Analysis of Critical Factors for Automatic Measurement of OEE

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• 作者：Richard Hedman^a ; ^{richard.hedman@chalmers.se} ; Mukund Subramaniyan^b ; Peter Almströ ; m^a
• 关键词：Overall equipment efficiency ; operator influence ; loss classification ; performance measurement
• 刊名：Procedia CIRP
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：57
• 期：Complete
• 页码：128-133
• 全文大小：392 K
• 卷排序：57

文摘

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.