Assembly Line Overall Equipment Effectiveness (OEE) Prediction from Human Estimation to Supervised Machine Learning

Nowadays, in the domain of production logistics, one most complex planning processes is accurate forecasting and assembly efficiency. In industrial companies, Overall Equipment Effectiveness (OEE) common used efficiency measures at semi-automatic lines. Proper estimation supports right use resources more cost-effective delivery to customers. This paper presents prediction OEE by comparing human with techniques supervised machine learning through a real-life example. addition descriptive statistics, takt time-based decision trees are applied target-oriented model presented. concept takes into account recent data line targets different weights. Using model, value can be predicted an accuracy within 1% on weekly basis, four weeks advance.