Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root‐cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures.In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root‐cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications.The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root‐cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.

Kurzfassung Service-orientierte Geschäftsmodelle und virtuelle Produktentwicklung erfordern es, jederzeit den Zustand eines Produktes zu kennen. Neben der Bereitstellung dieser Daten über das Internet ist dazu ein instanziiertes Datenmanagement notwendig. Ein Digital Twin für jedes einzelne physische Produkt bildet dafür die konzeptionelle Grundlage. Dieser Beitrag beleuchtet den aktuellen Stand der Forschung im Bereich Digital Twin in unterschiedlichen Ausprägungen entlang des gesamten Produktlebenszyklus.

Mass customization and increasing product complexity require new methods to ensure a continuously high product quality. In the case of product failures it has to be determined what distinguishes flawed products. The data generated by cybertronic products over their lifecycle offers new possibilities to find such distinctions. To manage this data for individual product instances the concept of a Digital Twin has been proposed. This paper introduces the elements of a Digital Twin for root cause analysis and product quality monitoring and suggests a data structure that enables data analytics.

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