Dealing with missing usage data in defect prediction: A case study of a welding supplier

Milot Gashi*, Patrick Ofner, Helmut Ennsbrunner, Stefan Thalmann

*Korrespondierende/r Autor/in für diese Arbeit

Publikation: Beitrag in einer FachzeitschriftArtikel

Abstract

End-of-line (EoL) testing is performed to determine product quality by ensuring reliable performance. Even though low-quality products may pass EoL testing, they have a high probability of failure over time. Analyzing product usage data can help to improve EoL testing in this regard. However, current approaches do not consider usage data for this purpose. The major challenge for manufacturers is that they do not have access to comprehensive usage data for their products because customers are unwilling to provide usage data. However, manufacturers obtain some usage data from their sales and service departments i.e., contextual data. In this paper, we introduce an alternative approach to improving EoL testing when usage data from customers are missing. We discuss whether it is possible to predict low-quality products from EoL testing data when only contextual information is available (i.e., historical service data and location data of shipped products). We find that a simple, duration-based product usage threshold is sufficient to separate products affected by the production process (low-quality products) from those affected primarily by usage and environmental factors (long-term influence). Low-quality products could only be predicted by combining EoL data and contextual data. Additionally, we identify frequent patterns of maintained components to tackle the challenge of having limited data and promote user acceptance of our predictive model. Finally, we demonstrate our approach by conducting a case study in the welding industry. Our approach can identify frequent component failures and improve product reliability in countries with varying environmental conditions and rates of product usage. We expect that our findings will improve EoL testing protocols in welding and other industries while improving defect prediction models in general.

Originalspracheenglisch
Aufsatznummer103505
Seitenumfang10
FachzeitschriftComputers in Industry
Jahrgang132
DOIs
PublikationsstatusVeröffentlicht - Nov 2021

ASJC Scopus subject areas

  • !!Engineering(all)
  • !!Computer Science(all)

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