Data-Driven Failure Management: An Ontology-Based Speech Recognition App for Failure Capturing in Manufacturing Processes

Author:

Scharfe Philipp,Ludwig Heiner,Bley Katja,Wiener Martin,Schmidt Thorsten

Publisher

Springer Nature Switzerland

Reference29 articles.

1. Bokrantz, J., Skoogh, A., Ylipää, T., & Stahre, J. (2016). Handling of production disturbances in the manufacturing industry. Journal of Manufacturing Technology Management, 27(8), 1054–1075.

2. Bergers, T., Stauder, L., Grünbaum, T., & Barth, S. (2021). Adaptive design of manufacturing process sequences in case of short-term disruptions in the production process. Manufacturing Letters, 27, 92–95.

3. Servicemax. Accessed 2020/07/05, from https://www.servicemax.com/de/unplanned-downtime

4. Stich, V., Jordan, F., Birkmeier, M., Oflazgil, K., Reschken, J., & Diews, A. (2015). Big data technology for resilient failure management in production systems. In S. Umeda, M. Nakano, H. Mizuyama, N. Hibino, D. Kiritsis, & G. von Cieminski (Eds.), Advances in production management systems: Innovative production management towards sustainable growth. APMS 2015. IFIP advances in information and communication technology (Vol. 459, pp. 447–454). Springer.

5. Cwikla, C. (2014). Methods of manufacturing data acquisition for production management – A review. Advanced Materials Research, 837, 618–623.

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