Automatic translation of plant data into management performance metrics: a case for real-time and predictive production control
- Submitting institution
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Brunel University London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 153-173103-5219
- Type
- D - Journal article
- DOI
-
10.1080/00207543.2016.1265682
- Title of journal
- International Journal Of Production Research
- Article number
- -
- First page
- 4862
- Volume
- 55
- Issue
- 17
- ISSN
- 0020-7543
- Open access status
- Compliant
- Month of publication
- December
- Year of publication
- 2016
- URL
-
https://www.tandfonline.com/doi/abs/10.1080/00207543.2016.1265682?needAccess=true#aHR0cHM6Ly93d3cudGFuZGZvbmxpbmUuY29tL2RvaS9wZGYvMTAuMTA4MC8wMDIwNzU0My4yMDE2LjEyNjU2ODI/bmVlZEFjY2Vzcz10cnVlQEBAMA==
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
1
- Research group(s)
-
5 - Manufacturing & Design
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Paper describes a novel method to use discrete event simulation principles for real-time KPI Modelling, and predictive strategies for optimising control.
The software solution developed to connect factory control system with discrete event simulation models was also considered innovative. Part of a series of publications that were critical in securing and application of a number multi-million pounds EU projects (e.g. ZFactor, ABreak, HYDROUSA, DEEP PURPLE, B2LF). Of these, projects which have finalised the results are being applied into multiple industrial partners improving their production processes. It has a wide range of uptakes from food, water, electronics, hard metal composites, biorefineries.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
