Abstract
Product failures are dreaded by manufacturers for the associated costs and resulting damage to their public image. While most defects can be traced back to decisions early in the design process they are often not discovered until much later during quality checks or, at worst, by the customer. We propose a machine learning-based system that automatically feeds back insights about failure rates from the quality assurance and return processes into the design process, without the need for any manual data analysis. As we show in a case study, this system helps to assure product quality in a preventive way.
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Acknowledgments
This work is based on Preventive Quality Assurance, a project partly funded by the German ministry of education and research (BMBF), reference number 01S17012D. The authors are responsible for the publication’s content.
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Nalbach, O., Linn, C., Derouet, M., Werth, D. (2018). Predictive Quality: Towards a New Understanding of Quality Assurance Using Machine Learning Tools. In: Abramowicz, W., Paschke, A. (eds) Business Information Systems. BIS 2018. Lecture Notes in Business Information Processing, vol 320. Springer, Cham. https://doi.org/10.1007/978-3-319-93931-5_3
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DOI: https://doi.org/10.1007/978-3-319-93931-5_3
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