Artificial Intelligence in Engineering Management in the Manufacturing Environment
Lead Research Organisation:
Durham University
Department Name: Engineering
Abstract
The focus of this project is the production of a novel AI solution capable of automating the quotation
process for manufacture companies in order to increase efficiency whilst neither reducing profit nor
losing business due to unrealistic quotations. The research of this AI project will be focused around the
need to create a 'feedback loop' of continual improvement of enquiry prioritisation, quotation accuracy
(plan vs actual), commercial base costing verification and automated review, shop floor communication
and production process optimisation.
Additionally, this project aims to augment the decomposition of data and metrics and to present
engineers and technicians with the most promising decision options, hence reducing the number of
parameters as a result of intelligent data processing and prediction. Due to my knowledge of AI
developed in my master's project and my experience participating in academic projects with industry
links I would be the ideal candidate to carry out this project.
This project is planned in collaboration with several manufacture companies based in County Durham,
including Dyer engineering. Dyer Engineering is a rapidly expanding business which manufactures metal
components and structures and delivers related services. Their business operates across a diverse range
of markets, with the ability to manufacture parts you can pick up by the handful, through to large-scale
structures operating in harsh sub-sea environments.
Following the winning of a national tender from Innovate UK to become the first UK demonstrator site
to showcase Industry 4.0 technology, namely IoT asset tracking, Dyer have propelled in their adoption
of digital technology to leverage gains in productivity and staff welfare. As one of the Industry 4.0
showcase companies of Innovate UK, data from a variety of sources is readily available at Dyer
Engineering. Currently, this information is communicated through various flexible BI dashboards and
subject to the tacit knowledge of individuals as well as a reliance that learning takes place.
process for manufacture companies in order to increase efficiency whilst neither reducing profit nor
losing business due to unrealistic quotations. The research of this AI project will be focused around the
need to create a 'feedback loop' of continual improvement of enquiry prioritisation, quotation accuracy
(plan vs actual), commercial base costing verification and automated review, shop floor communication
and production process optimisation.
Additionally, this project aims to augment the decomposition of data and metrics and to present
engineers and technicians with the most promising decision options, hence reducing the number of
parameters as a result of intelligent data processing and prediction. Due to my knowledge of AI
developed in my master's project and my experience participating in academic projects with industry
links I would be the ideal candidate to carry out this project.
This project is planned in collaboration with several manufacture companies based in County Durham,
including Dyer engineering. Dyer Engineering is a rapidly expanding business which manufactures metal
components and structures and delivers related services. Their business operates across a diverse range
of markets, with the ability to manufacture parts you can pick up by the handful, through to large-scale
structures operating in harsh sub-sea environments.
Following the winning of a national tender from Innovate UK to become the first UK demonstrator site
to showcase Industry 4.0 technology, namely IoT asset tracking, Dyer have propelled in their adoption
of digital technology to leverage gains in productivity and staff welfare. As one of the Industry 4.0
showcase companies of Innovate UK, data from a variety of sources is readily available at Dyer
Engineering. Currently, this information is communicated through various flexible BI dashboards and
subject to the tacit knowledge of individuals as well as a reliance that learning takes place.
Organisations
People |
ORCID iD |
| Stefano Giani (Primary Supervisor) | |
| Victoria Miles (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/T518001/1 | 01/10/2020 | 30/09/2025 | |||
| 2457717 | Studentship | EP/T518001/1 | 01/10/2020 | 31/03/2024 | Victoria Miles |