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Analysis of Critical Factors for Automatic Measurement of OEE

Richard Hedman (Institutionen för teknikens ekonomi och organisation, Supply and Operations Management ) ; Mukund Subramaniyan (Institutionen för teknikens ekonomi och organisation, Supply and Operations Management ) ; Peter Almström (Institutionen för teknikens ekonomi och organisation, Supply and Operations Management )
Procedia CIRP, Factories of the Future in the digital environment - Proceedings of the 49th CIRP Conference on Manufacturing Systems (2212-8271). Vol. 57 (2016), p. 128-133.
[Konferensbidrag, refereegranskat]

The increasing digitalization of industry provides means to automatically acquire and analyze manufacturing data. As a consequence, companies are investing in Manufacturing Execution Systems (MES) where the measurement of Overall Equipment Effectiveness (OEE) often is a central part and important reason for the investment. The purpose of this study is to identify critical factors and potential pitfalls when operating automatic measurement of OEE. It is accomplished by analyzing raw data used for OEE calculation acquired from a large data set; 23 different companies and 884 machines. The average OEE was calculated to 65%. Almost half of the recorded OEE losses could not be classified since the loss categories were either lacking or had poor descriptions. In addition, 90% of the stop time that was classified could be directly related to supporting activities performed by operators and not the automatic process itself. The findings and recommendations of this study can be incorporated to fully utilize the potential of automatic data acquisition systems and to derive accurate OEE measures that can be used to improve manufacturing performance.

Nyckelord: Overall equipment efficiency; operator influence; loss classification; performance measurement



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Denna post skapades 2017-01-13. Senast ändrad 2017-09-04.
CPL Pubid: 246913

 

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