Abstract
Today s factory involves more services and customisation. A paradigm shift is towards 'Industry 4.0' (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment.
| Language | English |
|---|---|
| Title of host publication | SKIMA 2016 - 2016 10th International Conference on Software, Knowledge, Information Management and Applications |
| Place of Publication | Piscataway, NJ. |
| Publisher | IEEE |
| Pages | 79-86 |
| Number of pages | 8 |
| ISBN (Electronic) | 9781509032976 |
| DOIs | |
| Publication status | Published - 1 May 2017 |
| Event | 10th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2016 - Chengdu, Chengdu, China Duration: 15 Dec 2016 → 17 Dec 2016 http://fusion-edu.eu/SKIMA2016/ |
Conference
| Conference | 10th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2016 |
|---|---|
| Abbreviated title | (SKIMA 2016) |
| Country | China |
| City | Chengdu |
| Period | 15/12/16 → 17/12/16 |
| Internet address |
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Keywords
- big data analytics
- fuzzy clustering
- genetic search
- Industry 4.0
- smart design
- Smart manufacturing
Cite this
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Attribute identification and predictive customisation using fuzzy clustering and genetic search for Industry 4.0 environments. / Saldivar, Alfredo Alan Flores; Goh, Cindy; Li, Yun; Yu, Hongnian; Chen, Yi.
SKIMA 2016 - 2016 10th International Conference on Software, Knowledge, Information Management and Applications. Piscataway, NJ. : IEEE, 2017. p. 79-86 7916201.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book
TY - GEN
T1 - Attribute identification and predictive customisation using fuzzy clustering and genetic search for Industry 4.0 environments
AU - Saldivar, Alfredo Alan Flores
AU - Goh, Cindy
AU - Li, Yun
AU - Yu, Hongnian
AU - Chen, Yi
N1 - © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2017/5/1
Y1 - 2017/5/1
N2 - Today s factory involves more services and customisation. A paradigm shift is towards 'Industry 4.0' (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment.
AB - Today s factory involves more services and customisation. A paradigm shift is towards 'Industry 4.0' (i4) aiming at realising mass customisation at a mass production cost. However, there is a lack of tools for customer informatics. This paper addresses this issue and develops a predictive analytics framework integrating big data analysis and business informatics, using Computational Intelligence (CI). In particular, a fuzzy c-means is used for pattern recognition, as well as managing relevant big data for feeding potential customer needs and wants for improved productivity at the design stage for customised mass production. The selection of patterns from big data is performed using a genetic algorithm with fuzzy c-means, which helps with clustering and selection of optimal attributes. The case study shows that fuzzy c-means are able to assign new clusters with growing knowledge of customer needs and wants. The dataset has three types of entities: specification of various characteristics, assigned insurance risk rating, and normalised losses in use compared with other cars. The fuzzy c-means tool offers a number of features suitable for smart designs for an i4 environment.
KW - big data analytics
KW - fuzzy clustering
KW - genetic search
KW - Industry 4.0
KW - smart design
KW - Smart manufacturing
U2 - 10.1109/SKIMA.2016.7916201
DO - 10.1109/SKIMA.2016.7916201
M3 - Conference contribution book
SP - 79
EP - 86
BT - SKIMA 2016 - 2016 10th International Conference on Software, Knowledge, Information Management and Applications
PB - IEEE
CY - Piscataway, NJ.
ER -