Data-Driven Analysis and Assignment of Manual Assembly Production Lines

Changha Lee; Jongpil Yun; Goo Young Kim; Junwoo Lim; Sang Do Noh; Yongjin Kim

doi:10.1007/978-3-031-43670-3_37

Data-Driven Analysis and Assignment of Manual Assembly Production Lines

Changha Lee
, Jongpil Yun
, Goo Young Kim
, Junwoo Lim
, Sang Do Noh
, Yongjin Kim

Department of Systems Management Engineering

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

4 Scopus citations

Abstract

Industry 5.0 envisions a future of manufacturing that realizes personalized production, increased efficiency, and sustainability with a focus on human-centric production. While there have been numerous studies aimed at improving the efficiency of assembly processes, there is a need for smart manufacturing technologies that consider the individual characteristics of workers. This study explores manufacturing shop-floor data collected through wearable devices to analyze the characteristics of processes and workers quantitatively, which have traditionally been evaluated qualitatively. In addition, this study proposes a mathematical method to define the process difficulty and the process proficiency of workers based on data, and presents a heuristic algorithm for worker assignment using a process proficiency matrix of workers by processes. A case study is conducted in a laboratory environment mimicking a home appliance assembly production line in the United States to validate the feasibility of the proposed methodology. This study presents a novel approach to examining human-centric assembly production lines and provides empirical evidence supporting the efficiency of data-driven analysis and improvement techniques for human-centric assembly lines.

Original language	English
Title of host publication	Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings
Editors	Erlend Alfnes, Anita Romsdal, Jan Ola Strandhagen, Gregor von Cieminski, David Romero
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	535-547
Number of pages	13
ISBN (Print)	9783031436697
DOIs	https://doi.org/10.1007/978-3-031-43670-3_37
State	Published - 2023
Event	IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023 - Trondheim, Norway Duration: 17 Sep 2023 → 21 Sep 2023

Publication series

Name	IFIP Advances in Information and Communication Technology
Volume	691 AICT
ISSN (Print)	1868-4238
ISSN (Electronic)	1868-422X

Conference

Conference	IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023
Country/Territory	Norway
City	Trondheim
Period	17/09/23 → 21/09/23

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Industry 5.0
Manual Assembly Production Line
Process Difficulty
Process Proficiency
Wearable Device

Access to Document

10.1007/978-3-031-43670-3_37

Cite this

Lee, C., Yun, J., Kim, G. Y., Lim, J., Do Noh, S., & Kim, Y. (2023). Data-Driven Analysis and Assignment of Manual Assembly Production Lines. In E. Alfnes, A. Romsdal, J. O. Strandhagen, G. von Cieminski, & D. Romero (Eds.), Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings (pp. 535-547). (IFIP Advances in Information and Communication Technology; Vol. 691 AICT). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-43670-3_37

Lee, Changha ; Yun, Jongpil ; Kim, Goo Young et al. / Data-Driven Analysis and Assignment of Manual Assembly Production Lines. Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings. editor / Erlend Alfnes ; Anita Romsdal ; Jan Ola Strandhagen ; Gregor von Cieminski ; David Romero. Springer Science and Business Media Deutschland GmbH, 2023. pp. 535-547 (IFIP Advances in Information and Communication Technology).

@inproceedings{039bcd0f07c74cba95ece9861cc17320,

title = "Data-Driven Analysis and Assignment of Manual Assembly Production Lines",

abstract = "Industry 5.0 envisions a future of manufacturing that realizes personalized production, increased efficiency, and sustainability with a focus on human-centric production. While there have been numerous studies aimed at improving the efficiency of assembly processes, there is a need for smart manufacturing technologies that consider the individual characteristics of workers. This study explores manufacturing shop-floor data collected through wearable devices to analyze the characteristics of processes and workers quantitatively, which have traditionally been evaluated qualitatively. In addition, this study proposes a mathematical method to define the process difficulty and the process proficiency of workers based on data, and presents a heuristic algorithm for worker assignment using a process proficiency matrix of workers by processes. A case study is conducted in a laboratory environment mimicking a home appliance assembly production line in the United States to validate the feasibility of the proposed methodology. This study presents a novel approach to examining human-centric assembly production lines and provides empirical evidence supporting the efficiency of data-driven analysis and improvement techniques for human-centric assembly lines.",

keywords = "Industry 5.0, Manual Assembly Production Line, Process Difficulty, Process Proficiency, Wearable Device",

author = "Changha Lee and Jongpil Yun and Kim, \{Goo Young\} and Junwoo Lim and \{Do Noh\}, Sang and Yongjin Kim",

note = "Publisher Copyright: {\textcopyright} 2023, IFIP International Federation for Information Processing.; IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023 ; Conference date: 17-09-2023 Through 21-09-2023",

year = "2023",

doi = "10.1007/978-3-031-43670-3\_37",

language = "English",

isbn = "9783031436697",

series = "IFIP Advances in Information and Communication Technology",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "535--547",

editor = "Erlend Alfnes and Anita Romsdal and Strandhagen, \{Jan Ola\} and \{von Cieminski\}, Gregor and David Romero",

booktitle = "Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings",

}

Lee, C, Yun, J, Kim, GY, Lim, J, Do Noh, S & Kim, Y 2023, Data-Driven Analysis and Assignment of Manual Assembly Production Lines. in E Alfnes, A Romsdal, JO Strandhagen, G von Cieminski & D Romero (eds), Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings. IFIP Advances in Information and Communication Technology, vol. 691 AICT, Springer Science and Business Media Deutschland GmbH, pp. 535-547, IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023, Trondheim, Norway, 17/09/23. https://doi.org/10.1007/978-3-031-43670-3_37

Data-Driven Analysis and Assignment of Manual Assembly Production Lines. / Lee, Changha; Yun, Jongpil; Kim, Goo Young et al.
Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings. ed. / Erlend Alfnes; Anita Romsdal; Jan Ola Strandhagen; Gregor von Cieminski; David Romero. Springer Science and Business Media Deutschland GmbH, 2023. p. 535-547 (IFIP Advances in Information and Communication Technology; Vol. 691 AICT).

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

TY - GEN

T1 - Data-Driven Analysis and Assignment of Manual Assembly Production Lines

AU - Lee, Changha

AU - Yun, Jongpil

AU - Kim, Goo Young

AU - Lim, Junwoo

AU - Do Noh, Sang

AU - Kim, Yongjin

PY - 2023

Y1 - 2023

N2 - Industry 5.0 envisions a future of manufacturing that realizes personalized production, increased efficiency, and sustainability with a focus on human-centric production. While there have been numerous studies aimed at improving the efficiency of assembly processes, there is a need for smart manufacturing technologies that consider the individual characteristics of workers. This study explores manufacturing shop-floor data collected through wearable devices to analyze the characteristics of processes and workers quantitatively, which have traditionally been evaluated qualitatively. In addition, this study proposes a mathematical method to define the process difficulty and the process proficiency of workers based on data, and presents a heuristic algorithm for worker assignment using a process proficiency matrix of workers by processes. A case study is conducted in a laboratory environment mimicking a home appliance assembly production line in the United States to validate the feasibility of the proposed methodology. This study presents a novel approach to examining human-centric assembly production lines and provides empirical evidence supporting the efficiency of data-driven analysis and improvement techniques for human-centric assembly lines.

AB - Industry 5.0 envisions a future of manufacturing that realizes personalized production, increased efficiency, and sustainability with a focus on human-centric production. While there have been numerous studies aimed at improving the efficiency of assembly processes, there is a need for smart manufacturing technologies that consider the individual characteristics of workers. This study explores manufacturing shop-floor data collected through wearable devices to analyze the characteristics of processes and workers quantitatively, which have traditionally been evaluated qualitatively. In addition, this study proposes a mathematical method to define the process difficulty and the process proficiency of workers based on data, and presents a heuristic algorithm for worker assignment using a process proficiency matrix of workers by processes. A case study is conducted in a laboratory environment mimicking a home appliance assembly production line in the United States to validate the feasibility of the proposed methodology. This study presents a novel approach to examining human-centric assembly production lines and provides empirical evidence supporting the efficiency of data-driven analysis and improvement techniques for human-centric assembly lines.

KW - Industry 5.0

KW - Manual Assembly Production Line

KW - Process Difficulty

KW - Process Proficiency

KW - Wearable Device

UR - https://www.scopus.com/pages/publications/85174444033

U2 - 10.1007/978-3-031-43670-3_37

DO - 10.1007/978-3-031-43670-3_37

M3 - Conference contribution

AN - SCOPUS:85174444033

SN - 9783031436697

T3 - IFIP Advances in Information and Communication Technology

SP - 535

EP - 547

BT - Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings

A2 - Alfnes, Erlend

A2 - Romsdal, Anita

A2 - Strandhagen, Jan Ola

A2 - von Cieminski, Gregor

A2 - Romero, David

PB - Springer Science and Business Media Deutschland GmbH

T2 - IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023

Y2 - 17 September 2023 through 21 September 2023

ER -

Lee C, Yun J, Kim GY, Lim J, Do Noh S, Kim Y. Data-Driven Analysis and Assignment of Manual Assembly Production Lines. In Alfnes E, Romsdal A, Strandhagen JO, von Cieminski G, Romero D, editors, Advances in Production Management Systems. Production Management Systems for Responsible Manufacturing, Service, and Logistics Futures - IFIP WG 5.7 International Conference, APMS 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 535-547. (IFIP Advances in Information and Communication Technology). doi: 10.1007/978-3-031-43670-3_37

Data-Driven Analysis and Assignment of Manual Assembly Production Lines

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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