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Details of Grant 

EPSRC Reference: EP/V05127X/1
Title: Responsive Manufacturing: Maximising Value Through Life
Principal Investigator: Newnes, Professor LB
Other Investigators:
Giannikas, Dr V McManus, Professor MC Yu, Dr H
Researcher Co-Investigators:
Dr S Lattanzio
Project Partners:
ARES Software UK Ltd. Arup Group Ltd Quick Release (Automotive) Ltd (UK)
The Manufacturing Technology Centre Ltd
Department: Mechanical Engineering
Organisation: University of Bath
Scheme: Standard Research
Starts: 24 May 2021 Ends: 23 May 2024 Value (£): 523,467
EPSRC Research Topic Classifications:
Design & Testing Technology Manufact. Enterprise Ops& Mgmt
EPSRC Industrial Sector Classifications:
Manufacturing Construction
Transport Systems and Vehicles
Related Grants:
Panel History:
Panel DatePanel NameOutcome
23 Feb 2021 Responsive Manufacturing Full Announced
Summary on Grant Application Form
Imagine you are responsible for the operation of a manufacturing system that is producing the next generation of electric cars. The manufacturing system is streamlined and producing leading-edge innovative cars just in time to meet the consumer needs, has minimum waste, the supply chain providing materials and products to the manufacturing line is green and quality of production is high. Everyone is happy. However, suddenly the supply of a core material used in the manufacture of the car is now quite scarce i.e. there is limited availability. Unfortunately, our manufacturing system is no longer working as it should! The manufacturing system is not producing enough cars and the productivity has hit rock bottom. Sadly, there were indications that the material was becoming scarce - the supplier had been issuing warnings, but the warnings were missed and no-one realised the impact this would have. So, we now have a manufacturing system that is not efficient, the cars can no longer be manufactured at an appropriate rate, and the manufacturer is about to be bankrupt! This could have all been avoided if we had a manufacturing system that was responsive i.e. adapt to change, be sustainable and resilient. The outputs from this research are geared to avoid such occurrences by providing the information to enable the manufacturing system to adapt to both internal and external factors i.e. enable the manufacturing system to be responsive.

Our research will use Data, Information and Knowledge, automatically accessed via digital methods to enable the brain (the control centre) of the manufacturing system to continually assess its current status and predict future states. We will facilitate the ability of a manufacturing system to be truly responsive, whilst sustaining its whole life value. Although easy to say - achieving this is extremely challenging. However, with the current impacts of major disruptions such as COVID-19 on manufacturing there is a strong desire and willingness from manufacturers to ensure their systems can be responsive. Hence, the call and our proposed solution is very timely. In parallel to this need, the advancements in the technology and processes, such as digitalisation, 5G and Industry 4.0 have reached the stage that we can create a means by which a manufacturing system can automatically assess whether it needs to change and predict the most appropriate action.

Our proposed solution has its foundations in value modelling (a value model is used to assess the impact of any proposed solution in terms of e.g. cost, quality, delivery, environment) to evaluate and assess the impact of any proposed response to changes within/external to the manufacturing system. We will achieve this via the investigation and analysis of a number of real-life manufacturing case studies to identify the level of autonomy that is appropriate in relation to the characteristics of the manufacturing system. We will identify the core Data Information and Knowledge required to create the value model, use data analytic techniques such as clustering/network modelling to automatically analyse the manufacturing system and create a pragmatic and useable step-by-step process to ensure impact from the outputs of the research.

In summary, our Vision is to create an automated real-time manufacturing system support toolkit to achieve whole life value from current and future Manufacturing Systems, maximising value through their lifetime i.e. being responsive, sustainable, adaptable and resilient.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Organisation Website: http://www.bath.ac.uk