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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Bottlenecks in a Multi-Variant Production Setting: Detection, Analysis, Visualization and Prediction. |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2021 |
| Abstract Text | In this digital age, manufacturing companies generate large volumes of machine data that can be used to make data driven decisions using computerized algorithms. It is well-known that the productivity of any production line is constrained by throughput bottlenecks. These bottlenecks if detected at the right time can allow maintenance engineers to proactively plan resources to e ectively mitigate such bottlenecks and achieve a higher throughput across the production line. In order to provide support to such an exercise, this masters thesis dealt with building a bottleneck detection model using real-world production data, implementing a working formulation of the detection model on a real production line, further analysing the nature of the observed bottlenecks, and using machine learning algorithms to predict future bottlenecks in the production line. A detection mechanism was build to compare the di erence between consecutive products arriving at all machines with their takt time (threshold time). Through a practical demonstration, the said framework was used to detect not just the bottlenecks but also its severity. For predicting future bottlenecks on production machines, a threshold mechanism was developed to predict if the next time step will be a bottleneck or not. In order to do this, four machine learning algorithms were used to learn their distribution, both on single-step and multi-step (auto-regressive) frameworks for all machines. All models were implemented on a validation set and the model with the lowest MSE score was used for prediction on the test data at each machine respectively. On the single-step framework, we observed an overall accuracy score of 76.7%, while an accuracy of 90.4% was observed on the multi-step framework. |
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