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Projects: Projects for Investigator
Reference Number EP/K031686/1
Title Resilience and Robustness of Dynamic Manufacturing Supply Networks
Status Completed
Energy Categories Renewable Energy Sources(Ocean Energy) 5%;
Renewable Energy Sources(Wind Energy) 5%;
Not Energy Related 90%;
Research Types Applied Research and Development 100%
Science and Technology Fields SOCIAL SCIENCES (Business and Management Studies) 50%;
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 50%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Professor A R Champneys
No email address given
Engineering Mathematics
University of Bristol
Award Type Standard
Funding Source EPSRC
Start Date 01 September 2013
End Date 28 February 2017
Duration 42 months
Total Grant Value £948,883
Industrial Sectors Manufacturing
Region South West
Programme Manufacturing : Maths
Investigators Principal Investigator Professor A R Champneys , Engineering Mathematics, University of Bristol (99.995%)
  Other Investigator Professor L Walls , Management Science, University of Strathclyde (0.001%)
Professor J Quigley , Management Science, University of Strathclyde (0.001%)
Dr T Gross , Engineering Mathematics, University of Bristol (0.001%)
Professor D Petrovic , Engineering and Computing, Coventry University (0.001%)
Professor BL MacCarthy , Nottingham University Business School, University of Nottingham (0.001%)
  Industrial Collaborator Project Contact , DSTL - Defence Science and Technology Laboratory (0.000%)
Project Contact , National Composites Centre (0.000%)
Project Contact , Regen SW (0.000%)
Project Contact , Nautricity (0.000%)
Project Contact , PA Consulting Group (0.000%)
Project Contact , Rolls-Royce PLC (0.000%)
Project Contact , Tricorn Group (0.000%)
Web Site
Abstract 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
Publications (none)
Final Report (none)
Added to Database 22/11/13