nwankwo N, Emuze FA, Chinyio E. Barriers to the application of digital technologies in construction health and safety: a systematic review. Buildings. 2024;14:2386. https://doi.org/10.3390/buildings14082386

[24] Piras G, Muzi F, Tiburcio VA. Digital management methodology for building production optimization through digital twin and artificial intelligence integration. Buildings. 2024;14:2110. https://doi.org/10.3390/buildings14072110

[25] Omrany H, Al-Obaidi KM, Husain A, Ghaffarianhoseini A. Digital twins in the construction industry: a comprehensive review of current implementations, enabling technologies, and future directions. Sustainability. 2023;15:10908. https://doi.org/10.3390/su151410908

[26] Alnaser AA, Maxi M, Elmousalami H. AI-powered digital twins and Internet of Things for smart cities and sustainable building environment. Applied Sciences. 2024;14:12056. https://doi.org/10.3390/app142412056

[27] Serugga J. Digital twins and AI decision models: advancing cost modelling in off-site construction. Eng. 2025;6:22. https://doi.org/10.3390/eng6020022

[28] Yin L, Li T, Li G, Besklubova S, Zhong RY, Zhao S. Cyber-physical Internet based intelligent operation platform for off-site construction supply chain management. Enterprise Information Systems. 2024;18:2346743. https://doi.org/10.1080/17517575.2024.2346743

[29] Granacher J, Van Nguyen T, Castro-Amoedo R, Maréchal F. Overcoming decision paralysis – a digital twin for decision making in energy system design. Applied Energy. 2022;306:117954. https://doi.org/10.1016/j.apenergy.2021.117954

[30] Reja VK, Pradeep MS, Varghese K. Digital twins for construction project management (DT-CPM): applications and future research directions. Journal of the Institution of Engineers (India): Series A. 2024;105:793–807. https://doi.org/10.1007/s40030-024-00810-8

@article{1723994,
      author = {Amaan Ullah Sharif Mohammed},
      title = {Predictive Cost-Risk Analytics and Construction Supply Chain Resilience for Saudi Giga-Projects: A Framework for Early Warning, Forecasting and Procurement Decision-Making},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {4},
      pages = {2121-2133},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723994.pdf},
      abstract = {Saudi Arabia’s giga-project portfolio concentrates exceptional capital, schedule pressure, supplier interdependence and exposure to volatile markets within programmes expected to deliver strategic outcomes under Vision 2030. Conventional cost control and procurement reporting are necessary but often backward-looking: they describe variance after commitments, disruptions or productivity losses have already materialised. This review develops an evidence-informed framework for predictive cost-risk analytics and construction supply chain resilience that shifts management attention towards earlier signals, probabilistic forecasts and procurement interventions. Thirty peer-reviewed sources published from 2020 to 2025 were synthesised across construction cost prediction, cost-risk modelling, digital twins, construction supply chain management, resilience, robust optimisation and Saudi digital transformation. The evidence indicates that useful early warning requires more than a single machine-learning model. It depends on a governed data architecture that connects quantities, schedules, commitments, supplier performance, market indices, logistics events and site progress; combines statistical, machine-learning and scenario-based forecasts; and translates risk states into explicit procurement responses. Supply chain resilience is strengthened when visibility is paired with options such as back-up sourcing, inventory buffers, package resequencing, alternative logistics and contract mechanisms. Digital twins can improve the temporal connection between physical progress, cost exposure and supply conditions, but fragmented data, inconsistent coding, model drift and weak organisational response can undermine technical capability. The review proposes a six-layer Predictive Cost-Risk and Resilient Procurement Framework comprising data integration, baseline intelligence, predictive modelling, early-warning governance, procurement decision support and outcome learning. For Saudi giga-projects, the framework supports earlier intervention, more defensible contingency allocation and more resilient procurement decisions without displacing professional judgement.},
      keywords = {predictive analytics; construction cost risk; supply chain resilience; giga-projects; procurement; digital twins; machine learning; Saudi Arabia; Vision 2030},
      month = {October},
  }