@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",
}