Optimising industrial efficiency: integrating K-Means clustering and data Science for sustainable manufacturing and waste Reduction
This study investigates the practical application of K-means clustering analysis within industrial settings to optimise machine performance and operational efficiency. By collecting data every minute from 34 machines within a multinational company, we constructed an extensive time-series database. T...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Sustainable Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19397038.2025.2527300 |
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| Summary: | This study investigates the practical application of K-means clustering analysis within industrial settings to optimise machine performance and operational efficiency. By collecting data every minute from 34 machines within a multinational company, we constructed an extensive time-series database. This database facilitated the classification of machine operations into five distinct speed classes, allowing us to meticulously analyse the time machines spent in each class to evaluate their operational efficiency. Through this analytical approach, we identified optimal performance levels, thereby enhancing managerial decision-making. The results highlight the significant benefits of employing advanced data analytics to refine industrial operations, contributing valuable insights to both the fields of industrial engineering and management. The use of sophisticated data analytics not only fosters academic advancement but also acts as a potent tool for industry application, underscoring its essential role in evolving manufacturing practices towards greater sustainability and waste reduction. |
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| ISSN: | 1939-7038 1939-7046 |