Application of decision trees for quality management support

Rafal Cupek; Adam Ziebinski; Marek Drewniak

doi:10.1007/978-3-319-98446-9_7

Application of decision trees for quality management support

Rafal Cupek
, Adam Ziebinski
, Marek Drewniak

Department of Distributed Systems and Informatic Devices (RAU/RAU8)

AIUT

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

The quality management process is one of the most important manufacturing activities. Although it can be implemented in a dedicated IT system, because of the easy access to production data in Manufacturing Execution Systems, it seems that an MES is a particularly convenient place for its implementation. This paper describes the concept of using decision tree methods to analyse the relationship between the production path and quality problems. The proposed method is based on an information model that is compliant with the ISA95 standard. Because of this, it can be applied not only in the case presented in the research part, but is also applicable for the problem of quality analysis in other types of discrete production. The authors present the information model that was used, the proposed method of analysis and the results for the simulation data. The simulation scenario was created as a simplification of the actual production process of electronic devices performed by AIUT company.

Original language	English
Title of host publication	Computational Collective Intelligence - 10th International Conference, ICCCI 2018, Proceedings
Editors	Ngoc Thanh Nguyen, Bogdan Trawinski, Ngoc Thanh Nguyen, Elias Pimenidis, Zaheer Khan
Publisher	Springer Verlag
Pages	67-78
Number of pages	12
ISBN (Print)	9783319984452
DOIs	https://doi.org/10.1007/978-3-319-98446-9_7
Publication status	Published - 2018
Event	10th International Conference on Computational Collective Intelligence, ICCCI 2018 - Bristol, United Kingdom Duration: 5 Sept 2018 → 7 Sept 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11056 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Computational Collective Intelligence, ICCCI 2018
Country/Territory	United Kingdom
City	Bristol
Period	5/09/18 → 7/09/18

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Decision trees
ISA95 (IEC 62264)
Information model
Manufacturing Execution System (MES)
Quality management

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-98446-9_7

Cite this

Cupek, R., Ziebinski, A., & Drewniak, M. (2018). Application of decision trees for quality management support. In N. T. Nguyen, B. Trawinski, N. T. Nguyen, E. Pimenidis, & Z. Khan (Eds.), Computational Collective Intelligence - 10th International Conference, ICCCI 2018, Proceedings (pp. 67-78). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11056 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-98446-9_7

Cupek, Rafal ; Ziebinski, Adam ; Drewniak, Marek. / Application of decision trees for quality management support. Computational Collective Intelligence - 10th International Conference, ICCCI 2018, Proceedings. editor / Ngoc Thanh Nguyen ; Bogdan Trawinski ; Ngoc Thanh Nguyen ; Elias Pimenidis ; Zaheer Khan. Springer Verlag, 2018. pp. 67-78 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ef838813ec454a249826f5ec808b44cb,

title = "Application of decision trees for quality management support",

abstract = "The quality management process is one of the most important manufacturing activities. Although it can be implemented in a dedicated IT system, because of the easy access to production data in Manufacturing Execution Systems, it seems that an MES is a particularly convenient place for its implementation. This paper describes the concept of using decision tree methods to analyse the relationship between the production path and quality problems. The proposed method is based on an information model that is compliant with the ISA95 standard. Because of this, it can be applied not only in the case presented in the research part, but is also applicable for the problem of quality analysis in other types of discrete production. The authors present the information model that was used, the proposed method of analysis and the results for the simulation data. The simulation scenario was created as a simplification of the actual production process of electronic devices performed by AIUT company.",

keywords = "Decision trees, ISA95 (IEC 62264), Information model, Manufacturing Execution System (MES), Quality management",

author = "Rafal Cupek and Adam Ziebinski and Marek Drewniak",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 10th International Conference on Computational Collective Intelligence, ICCCI 2018 ; Conference date: 05-09-2018 Through 07-09-2018",

year = "2018",

doi = "10.1007/978-3-319-98446-9\_7",

language = "English",

isbn = "9783319984452",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "67--78",

editor = "Nguyen, \{Ngoc Thanh\} and Bogdan Trawinski and Nguyen, \{Ngoc Thanh\} and Elias Pimenidis and Zaheer Khan",

booktitle = "Computational Collective Intelligence - 10th International Conference, ICCCI 2018, Proceedings",

address = "Germany",

}

Cupek, R , Ziebinski, A & Drewniak, M 2018, Application of decision trees for quality management support. in NT Nguyen, B Trawinski, NT Nguyen, E Pimenidis & Z Khan (eds), Computational Collective Intelligence - 10th International Conference, ICCCI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11056 LNAI, Springer Verlag, pp. 67-78, 10th International Conference on Computational Collective Intelligence, ICCCI 2018, Bristol, United Kingdom, 5/09/18. https://doi.org/10.1007/978-3-319-98446-9_7

Application of decision trees for quality management support. / Cupek, Rafal ; Ziebinski, Adam; Drewniak, Marek.
Computational Collective Intelligence - 10th International Conference, ICCCI 2018, Proceedings. ed. / Ngoc Thanh Nguyen; Bogdan Trawinski; Ngoc Thanh Nguyen; Elias Pimenidis; Zaheer Khan. Springer Verlag, 2018. p. 67-78 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11056 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Application of decision trees for quality management support

AU - Cupek, Rafal

AU - Ziebinski, Adam

AU - Drewniak, Marek

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - The quality management process is one of the most important manufacturing activities. Although it can be implemented in a dedicated IT system, because of the easy access to production data in Manufacturing Execution Systems, it seems that an MES is a particularly convenient place for its implementation. This paper describes the concept of using decision tree methods to analyse the relationship between the production path and quality problems. The proposed method is based on an information model that is compliant with the ISA95 standard. Because of this, it can be applied not only in the case presented in the research part, but is also applicable for the problem of quality analysis in other types of discrete production. The authors present the information model that was used, the proposed method of analysis and the results for the simulation data. The simulation scenario was created as a simplification of the actual production process of electronic devices performed by AIUT company.

AB - The quality management process is one of the most important manufacturing activities. Although it can be implemented in a dedicated IT system, because of the easy access to production data in Manufacturing Execution Systems, it seems that an MES is a particularly convenient place for its implementation. This paper describes the concept of using decision tree methods to analyse the relationship between the production path and quality problems. The proposed method is based on an information model that is compliant with the ISA95 standard. Because of this, it can be applied not only in the case presented in the research part, but is also applicable for the problem of quality analysis in other types of discrete production. The authors present the information model that was used, the proposed method of analysis and the results for the simulation data. The simulation scenario was created as a simplification of the actual production process of electronic devices performed by AIUT company.

KW - Decision trees

KW - ISA95 (IEC 62264)

KW - Information model

KW - Manufacturing Execution System (MES)

KW - Quality management

UR - https://www.scopus.com/pages/publications/85053166192

U2 - 10.1007/978-3-319-98446-9_7

DO - 10.1007/978-3-319-98446-9_7

M3 - Conference contribution

AN - SCOPUS:85053166192

SN - 9783319984452

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 67

EP - 78

BT - Computational Collective Intelligence - 10th International Conference, ICCCI 2018, Proceedings

A2 - Nguyen, Ngoc Thanh

A2 - Trawinski, Bogdan

A2 - Nguyen, Ngoc Thanh

A2 - Pimenidis, Elias

A2 - Khan, Zaheer

PB - Springer Verlag

T2 - 10th International Conference on Computational Collective Intelligence, ICCCI 2018

Y2 - 5 September 2018 through 7 September 2018

ER -

Cupek R , Ziebinski A, Drewniak M. Application of decision trees for quality management support. In Nguyen NT, Trawinski B, Nguyen NT, Pimenidis E, Khan Z, editors, Computational Collective Intelligence - 10th International Conference, ICCCI 2018, Proceedings. Springer Verlag. 2018. p. 67-78. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-98446-9_7

Application of decision trees for quality management support

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this