the Futureof Freight: Foresight for Smarter, Greener, and More Resilient Supply Chains Organisations are turning to AI to create smarter, faster, greener, and more resilient freight and logistics networks. This PhD project will use foresight methods such as scenario planning, Delphi studies, and horizon scanning to examine the future implications of generative, predictive, agentic, causal, or frontier AI for freight operations, integrated logistics infrastructure, resilience building, and the optimisation of operational effectiveness across future supply chains. Projects in this context could develop a strategic foresight and decision-support framework for AI-enabled freight and logistics systems, with relevance to the UK and Scotland, other alternatives are also possible. Proposals should critically explore how emerging AI capabilities could reshape the planning, governance, and performance of supply chains in the context of decarbonisation, digital transformation, and increasing infrastructure and resilience challenges. Innovative proposals are highly encouraged. This research is especially timely given the UK Government’s commitment to accelerating AI adoption through the AI Opportunities Action Plan and its progress update, alongside wider policy commitments to transport decarbonisation and freight transition (Department for Science, Innovation and Technology [DSIT], 2025, 2026; Department for Transport [DfT], 2021). What to expect This programme is ideal for someone who wants to explore innovative trend analyses, predictive analytics, progression modelling, foresight mixed-methods, bibliometric analysis, qualitative comparative analysis, ML, and more. There is scope to develop your own focus and to link into targeted and relevant UN Sustainable Development Goals. We welcome targeted, focused, and well-developed research proposals that address this evolving area. Applicants should demonstrate a clear understanding of the role of AI in shaping our plausible futures and shouldset out a coherent andfeasible plan for investigating thistopic within theUK and/or Scottish context. Proposals should be detailed enough to show originality, methodological rigour, and clear alignment with the project’s themes. Willing to accept full-time and part-time applicants. Publications This programme emphasizes sharing your discoveries through publications (e.g.academic journals, books and conferences) and with non-academic audiences (e.g. industry events, podcasts, and media). Science communication is a core value of the University, and your supervisors and department will support you through these outputs. Academic qualifications A firstdegree (at leasta 2.1) ideallyin behavioural sciences, supply management, or related, witha good fundamental knowledge of statistical analyses, supply chain systems, logistics and secondary data research methods. English language requirement IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online. Essential attributes: Experience of fundamental quantitative research methods, human-based testing, secondary data methods, organisational behaviour theory, LLM, NLP, or related machine learning context. Competent in mixed-methods approaches and online recruitment platforms. Knowledge of AI industry, green and circular economies and foresight theories Good written and oral communication skills Strong motivation, with evidence of independent research skills relevant to the project Good time management Desirable attributes: Applications are especially welcomed from those with experience in decolonizing scientific knowledge and methods, business ethics, building dashboards, social media applications, philosophy of science and technology, and machine learning ethics. Funding Notes This is an unfunded position References Crawford, M. M. (2019). A comprehensive scenario intervention typology. Technological Forecasting And Social Change, 149, 119748. https://doi.org/10.1016/j.techfore.2019.119748 Crawford, M. M. & Wright, G. (Eds.). (2025). Improving and Enhancing Scenario Planning: Futures Thinking. Edward Elgar. https://www.e-elgar.com/shop/gbp/improving-and-enhancing-scenario-planning-9781035310579.html Costanzo, L. A., & MacKay, R. B. (Eds.). (2009). Handbook Of Research On Strategy And Foresight. Edward Elgar Publishing. https://www.e-elgar.com/shop/gbp/handbook-of-research-on-strategy-and-foresight-9781845429638.html Bradfield. (20205). Understanding the Future: An Introduction to Scenario Planning. De Gruyter. https://www.degruyterbrill.com/document/doi/10.1515/9783111617442/html Goodwin, P., & Wright, G. (2014). Decision Analysis for Management Judgment. John Wiley & Sons. https://pureportal.strath.ac.uk/en/publications/decision-analysis-for-management-judgment-5th-ed/ Juárez Ramos, V. (Ed.). (2018). Analyzing the role of cognitive biases in the decision-making process. IGI Global. https://www.igi-global.com/book/analyzing-role-cognitive-biases-decision/179223 Bourgeois, R., Karuri-Sebina, G. and Feukeu, K.E. (2024). The future as a public good: decolonising the future through anticipatory participatory action research, Foresight, 26(4), pp. 533-549. https://doi.org/10.1108/FS-11-2021-0225 Department for Science, Innovation and Technology. (2025, January 13). AI opportunities action plan. UK Government. https://www.gov.uk/government/publications/ai-opportunities-action-plan/ai-opportunities-action-plan Department for Science, Innovation and Technology. (2026, January 29). AI opportunities action plan: One year on. UK Government. https://www.gov.uk/government/publications/ai-opportunities-action-plan-one-year-on/ai-opportunities-action-plan-one-year-on Department for Transport. (2021, July 14). Transport decarbonisation plan. UK Government. https://www.gov.uk/government/publications/transport-decarbonisation-plan Scottish Government. (2026, March 20). Scotland’s artificial intelligence strategy 2026–2031. https://www.gov.scot/publications/scotlands-ai-strategy-2026-2031/ Transport Scotland. (2024, March 19). HGV decarbonisation pathway for Scotland: Zero emission truck taskforce. https://www.transport.gov.scot/publication/hgv-decarbonisation-pathway-for-scotland-zero-emission-truck-taskforce/

the Futureof Freight: Foresight for Smarter, Greener, and More Resilient Supply Chains Organisations are turning to AI to create smarter, faster, greener, and more resilient freight and logistics networks. This PhD project will use foresight methods such as scenario planning, Delphi studies, and horizon scanning to examine the future implications of generative, predictive, agentic, causal, or frontier AI for freight operations, integrated logistics infrastructure, resilience building, and the optimisation of operational effectiveness across future supply chains. Projects in this context could develop a strategic foresight and decision-support framework for AI-enabled freight and logistics systems, with relevance to the UK and Scotland, other alternatives are also possible. Proposals should critically explore how emerging AI capabilities could reshape the planning, governance, and performance of supply chains in the context of decarbonisation, digital transformation, and increasing infrastructure and resilience challenges. Innovative proposals are highly encouraged. This research is especially timely given the UK Government’s commitment to accelerating AI adoption through the AI Opportunities Action Plan and its progress update, alongside wider policy commitments to transport decarbonisation and freight transition (Department for Science, Innovation and Technology [DSIT], 2025, 2026; Department for Transport [DfT], 2021). What to expect This programme is ideal for someone who wants to explore innovative trend analyses, predictive analytics, progression modelling, foresight mixed-methods, bibliometric analysis, qualitative comparative analysis, ML, and more. There is scope to develop your own focus and to link into targeted and relevant UN Sustainable Development Goals. We welcome targeted, focused, and well-developed research proposals that address this evolving area. Applicants should demonstrate a clear understanding of the role of AI in shaping our plausible futures and shouldset out a coherent andfeasible plan for investigating thistopic within theUK and/or Scottish context. Proposals should be detailed enough to show originality, methodological rigour, and clear alignment with the project’s themes. Willing to accept full-time and part-time applicants. Publications This programme emphasizes sharing your discoveries through publications (e.g.academic journals, books and conferences) and with non-academic audiences (e.g. industry events, podcasts, and media). Science communication is a core value of the University, and your supervisors and department will support you through these outputs. Academic qualifications A firstdegree (at leasta 2.1) ideallyin behavioural sciences, supply management, or related, witha good fundamental knowledge of statistical analyses, supply chain systems, logistics and secondary data research methods. English language requirement IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online. Essential attributes: Experience of fundamental quantitative research methods, human-based testing, secondary data methods, organisational behaviour theory, LLM, NLP, or related machine learning context. Competent in mixed-methods approaches and online recruitment platforms. Knowledge of AI industry, green and circular economies and foresight theories Good written and oral communication skills Strong motivation, with evidence of independent research skills relevant to the project Good time management Desirable attributes: Applications are especially welcomed from those with experience in decolonizing scientific knowledge and methods, business ethics, building dashboards, social media applications, philosophy of science and technology, and machine learning ethics. Funding Notes This is an unfunded position References Crawford, M. M. (2019). A comprehensive scenario intervention typology. Technological Forecasting And Social Change, 149, 119748. https://doi.org/10.1016/j.techfore.2019.119748 Crawford, M. M. & Wright, G. (Eds.). (2025). Improving and Enhancing Scenario Planning: Futures Thinking. Edward Elgar. https://www.e-elgar.com/shop/gbp/improving-and-enhancing-scenario-planning-9781035310579.html Costanzo, L. A., & MacKay, R. B. (Eds.). (2009). Handbook Of Research On Strategy And Foresight. Edward Elgar Publishing. https://www.e-elgar.com/shop/gbp/handbook-of-research-on-strategy-and-foresight-9781845429638.html Bradfield. (20205). Understanding the Future: An Introduction to Scenario Planning. De Gruyter. https://www.degruyterbrill.com/document/doi/10.1515/9783111617442/html Goodwin, P., & Wright, G. (2014). Decision Analysis for Management Judgment. John Wiley & Sons. https://pureportal.strath.ac.uk/en/publications/decision-analysis-for-management-judgment-5th-ed/ Juárez Ramos, V. (Ed.). (2018). Analyzing the role of cognitive biases in the decision-making process. IGI Global. https://www.igi-global.com/book/analyzing-role-cognitive-biases-decision/179223 Bourgeois, R., Karuri-Sebina, G. and Feukeu, K.E. (2024). The future as a public good: decolonising the future through anticipatory participatory action research, Foresight, 26(4), pp. 533-549. https://doi.org/10.1108/FS-11-2021-0225 Department for Science, Innovation and Technology. (2025, January 13). AI opportunities action plan. UK Government. https://www.gov.uk/government/publications/ai-opportunities-action-plan/ai-opportunities-action-plan Department for Science, Innovation and Technology. (2026, January 29). AI opportunities action plan: One year on. UK Government. https://www.gov.uk/government/publications/ai-opportunities-action-plan-one-year-on/ai-opportunities-action-plan-one-year-on Department for Transport. (2021, July 14). Transport decarbonisation plan. UK Government. https://www.gov.uk/government/publications/transport-decarbonisation-plan Scottish Government. (2026, March 20). Scotland’s artificial intelligence strategy 2026–2031. https://www.gov.scot/publications/scotlands-ai-strategy-2026-2031/ Transport Scotland. (2024, March 19). HGV decarbonisation pathway for Scotland: Zero emission truck taskforce. https://www.transport.gov.scot/publication/hgv-decarbonisation-pathway-for-scotland-zero-emission-truck-taskforce/