The application of real-time overall equipment efficiency indicator in a medium-sized company




OEE, IoT, Industry 4.0, Efficiency, Big Data


Goal: This research investigated the application of real-time Overall Equipment Efficiency (OEE) at three assembly work centers in a medium-sized company. The objective was to demonstrate the feasibility of integrating Industry 4.0 technologies, such as the Internet of Things, Big Data, and Cloud Computing, in manual work center environments. It aimed to underscore the potential improvements achievable through data-driven actions facilitated by Industry 4.0 technologies, while emphasizing the significance of acquiring real-time OEE data.

Design / Methodology / Approach: The research involved theoretical exploration, implementation, data collection (Nov 2022–May 2023), and analysis on assembly workstations in a medium-sized Brazilian eyewear manufacturer.

Results: Based on the captured data, the factory implemented a series of corrective actions, leading to a reduction in unplanned stops. The obtained results were significant, as the average efficiency of the studied work centers improved by 12.3% in 7 months, with an increase in performance and in availability.

Limitations of the investigation: The analysis faces challenges due time constraints, potentially limiting the full assessment of IoT impact. Seasonal variations in eyeglass production and style-specific demand complicate evaluating the true benefits of Industry 4.0 tools, making effective OEE improvement hard to determine.

Practical implications: The study demonstrates a method to gauge manual labor efficiency through Industry 4.0 technologies.

Originality / Value: This study shows how Industry 4.0 technologies (IoT, Big Data, Cloud Computing) can be integrated into manual workforces, enhancing efficiency and providing real-time OEE for workers to self-assess.


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How to Cite

Novochadlo, Y. M., & Paladini, E. P. (2024). The application of real-time overall equipment efficiency indicator in a medium-sized company. Brazilian Journal of Operations & Production Management, 21(2), 2042 .



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