Manufacturing Analytics - DIAL Newsletter Spring 2018
Manufacturing Analytics is one of the youngest groups in DIAL, and we think it is crucial to get not only academic but also industrial input in the development of AI for manufacturing. With this in mind, we had an exciting beginning to the year with our first Manufacturing Analytics Workshop, where delegates from industry and academia discussed the breadth and depth of applications of analytics and AI technology used in manufacturing. Topics ranged from supply chain risk analytics to digitalising manual manufacturing processes using image recognition. The workshop was sponsored by Cambridge Big Data, and due to its success, a special interest group (SIGMA – Special Interest Group in Manufacturing Analytics) was formed to continue fostering industry-academia knowledge exchange through annual workshops and quarterly webinars. Please get in touch if you are interested in joining by emailing Alexandra Brintrup, email@example.com
Image: Prof Thomas Choi from Arizona State University presenting an industrial survey on the adoption of analytics technologies in supply chain management.
The group has been awarded two new projects: the Pitch-In project (Promoting the Internet of Things via Collaborations between HEIs and Industry), led by the University of Sheffield in collaboration with the University of Cambridge, Oxford and Newcastle. The project is funded under Research England’s Connecting Capability Fund (CCF), and is one of 14 successful bids awarded across England. The project will investigate barriers to successful IoT take-up, trial solutions, and capture and share good practice learning outcomes. We will lead the Manufacturing theme and develop a number of feasibility studies and industrial IoT demonstrators – read more about it here.
The second project, funded by the Aerospace Technology Institute/Innovate UK, is on Digital Through Life Engineering Services (Digital TES). The project is led by Rolls Royce, and involves DIAL’s Manufacturing Analytics, Asset Management and Data Management groups. As part of the project, we will be investigating how aerospace companies can develop common standards and architectures to establish Digital Twins of their assets, so they can monitor and analyse them to drive optimal decisions for maintenance and services.
We are also welcoming two Manufacturing Engineering Tripos students to work with us on over the summer. Johnson Pak will investigate the potential of supply chain disruption prediction algorithms with Boeing, and Pranay Shah will be exploring the use of predictive quality analytics with GKN Powder Metallurgy.