| EPSRC Reference: |
EP/K031686/1 |
| Title: |
Resilience and Robustness of Dynamic Manufacturing Supply Networks |
| Principal Investigator: |
Champneys, Professor AR |
| Other Investigators: |
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| Researcher Co-Investigators: |
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| Project Partners: |
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| Department: |
Engineering Mathematics and Technology |
| Organisation: |
University of Bristol |
| Scheme: |
Standard Research |
| Starts: |
01 September 2013 |
Ends: |
28 February 2017 |
Value (£): |
948,883
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| EPSRC Research Topic Classifications: |
| Manufact. Enterprise Ops& Mgmt |
Numerical Analysis |
| Statistics & Appl. Probability |
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| EPSRC Industrial Sector Classifications: |
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| Related Grants: |
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| Panel History: |
| Panel Date | Panel Name | Outcome |
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19 Feb 2013
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Maths-Manufacturing Call Prioritisation Panel
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Announced
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Summary on Grant Application Form |
Efficient and effective manufacturing supply networks (MSN) are essential to the functioning of the global economy. In line with the EPSRC call, this proposal is premised on the strong belief that appropriate mathematical theory and methods can provide fundamentally new understanding on the behaviour of MSNs and provide an effective investigative toolset for MSN analysis, design and management. In particular we argue that the power of network science can be harnessed to underpin new thinking in MSNs for resilience and robustness.
The work will be strongly embedded in real MSNs in three domains - producer-driven inbound MSNs and outbound distribution channels for industrial companies; global MSNs for critical products used in high-valued manufacturing (e.g. titanium or composite pre-preg materials); and evolving MSNs for emerging UK industries such as renewable energy. The project will develop and apply existing and new mathematics specifically in the theory of complex adaptive networks, drawing on techniques from game theory, dynamical systems and Bayesian informatics. It will also learn from related modelling approaches in ecology, metabolism modelling and utility grids.
This grant will represent the first attempt to develop an integrated mathematical modelling suite to support effective decision making in MSNs in the context of risk and uncertainty. The work will build on disparate recent developments in network science and complex adaptive dynamical systems, Bayesian statistics and operational research to develop new models and measures to better understand and analyse MSN behaviour and performance. Multiple perspectives and a multi-level view of risks and vulnerabilities in MSNs will be taken, including physical, financial, informational, relational, and governance perspectives at the strategic MSN design and policy levels, and risk mitigating strategies at both strategic and operational levels to support MSN management.
This is an adventurous and challenging proposal due to the following reasons: (1) The PIs based in have various domains of expertise, from theory of complex networks and nonlinear dynamics, to applied statistics in domains such as reliability and risk assessment, and development and application of operational research and operations management methods to MSN management and control problems. However, our expertise is complementary and will add a substantial body of new knowledge and bring novelties to the theory of complex networks, network dynamics and Bayesian networks, but also, applications of these new models to real-world MSN problems will ultimately lead to better understanding of complex MSN behaviour and will improve MSN management and control in the presence of risks and uncertainties. (2) This proposal will bring together PIs and PDRAs from 4 universities. The management of the resources involved is a challenge on its own. However, we believe that a very carefully designed project management plan can lead this research collaboration to its success. Furthermore, if funded, this research project can potentially secure the continuation of the collaboration among the four universities. (3) The project will involve a wide array of industrial partners from manufacturing primes (e.g. in Aerospace and Defence) to manufacturing trade organisations and consultants, to representatives of a brand new industry (offshore renewable energy) for which the in-bound MSNn does not yet exist.
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Key Findings |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Potential use in non-academic contexts |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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Impacts |
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| Description |
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk |
| Summary |
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| Date Materialised |
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Sectors submitted by the Researcher |
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This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
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| Project URL: |
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| Further Information: |
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| Organisation Website: |
http://www.bris.ac.uk |