Generating operating curves in complex systems using machine learning

Birkan Can; Cathal Heavey; Kamil Erkan Kabak

doi:10.1109/WSC.2014.7020084

Generating operating curves in complex systems using machine learning

Birkan Can
, Cathal Heavey
, Kamil Erkan Kabak

School of Engineering

University of Limerick
Izmir Ekonomi University

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

Abstract

This paper proposes using data analytic tools to generate operating curves in complex systems. Operating curves are productivity tools that benchmark factory performance based on key metrics, cycle time and throughput. We apply a machine learning approach on the flow time data gathered from a manufacturing system to derive predictive functions for these metrics. To perform this, we investigate incorporation of detailed shop-floor data typically available from manufacturing execution systems. These functions are in explicit mathematical form and have the ability to predict the operating points and operating curves. Simulation of a real system from semiconductor manufacturing is used to demonstrate the proposed approach.

Original language	English
Title of host publication	Proceedings of the 2014 Winter Simulation Conference, WSC 2014
Editors	Andreas Tolk, Levent Yilmaz, Saikou Y. Diallo, Ilya O. Ryzhov
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2404-2413
Number of pages	10
ISBN (Electronic)	9781479974863
DOIs	https://doi.org/10.1109/WSC.2014.7020084
Publication status	Published - 23 Jan 2015
Event	2014 Winter Simulation Conference, WSC 2014 - Savannah, United States Duration: 7 Dec 2014 → 10 Dec 2014

Publication series

Name	Proceedings - Winter Simulation Conference
Volume	2015-January
ISSN (Print)	0891-7736

Conference

Conference	2014 Winter Simulation Conference, WSC 2014
Country/Territory	United States
City	Savannah
Period	7/12/14 → 10/12/14

UN SDGs

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

SDG 9 Industry, Innovation, and Infrastructure

Access to Document

10.1109/WSC.2014.7020084

Cite this

Can, B., Heavey, C., & Kabak, K. E. (2015). Generating operating curves in complex systems using machine learning. In A. Tolk, L. Yilmaz, S. Y. Diallo, & I. O. Ryzhov (Eds.), Proceedings of the 2014 Winter Simulation Conference, WSC 2014 (pp. 2404-2413). Article 7020084 (Proceedings - Winter Simulation Conference; Vol. 2015-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WSC.2014.7020084

Can, Birkan ; Heavey, Cathal ; Kabak, Kamil Erkan. / Generating operating curves in complex systems using machine learning. Proceedings of the 2014 Winter Simulation Conference, WSC 2014. editor / Andreas Tolk ; Levent Yilmaz ; Saikou Y. Diallo ; Ilya O. Ryzhov. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 2404-2413 (Proceedings - Winter Simulation Conference).

@inproceedings{f6ae7f9e91dc42c8967029e5cb2bc786,

title = "Generating operating curves in complex systems using machine learning",

abstract = "This paper proposes using data analytic tools to generate operating curves in complex systems. Operating curves are productivity tools that benchmark factory performance based on key metrics, cycle time and throughput. We apply a machine learning approach on the flow time data gathered from a manufacturing system to derive predictive functions for these metrics. To perform this, we investigate incorporation of detailed shop-floor data typically available from manufacturing execution systems. These functions are in explicit mathematical form and have the ability to predict the operating points and operating curves. Simulation of a real system from semiconductor manufacturing is used to demonstrate the proposed approach.",

author = "Birkan Can and Cathal Heavey and Kabak, \{Kamil Erkan\}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 Winter Simulation Conference, WSC 2014 ; Conference date: 07-12-2014 Through 10-12-2014",

year = "2015",

month = jan,

day = "23",

doi = "10.1109/WSC.2014.7020084",

language = "English",

series = "Proceedings - Winter Simulation Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2404--2413",

editor = "Andreas Tolk and Levent Yilmaz and Diallo, \{Saikou Y.\} and Ryzhov, \{Ilya O.\}",

booktitle = "Proceedings of the 2014 Winter Simulation Conference, WSC 2014",

}

Can, B, Heavey, C & Kabak, KE 2015, Generating operating curves in complex systems using machine learning. in A Tolk, L Yilmaz, SY Diallo & IO Ryzhov (eds), Proceedings of the 2014 Winter Simulation Conference, WSC 2014., 7020084, Proceedings - Winter Simulation Conference, vol. 2015-January, Institute of Electrical and Electronics Engineers Inc., pp. 2404-2413, 2014 Winter Simulation Conference, WSC 2014, Savannah, United States, 7/12/14. https://doi.org/10.1109/WSC.2014.7020084

Generating operating curves in complex systems using machine learning. / Can, Birkan; Heavey, Cathal; Kabak, Kamil Erkan.
Proceedings of the 2014 Winter Simulation Conference, WSC 2014. ed. / Andreas Tolk; Levent Yilmaz; Saikou Y. Diallo; Ilya O. Ryzhov. Institute of Electrical and Electronics Engineers Inc., 2015. p. 2404-2413 7020084 (Proceedings - Winter Simulation Conference; Vol. 2015-January).

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

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T1 - Generating operating curves in complex systems using machine learning

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AU - Heavey, Cathal

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N2 - This paper proposes using data analytic tools to generate operating curves in complex systems. Operating curves are productivity tools that benchmark factory performance based on key metrics, cycle time and throughput. We apply a machine learning approach on the flow time data gathered from a manufacturing system to derive predictive functions for these metrics. To perform this, we investigate incorporation of detailed shop-floor data typically available from manufacturing execution systems. These functions are in explicit mathematical form and have the ability to predict the operating points and operating curves. Simulation of a real system from semiconductor manufacturing is used to demonstrate the proposed approach.

AB - This paper proposes using data analytic tools to generate operating curves in complex systems. Operating curves are productivity tools that benchmark factory performance based on key metrics, cycle time and throughput. We apply a machine learning approach on the flow time data gathered from a manufacturing system to derive predictive functions for these metrics. To perform this, we investigate incorporation of detailed shop-floor data typically available from manufacturing execution systems. These functions are in explicit mathematical form and have the ability to predict the operating points and operating curves. Simulation of a real system from semiconductor manufacturing is used to demonstrate the proposed approach.

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DO - 10.1109/WSC.2014.7020084

M3 - Conference contribution

AN - SCOPUS:84940542411

T3 - Proceedings - Winter Simulation Conference

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EP - 2413

BT - Proceedings of the 2014 Winter Simulation Conference, WSC 2014

A2 - Tolk, Andreas

A2 - Yilmaz, Levent

A2 - Diallo, Saikou Y.

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 Winter Simulation Conference, WSC 2014

Y2 - 7 December 2014 through 10 December 2014

ER -

Can B, Heavey C, Kabak KE. Generating operating curves in complex systems using machine learning. In Tolk A, Yilmaz L, Diallo SY, Ryzhov IO, editors, Proceedings of the 2014 Winter Simulation Conference, WSC 2014. Institute of Electrical and Electronics Engineers Inc. 2015. p. 2404-2413. 7020084. (Proceedings - Winter Simulation Conference). doi: 10.1109/WSC.2014.7020084

Generating operating curves in complex systems using machine learning

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