Overview
The energy transition for a low carbon future requires a reduction in greenhouse gas emissions from fossil fuel usage along global supply chains (GSCs). GSCs are influenced by complex tensions between regulatory, logistics, economic, and geopolitical factors. This project will develop and validate a dynamic decision-making support system to guide stakeholders in making dynamic trade-off decisions while they are reconfiguring GSCs.
About this opportunity
Context
The energy transition for a low carbon future requires a reduction in greenhouse gas emissions from fossil fuel usage along global supply chains (GSCs). GSCs are influenced by, and need to respond to, complex tensions between regulatory, logistics, economic, and geopolitical factors. Optimisation of GSCs therefore needs solving using modelling that is well-constrained and can navigate multi-objective, conflicting issues, with regularly updated assumptions and re-calibration. GSCs will hugely benefit from a dynamic decision support system (DDSS) that can guide stakeholders in informed trade-off decision making.
Aims
This project will develop and validate a conceptual decision intelligence framework and an operational DDSS, to guide stakeholders in making their dynamic trade-off decisions while they are in the process of reconfiguring GSCs (Table 1, Study 1). The developed DDSS will be validated through its application to the critical raw materials (strategic raw materials that are at critical risk of short supply) at the minerals-energy nexus, and to the petrochemical industries GSCs, which have significant scope for reconfiguring to decarbonise by making dynamic trade-off decisions (Table 1, Studies 2 and 3).
Outline
Table 1 – Project Outline (ESG = Environmental, social, governance)
| Study |
Context |
Objectives |
Methods |
Deliverables |
| 1 |
Conceptual (based on secondary sources – Partner: Prof Matt Reed, MIF). |
Construct a decision intelligence framework; Develop a DDSS |
Review of publicly available macro information/data sources; Input data in conceptual models and computational tools;
Develop DDSS platform. |
DDSS Platform;
New open source code (e.g. shared in Github). |
| 2 |
Mining industries (& linkage to petrochemical industries).
Partner: Mining ESG Consultant Ben Lepley (SLR) |
Validation of DDSS 1 |
Mapping of material interactions along GSCs for selected product streams & materiality (ESG) assessment; Analysis of responsible mining. |
Systems approach identification of vulnerabilities in GSCs scenario for trade-off understanding |
| 3 |
Petrochemical industries (Partner: Prof Matt Reed, MIF) |
Validation of DDSS 2 |
Field case study involving interviews, participant observations, and document analysis |
Impact of GSC reconfiguration on greenhouse gas emissions & generalisability of DDSS |
During the first year the student will meet collaborators and receive training on tools and methods. The final year will be focussed on independent research and the dissertation write-up.
Impact
The successful applicant, supported by all team members, will feed their expertise into devising overarching solutions to accelerate decarbonisation, building strong long-term partnerships in the process. Novel DDSS platforms validated against two case studies will be published in high impact, peer-reviewed international journals. The project outcomes will lead to consulting requests and follow-on projects, opening excellent career opportunities.
Equality Diversity and Inclusion (EDI): We encourage applications from all sections of the community, regardless of gender, race, disability, religion, sexual orientation, age, career paths and backgrounds.
Further reading
Sharma, M., Shah, J.K. & Joshi, S. “Modeling enablers of supply chain decarbonisation to achieve zero carbon emissions: an environment, social and governance (ESG) perspective”. Environ Sci Pollut Res 30, 76718–76734 (2023). https://doi-org.liverpool.idm.oclc.org/10.1007/s11356-023-27480-6
Xu, L., Jia, F., Lin, X. and Chen, L. (2023), “The role of technology in supply chain decarbonisation: towards an integrated conceptual framework”, Supply Chain Management, Vol. 28 No. 4, pp. 803-824. https://doi-org.liverpool.idm.oclc.org/10.1108/SCM-09-2022-0352