Enhancing Supply Chain Resilience through AI-Driven Disruption Mitigation in Off-Site Construction Logistics Operations
Doctoral Training Grant Funding Information
This funding model includes a 36 month fully funded PhD Studentship, set in-line with UK Research & Innovation values. For 2025/6, this will be £20,780 per year. The tax-free stipend will be paid monthly. This PhD Studentship also includes a Full-Time Fee Scholarship for up to 3 years. The funding is subject to your continued registration on the research degree, making satisfactory progression within your PhD, as well as attendance on and successful completion of the Postgraduate Certificate in Research Practice.
All applicants will receive the same stipend irrespective of fee status.
Application Closing Date:
Midday (UK Time) on Wednesday 17th September 2025 for a start date of 2nd February 2026.
How to Apply
To apply, please follow the below steps:
- Complete the BCU Online Application Form
- Complete the Doctoral Studentship Proposal Form in full, ensuring that you quote the project ID. You will be required to upload your proposal in place of a personal statement on the BCU online application form.
- Upload two references to your online application form (at least one of which must be an academic reference).
- Upload your qualification(s) for entry onto the research degree programme. This will be Bachelor/Master’s certificate(s) and transcript(s).
- International applicants must also provide a valid English language qualification. Please see the list of English language qualifications accepted here. Please check the individual research degree course page for the required scores.
Frequently Asked Questions
To help support you to complete your application, please consult the frequently asked questions below:
Project title: Enhancing Supply Chain Resilience through AI-Driven Disruption Mitigation in Off-Site Construction Logistics Operations
Project Lead: Dr Anushika Ekanayake Mudiyanselage
Project ID: 01 - 46456572
Project description:
This research project aims to develop an AI (Artificial Intelligence) driven disruption mitigation model to enhance the logistics operations within the off-site construction (OSC) supply chains targeting supply chain resilience. The construction industry is increasingly adopting Modern Methods of Construction (MMC), including modular and prefabricated systems, in response to the pressing demand for faster, more efficient, and sustainable project delivery (Montazeri et al., 2024). However, as the complexity of these construction supply chains grows, so does their vulnerability, particularly during the logistics phase, which plays a critical role in ensuring that prefabricated components arrive at the right place, at the right time, and in the right condition (Ekanayake et al., 2024; Wu et al., 2018).
Logistics in OSC is often considered the “invisible backbone” of the supply chain. Unlike traditional construction, where materials can often be stored on-site, OSC relies on just-in-time (JIT) deliveries, tighter sequencing, and co-ordination across multiple dispersed stakeholders (Wuni et al., 2023). A single disruption such as vehicle breakdowns, miscommunication, port congestion, or unforeseen weather conditions can cause cascading delays across the entire project, affecting productivity, cost, and quality (Ekanayake et al., 2021; Lee and Kim, 2017). Despite these challenges, disruption forecasting in the logistics phase remains underexplored in current research and practice.
This study addresses that gap by integrating AI techniques, specifically Long Short-Term Memory (LSTM) neural networks into disruption forecasting and mitigation strategies. LSTM models are well-suited for analysing time-series and sequential data, making them ideal for modelling dynamic logistics operations where delays and performance trends unfold over time (Zhao et al., 2022). By learning temporal dependencies from historical logistics data, LSTM algorithms can predict future disruption risks, enabling proactive decision-making (Zohal & Soleimani, 2023). A mixed-methods approach will be adopted in data collection, combining case study investigations with empirical AI modelling using real-world logistics datasets from OSC projects.
The research will follow four main objectives:
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To identify the critical vulnerabilities and sources of disruption affecting logistics operations in off-site construction supply chains
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To investigate the application of AI techniques for disruption assessment, prediction and mitigation in construction logistics
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To develop and validate an AI-Driven model to proactively manage disruptions and enhance the logistics operations in off-site construction supply chains
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To evaluate the impact of identified disruptions on the resilience and continuity of supply chain logistics operations in off-site construction projects with the use of developed model
The construction industry stands to benefit significantly from this research. By focusing on the logistics phase, the study directly targets one of the most vulnerable and least digitised stages in the OSC supply chain. The proposed model supports government ambitions for digital transformation, increased productivity, and sustainable infrastructure delivery under initiatives like the Construction Playbook and the Industrial Strategy.
For industry, the outcomes include enhanced ability to forecast and pre-empt disruptions, improved supply chain coordination, reduction of costly delays, and increased confidence in adopting OSC at scale. The use of explainable AI models like LSTM further allows practitioners to understand the rationale behind disruption forecasts, supporting greater trust and adoption. Ultimately, this project will provide the construction industry with a timely, intelligent, and practical tool to boost the reliability and efficiency of its future-ready, modular-based construction delivery systems.
Anticipated findings and contributions to knowledge:
This research is expected to generate a novel, data-driven model using Long Short-Term Memory (LSTM) neural networks to predict and mitigate logistics disruptions in OSC supply chains. The anticipated findings will highlight critical patterns of disruption across the logistics phase such as delivery delays, transport network vulnerabilities, and scheduling conflicts rooted in real-time and historical project data.
By mapping these insights, the study will contribute a predictive disruption mitigation framework that dynamically supports decision-making for supply chain and project managers. Unlike static risk registers or contingency planning approaches, the proposed model will evolve with ongoing data input and adapt to changing site and supply conditions. The model will be validated through real-world industry case studies, ensuring practical applicability and transferability across diverse OSC settings.
This research fills a significant gap in the construction literature, where disruption mitigation in logistics especially using LSTM-based models remains underexplored. The outcomes will be particularly valuable as the UK even accelerates MMC adoption in response to labour shortages, housing demands, and sustainability commitments.
Person Specification:
We are seeking an enthusiastic and self-motivated candidate to undertake a PhD as part of a research project focused on developing an AI-driven disruption mitigation model to enhance logistics operations in off-site construction (OSC) supply chains.
The ideal applicant will have a strong academic background in a relevant discipline, a keen interest in interdisciplinary research, and a desire to contribute to innovation in the built environment through the application of artificial intelligence and data analytics.
Essential Criteria:
Academic Qualifications:
Applicants should hold a minimum of a First-Class undergraduate degree in a relevant subject such as Construction Management, Civil Engineering, Logistics and Supply Chain Management, Computer Science, Data Science, or a closely aligned field.
A relevant Master’s degree is desirable but not essential.
Knowledge and Research Aptitude:
Demonstrable interest in one or more of the following areas: modern methods of construction (MMC), construction logistics, supply chain resilience, or artificial intelligence in the built environment.
Evidence of research aptitude through a dissertation, undergraduate research project, or related academic work.
Technical Skills:
Basic proficiency in data analysis and familiarity with programming tools (e.g., Python, R, MATLAB) used in machine learning or data modelling.
Willingness to further develop skills in AI techniques such as Long Short-Term Memory (LSTM) neural networks and time-series modelling.
Communication and Teamworking Skills:
Strong written and verbal communication skills with the ability to work both independently and collaboratively as part of a research team.
An ability to engage with academic supervisors and industry stakeholders effectively.
Motivation and Commitment:
A clear motivation for pursuing a PhD and a commitment to contributing to academic knowledge and practical industry impact in the field of digital construction and supply chain resilience.
Desirable Criteria:
Industry or Practical Experience:
Any experience in the construction industry, particularly in areas related to project logistics, off-site delivery coordination, or digital construction tools, will be an advantage.
Familiarity with Research Methods:
Awareness of qualitative and/or quantitative research approaches, with the ability to engage in case study-based data collection and analysis.
Interest in Applied AI:
Enthusiasm for the application of AI and data-driven solutions to address real-world problems in the built environment, particularly in forecasting and mitigating disruptions.
We welcome applications from candidates with diverse academic and professional backgrounds who are keen to explore innovative, interdisciplinary research that has real-world relevance and impact.
Overseas applicants:
International applicants must also provide a valid English language qualification, such as International English Language Test System (IELTS) or equivalent with an overall score of 6.5 with no band below 6.0.
Contact:
If you have any questions or need further information, please use the contact details below:
- For enquiries about the funding or project proposal, please contact:Anushika.mudiyanselage@bcu.ac.uk.
- For enquiries about the application process, please contact: research.admissions@bcu.ac.uk