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Details of Grant 

EPSRC Reference: EP/V051113/1
Title: Productivity and Sustainability Management in the Responsive Factory
Principal Investigator: Corney, Professor J
Other Investigators:
Sherlock, Professor A Quigley, Professor J Annamalai Vasantha, Dr G
Researcher Co-Investigators:
Project Partners:
Babcock International Group Plc (UK) Simul8 Corporation ubisense
Department: Sch of Engineering
Organisation: University of Edinburgh
Scheme: Standard Research
Starts: 18 October 2021 Ends: 17 April 2025 Value (£): 1,146,222
EPSRC Research Topic Classifications:
Manufacturing Machine & Plant
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
23 Feb 2021 Responsive Manufacturing Full Announced
Summary on Grant Application Form
The ambition of this project is to use a mix of factory activity data to optimise industrial operations, and to identify opportunities and deliver improvements in efficiency, productivity and sustainability.

The rapid advance of digital sensing technologies, is making the real time recording of activities in a manufacturing environment both practical and affordable. However, the availability of diverse, real time data about movement and activity does not automatically help engineers manage the complex, dynamic environments typical of modern industrial operations. To do this they need tools that support their interpretation of constantly changing data in ways that enhance productivity and sustainability. In other words, the research challenge posed by digital manufacturing is not the capture of data, but rather the lack of computational methods to analyse large flows of diverse (i.e. multimodal) sensor data and recognise the patterns that allow engineers to assess the current state of the shop floor, understand the impact of past events and predict the consequences of incidents on a range measures. Motivated by this need, the following proposal details a program of work to investigate if the forms of probabilistic networks that have been employed to generate computational models from location tracking data in other contexts (e.g. vehicles movements in traffic models and the daily routines of individuals in domestic environments) can be extended to work with multiple forms of industrial activity data recorded on a factory floor. Such a model would allow diverse signals of manufacturing activity (e.g. material transport, staff movement, vibration, electrical current and air quality etc.) to be used to infer the behaviour of an industrial workplace and generate quantitative measures that support decisions which impact on a sites' production and sustainability performance.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
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