期刊
3RD INTERNATIONAL CONFERENCE ON INDUSTRY 4.0 AND SMART MANUFACTURING
卷 200, 期 -, 页码 1145-1154出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.procs.2022.01.314
关键词
data-driven discrete-event simulation; bottleneck prediction; machine learning; manufacturing lines
资金
- Portugal 2020, under the Competitiveness and Internationalization Operational Program [POCI-01-0247-FEDER-046103]
- Lisbon Regional Operational Program [POCI-01-0247-FEDER-046103]
- European Regional Development Fund [POCI-01-0247-FEDER-046103]
- Center for Research and Development in Mathematics and Applications (CIDMA), through the Portuguese Foundation for Science and Technology [UIDB/04106/2020]
- Bosch Termotecnologia
The study implemented a data-driven discrete-event simulator to analyze the production line at Bosch Thermotechnology. It found that certain scenarios perceived to increase output may actually decrease production metrics, highlighting the importance of line injection rates, and proposed the need for suitable real-time bottleneck forecasting tools.
Bottleneck identification is a relevant tool for continuous optimization of production lines. In this work, we implement a data-driven discrete-event simulator (DDS) based on experimental distributions, obtained from real historical data. The DDS allows to analyse the behavior of a balanced manufacturing line at Bosch Thermotechnology, under different hypotheses. It shows that some scenarios perceived as likely to increase output may actually decrease production metrics, reveals the importance of line injection rates, and leads to the need for adequate real time bottleneck forecasting tools, which allow shift managers intervention in a useful time frame. Eleven prediction models are tested, where a random forest and a multi-layer perceptron attain the best performances (above 95% in all metrics). This data flow is operationalized through a micro-services pipeline which is briefly discussed. (C) 2022 The Authors. Published by Elsevier B.V.
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