Thematic authors relating AL/ML to OEE and operational efficiency
| Authors | Themes |
|---|---|
| Antosz et al. (2020) | AI tools improve the effectiveness of lean maintenance, enabling manufacturers to achieve higher operational reliability and efficiency |
| de Souza et al. (2022) | Lean and agile integration reduces waste and accelerates demand response |
| Dobra and Josvai (2022) | Decision tree models analysed show better performance than human predictions, with error rates below 1%, leading to significant improvements in planning results |
| Dobra and Josvai (2023) | Predicting changes in product variability enables reliable operational consistency across planning cycles |
| Legat et al. (2024) | Enhances fault tolerance, reduces system downtime, and improves adaptability, resulting in more resilient operations |
| Lucantoni et al. (2024) | The targeted anomaly-resolution strategy analysed achieves a significant performance improvement, providing evidence of an advancement in system responsiveness |
| Mohan et al. (2023) | Predictive maintenance using Long Short-Term Memory technology reduces downtime to 95%, thereby improving equipment efficiency and performance metrics |
| Carvalho et al. (2019) | Broader reviews of machine learning for predictive maintenance confirm these trends and highlight the wide applicability across industries |
| Authors | Themes |
|---|---|
| AI tools improve the effectiveness of lean maintenance, enabling manufacturers to achieve higher operational reliability and efficiency | |
| Lean and agile integration reduces waste and accelerates demand response | |
| Decision tree models analysed show better performance than human predictions, with error rates below 1%, leading to significant improvements in planning results | |
| Predicting changes in product variability enables reliable operational consistency across planning cycles | |
| Enhances fault tolerance, reduces system downtime, and improves adaptability, resulting in more resilient operations | |
| The targeted anomaly-resolution strategy analysed achieves a significant performance improvement, providing evidence of an advancement in system responsiveness | |
| Predictive maintenance using Long Short-Term Memory technology reduces downtime to 95%, thereby improving equipment efficiency and performance metrics | |
| Broader reviews of machine learning for predictive maintenance confirm these trends and highlight the wide applicability across industries |
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