Analysis of key operation performance data in manufacturing systems

Ningxuan Kang; Cong Zhao; Jingshan Li; John A. Horst

doi:10.1109/BigData.2015.7364078

Analysis of key operation performance data in manufacturing systems

Ningxuan Kang
, Cong Zhao
, Jingshan Li
, John A. Horst

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

8 Citations (Scopus)

Abstract

In modern manufacturing systems, the key performance indicators (KPIs) are defined as a set of metrics to reflect operation performance. The KPIs are not independent and may have intrinsic mutual relationships. In this paper, by categorizing such data into basic and comprehensive KPIs and supporting metrics, we introduce a hierarchical structure for identification and analysis of KPIs in manufacturing operation management, and use it to investigate pairwise relationship and dependencies between KPIs and supporting metrics. © 2015 IEEE.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Conference on Big Data, Big Data 2015
Publisher	IEEE
Pages	2767-2770
ISBN (Print)	9781479999255
DOIs	https://doi.org/10.1109/BigData.2015.7364078
Publication status	Published - 22 Dec 2015
Externally published	Yes
Event	3rd IEEE International Conference on Big Data, Big Data 2015 - Santa Clara, United States Duration: 29 Oct 2015 → 1 Nov 2015 https://ieeexplore.ieee.org/xpl/conhome/7347101/proceeding

Publication series

Name	Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

Conference

Conference	3rd IEEE International Conference on Big Data, Big Data 2015
Place	United States
City	Santa Clara
Period	29/10/15 → 1/11/15
Internet address	https://ieeexplore.ieee.org/xpl/conhome/7347101/proceeding

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

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

SDG 7 Affordable and Clean Energy
SDG 9 Industry, Innovation, and Infrastructure
SDG 12 Responsible Consumption and Production

Research Keywords

ISO 22400
Key performance indicator (KPI)
manufacturing operation management

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10.1109/BigData.2015.7364078

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title = "Analysis of key operation performance data in manufacturing systems",

abstract = "In modern manufacturing systems, the key performance indicators (KPIs) are defined as a set of metrics to reflect operation performance. The KPIs are not independent and may have intrinsic mutual relationships. In this paper, by categorizing such data into basic and comprehensive KPIs and supporting metrics, we introduce a hierarchical structure for identification and analysis of KPIs in manufacturing operation management, and use it to investigate pairwise relationship and dependencies between KPIs and supporting metrics. {\textcopyright} 2015 IEEE.",

keywords = "ISO 22400, Key performance indicator (KPI), manufacturing operation management",

author = "Ningxuan Kang and Cong Zhao and Jingshan Li and Horst, \{John A.\}",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; 3rd IEEE International Conference on Big Data, Big Data 2015 ; Conference date: 29-10-2015 Through 01-11-2015",

year = "2015",

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day = "22",

doi = "10.1109/BigData.2015.7364078",

language = "English",

isbn = "9781479999255",

series = "Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015",

publisher = "IEEE",

pages = "2767--2770",

booktitle = "Proceedings - 2015 IEEE International Conference on Big Data, Big Data 2015",

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url = "https://ieeexplore.ieee.org/xpl/conhome/7347101/proceeding",

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Kang, N, Zhao, C, Li, J & Horst, JA 2015, Analysis of key operation performance data in manufacturing systems. in Proceedings - 2015 IEEE International Conference on Big Data, Big Data 2015., 7364078, Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015, IEEE, pp. 2767-2770, 3rd IEEE International Conference on Big Data, Big Data 2015, Santa Clara, California, United States, 29/10/15. https://doi.org/10.1109/BigData.2015.7364078

Analysis of key operation performance data in manufacturing systems. / Kang, Ningxuan; Zhao, Cong; Li, Jingshan et al.
Proceedings - 2015 IEEE International Conference on Big Data, Big Data 2015. IEEE, 2015. p. 2767-2770 7364078 (Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Analysis of key operation performance data in manufacturing systems

AU - Kang, Ningxuan

AU - Zhao, Cong

AU - Li, Jingshan

AU - Horst, John A.

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2015/12/22

Y1 - 2015/12/22

N2 - In modern manufacturing systems, the key performance indicators (KPIs) are defined as a set of metrics to reflect operation performance. The KPIs are not independent and may have intrinsic mutual relationships. In this paper, by categorizing such data into basic and comprehensive KPIs and supporting metrics, we introduce a hierarchical structure for identification and analysis of KPIs in manufacturing operation management, and use it to investigate pairwise relationship and dependencies between KPIs and supporting metrics. © 2015 IEEE.

AB - In modern manufacturing systems, the key performance indicators (KPIs) are defined as a set of metrics to reflect operation performance. The KPIs are not independent and may have intrinsic mutual relationships. In this paper, by categorizing such data into basic and comprehensive KPIs and supporting metrics, we introduce a hierarchical structure for identification and analysis of KPIs in manufacturing operation management, and use it to investigate pairwise relationship and dependencies between KPIs and supporting metrics. © 2015 IEEE.

KW - ISO 22400

KW - Key performance indicator (KPI)

KW - manufacturing operation management

UR - https://www.scopus.com/pages/publications/84963785911

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84963785911&origin=recordpage

U2 - 10.1109/BigData.2015.7364078

DO - 10.1109/BigData.2015.7364078

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781479999255

T3 - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

SP - 2767

EP - 2770

BT - Proceedings - 2015 IEEE International Conference on Big Data, Big Data 2015

PB - IEEE

T2 - 3rd IEEE International Conference on Big Data, Big Data 2015

Y2 - 29 October 2015 through 1 November 2015

ER -

Analysis of key operation performance data in manufacturing systems

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Research Keywords

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