An intelligent real-time cyber-physical toolset for energy and process prediction and optimisation in the future industrial Internet of Things
- Submitting institution
-
Loughborough University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 615
- Type
- D - Journal article
- DOI
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10.1016/j.future.2017.09.026
- Title of journal
- Future Generation Computer Systems
- Article number
- -
- First page
- 815
- Volume
- 79
- Issue
- Part 3
- ISSN
- 0167-739X
- Open access status
- Compliant
- Month of publication
- October
- Year of publication
- 2017
- URL
-
-
- 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
-
7
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The capability demonstrated in this toolset was a major contributing factor towards Ford’s Industry 4.0 manufacturing and maintenance global programme (>$1B) and traceability standards based on solution incorporated into Ford’s Global Bill of Process defining best practices for manufacturing programmes (Powertrain Manufacturing, Ford of Europe, supporting letter for ICS). In addition, the predicted reduction in energy usage has provided the capability to reduce further the negative impact of manufacturing machinery energy demands on the environment, minimising company fines from the UK Climate Change Levy and reducing the demand on UK infrastructure.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
