Bottleneck Identification using Data Analytics to Increase Production Capacity

Abstract: The thesis work develops an automated, data-driven bottleneck detection procedure based on real-world data. Following a seven-step process it is possible to determine the average as well as the shifting bottleneck by automatically applying the active period method. A detailed explanation of how to pre-process the extracted data is presented which is a good guideline for other analysists to customize the available code according to their needs. The obtained results show a deviation between the expected bottleneck and the bottleneck calculated based on production data collected in one week of full production. The expected bottleneck is currently determined by the case company by measuring cycle times physically at the machine, but this procedure does not represent the whole picture of the production line and is therefore recommended to be replaced by the developed automated analysis. Based on the analysis results, different optimization potentials are elaborated and explained to improve the data quality as well as the overall production capacity of the investigated production line. Especially, the installed gantry systems need further analysis to decrease their impact on the overall capacity. As for data quality, especially, the improvement of the machines data itself as well as the standardization of timestamps should be focused to enable better analysis in the future. Finally, future recommendations mainly suggest to run the analysis several times with new data sets to validate the results and improve the overall understanding of the production lines behavior.