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The global demand for effective utilization of both humans and machinery is increasing due to wastage incurred during product manufacturing. Excessive waste generation has made entrepreneurs find it difficult to breakeven. The development of dynamic error-proof Overall Equipment Effectiveness (OEE) model for optimizing a complex production process is targeted at minimizing/eradicating operational wastes/losses. In this study, the error-proof sigma metric was integrated into the extended traditional OEE factors (availability, performance, quality) to include losses due to waste and man-machine relationships. Error-proof sigma statistics enabled continuous corrective measures on unsatisfactory or low-level OEE resulted from process output variations (quantity delivered or expected), which were mapped into sigma statistical standards (one- to six-sigma).  Application of the model in a processing company showed that errors of the process were reduced by 78% and 42% respectively for traditional OEE and the new Error-Proof OEE (OEE-EP).  The results revealed that the OEE-EP model is better than the other existing schemes in terms of losses elimination in the production process.


OEE dynamism Sigma metric process integration productivity

Article Details

Author Biographies

Buliaminu Kareem, Federal University of Technology Akure

Professor, Industrial and Production Engineering

Adefowope S. Alabi, Federal University of Technology Akure

Research fellow, Dept. of Industrial and Production Engineering

T I. Ogedengbe, The Federal University of Technology Akure

Associate Professor, Mechanical Engineering Dept.

B. O. Akinnuli, The Federal University of Technology, Akure

Associate Professor, Dept. of Industrial and Production Engineering

O. A. Aderoba, Elizade University, Ilara Mokin

Lecturer I, Mechanical and Mechatronics Engineering dept.
How to Cite
Kareem, B., Alabi, A. S., Ogedengbe, T. I., Akinnuli, B. O., & Aderoba, O. A. (2020). Development of OEE Error-Proof (OEE-EP) Model for Production Process Improvement. The Journal of Engineering Research [TJER], 17(2), 59–74. Retrieved from


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