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A machine learning approach for forecasting resilient material delivery in the construction industry.

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Title: A machine learning approach for forecasting resilient material delivery in the construction industry.
Authors: Zhang, Wennan1 (AUTHOR), Yu, Chenglin1 (AUTHOR), Newman, Stephen T.2 (AUTHOR), Cheung Ho Kin, Dennis3 (AUTHOR), Zhong, Ray Y.1 (AUTHOR) zhongzry@hku.hk
Source: International Journal of Production Research. Jul2026, Vol. 64 Issue 13, p5270-5289. 20p.
Subjects: Hidden Markov models, Supply chain management, Machine learning, Construction industry, Construction industry forecasting, Risk management in business, Statistical decision making, Digital twin
Abstract: In the construction industry, the unpredictable delivery of materials together with the delivery of incorrect materials is a frequent occurrence. This leads to supply chain disruptions and failure to align with other stakeholders. Contractors aim to develop a resilient supply chain by mitigating these risks regarding delivery and controlling costs by being kept well informed of potential delays. In this paper, the authors propose a novel digital twin-enabled model for mapping delivery networks and monitoring supply chain risks. The contingency and disruption amid the construction supply chain provide indisputable evidence for the need for digital twin technology to ensure visibility. The collected data shows the relationship between materials and corresponding machines (MCM) via a hidden transition and usage plan. The paper presents a new improved Hidden Markov Model (HMM) approach entitled the Multi-Material & Machine Hidden Markov Model (M3HMM), as it considers the relationships of usage plans for MCM to forecast delivery and ensure supply chain resilience. The results show that with respect to resilience, M3HMM consistently performs the best compared with other algorithms, including LR, SVR, GMM, LSTM and HMM. It also ultimately enables industrial contractors to enhance their predictive and reactive decision-making, reducing the risk. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd 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.)
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  Data: A machine learning approach for forecasting resilient material delivery in the construction industry.
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  Data: <searchLink fieldCode="DE" term="%22Hidden+Markov+models%22">Hidden Markov models</searchLink><br /><searchLink fieldCode="DE" term="%22Supply+chain+management%22">Supply chain management</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Construction+industry%22">Construction industry</searchLink><br /><searchLink fieldCode="DE" term="%22Construction+industry+forecasting%22">Construction industry forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+management+in+business%22">Risk management in business</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+decision+making%22">Statistical decision making</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink>
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  Data: In the construction industry, the unpredictable delivery of materials together with the delivery of incorrect materials is a frequent occurrence. This leads to supply chain disruptions and failure to align with other stakeholders. Contractors aim to develop a resilient supply chain by mitigating these risks regarding delivery and controlling costs by being kept well informed of potential delays. In this paper, the authors propose a novel digital twin-enabled model for mapping delivery networks and monitoring supply chain risks. The contingency and disruption amid the construction supply chain provide indisputable evidence for the need for digital twin technology to ensure visibility. The collected data shows the relationship between materials and corresponding machines (MCM) via a hidden transition and usage plan. The paper presents a new improved Hidden Markov Model (HMM) approach entitled the Multi-Material & Machine Hidden Markov Model (M3HMM), as it considers the relationships of usage plans for MCM to forecast delivery and ensure supply chain resilience. The results show that with respect to resilience, M3HMM consistently performs the best compared with other algorithms, including LR, SVR, GMM, LSTM and HMM. It also ultimately enables industrial contractors to enhance their predictive and reactive decision-making, reducing the risk. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd 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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        Value: 10.1080/00207543.2025.2482733
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      – Code: eng
        Text: English
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        PageCount: 20
        StartPage: 5270
    Subjects:
      – SubjectFull: Hidden Markov models
        Type: general
      – SubjectFull: Supply chain management
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Construction industry
        Type: general
      – SubjectFull: Construction industry forecasting
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      – SubjectFull: Risk management in business
        Type: general
      – SubjectFull: Statistical decision making
        Type: general
      – SubjectFull: Digital twin
        Type: general
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      – TitleFull: A machine learning approach for forecasting resilient material delivery in the construction industry.
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            NameFull: Zhang, Wennan
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            NameFull: Yu, Chenglin
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            NameFull: Newman, Stephen T.
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            – D: 01
              M: 07
              Text: Jul2026
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              Y: 2026
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