Analysis of Critical Factors for Automatic Measurement of OEE
Paper in proceedings, 2016

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.

Overall equipment efficiency

performance measurement

loss classification

operator influence

Author

Richard Hedman

Chalmers, Technology Management and Economics, Supply and Operations Management

Mukund Subramaniyan

Chalmers, Technology Management and Economics, Supply and Operations Management

Peter Almström

Chalmers, Technology Management and Economics, Supply and Operations Management

Procedia CIRP

22128271 (ISSN)

Vol. 57 128-133

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Areas of Advance

Production

DOI

10.1016/j.procir.2016.11.023

More information

Created

10/7/2017