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Details of Grant 

EPSRC Reference: EP/W019868/1
Title: Collective Risk Learning for Supply Chain Disruption Preparedness
Principal Investigator: Brintrup, Dr A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Advanced Manufacturing (Sheffield) Ltd Enspan.io Rolls-Royce Plc (UK)
Department: Engineering
Organisation: University of Cambridge
Scheme: Standard Research
Starts: 01 April 2022 Ends: 30 September 2024 Value (£): 438,572
EPSRC Research Topic Classifications:
Design & Testing Technology Manufact. Business Strategy
EPSRC Industrial Sector Classifications:
Manufacturing
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 Feb 2022 Engineering Prioritisation Panel Meeting 8 and 9 February 2022 Announced
Summary on Grant Application Form
The need for more efficient, resilient, supply chains has been highlighted by various government inquiries and amplified by recent world events including Brexit and Covid-19.

As organisations outsource production to one another they create economies-of-scale and reduce prices but also increase risk of disruption cascades if any member of the chain is disrupted. Typically, organisations act alone, rather than collectively, when predicting delays, disruptions and deciding on safety inventories. However, disruption data an individual organization can collect and analyse is small, imbalanced, and partial entirely to its own view. When uncertainties increase, this individualistic approach results in chaotic oscillations between stock inflation and stock-outs. Numerous studies proved that increased data sharing and collective decision making would increase resilience, but this has not been plausible as members of the chain fear that information such as capacity and excess stock can be "inferred" by clients, and used opportunistically for cost reduction. Two key emergent approaches can help change this state of affairs.

First is the development of low cost platforms that facilitate data sharing for SMEs and their buyers, which we will use in this project to enable SME access to collective learning. The second is the emergence of AI technology. In this project Collective learning (CL) approaches will be developed, which will enable organizational agents to collaboratively develop a shared prediction model. Here, if one organization is able to predict a disruption, its knowledge can be shared, preventing others from stock outs. As the approach can be automated, costs of manual orchestration are avoided. CORES approaches will be integrated into low cost data integration platforms and trialled within the Aerospace sector.
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Organisation Website: http://www.cam.ac.uk