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Details of Grant 

EPSRC Reference: EP/K014137/1
Title: Adaptive Informatics for Intelligent Manufacturing (AI2M)
Principal Investigator: West, Professor AA
Other Investigators:
Jackson, Professor L Whittow, Professor WG Hinde, Professor CJ
Jackson, Professor T Conway, Professor P
Researcher Co-Investigators:
Mr A Bindel Dr DM Segura Velandia
Project Partners:
Ford Motor Co GE Aviation Invotec Group LTD
KET Ltd Manufacturing Technology Centre MTG Research Ltd
S2S Electronics Ltd
Department: Wolfson Sch of Mech, Elec & Manufac Eng
Organisation: Loughborough University
Scheme: Standard Research
Starts: 01 February 2013 Ends: 30 April 2019 Value (£): 1,934,793
EPSRC Research Topic Classifications:
Information & Knowledge Mgmt Manufact. Business Strategy
Networks & Distributed Systems Robotics & Autonomy
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
07 Nov 2012 Future ICT-enabled Manufacuring (Fulls) Announced
Summary on Grant Application Form
The AI2M research cluster will bring together leading researchers and practitioners in high value manufacturing, information science, ICT, mathematical sciences and manufacturing services to address the needs for future globally competitive ICT-supported manufacturing practices and infrastructures. The cluster also leverages two distinct supply chains, automotive and aerospace and defence with associated ICT and manufacturing service providers.

UK manufacturing has to migrate towards supplying innovative, high quality, variable volume solutions to a global market. Low wage competition and reduced profit margins increase the difficulty of recovering the costs of early lifecycle phases (specification, design, analysis and setup) especially for lower volume products. "Right first time" production is a necessity to survive. In the automotive domain the relatively high volume market is crippled by increased complexity, quality and customer demands for variety. The high added-value, low volume defence and aerospace domains are also under pressure from: the spectrum of product and process complexity; the harsh manufacturing and operational environments and severe safety and legislative requirements. The future of UK manufacturing depends on supply chains being able to: remove defects generated throughout manufacturing; formalise and share product and process knowledge; optimise strategy based on resource utilisation, traceability and lifecycle performance monitoring and understand the implications of design features on manufacturing and operational performance as well as the impact of new materials, components and legislation (e.g. End of Life Vehicle) and the impact of the adoption of new technologies and business models. To pay dividends both in supply chain efficiencies, compliance and new business models, companies must capture and analyse a larger range of data, faster, at lower cost and manage it better than ever before.

The challenge of this project is therefore to develop an on-demand intelligent product lifecycle service system for increased yield for products and processes that can bridge the information gaps associated with inefficient supply chain integration and a lack of knowledge on product usage throughout lifecycles. Current commercial solutions are limited to "on-site" silos of information that are restricting UK manufacturing in terms of its ability to: optimise efficiency in materials, resource, energy utilisation; speed up innovation; improve the generation and exploitation of manufacturing intelligence; support supply chain collaboration throughout the product and process lifecycles, and enable new business models and technologies to be readily adopted (e.g. product service systems (PSS) supporting either product operation, usage or results oriented business models).

The key research challenges to be addressed by this cluster include: Service Foundations (dynamically reconfigurable architectures, data and process integration and sematic enhanced service discovery); Service Composition (composability analyses, dynamic and adaptive processes, quality of service compositions, business driven compositions); Service Management and Monitoring (self: -configuring, -adapting, -healing, -optimising and -protecting and Service Design and Development engineering of business services, versioning and adaptivity, governance across supply chains).
Key Findings
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Organisation Website: http://www.lboro.ac.uk