Analysis of Production and Failure Data in Automotive: From Raw Data to Predictive Modeling and Spare Parts

Cristiano Fragassa ()
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Cristiano Fragassa: Department of Industrial Engineering, University of Bologna, 40126 Bologna, Italy

Mathematics, 2024, vol. 12, issue 4, 1-19

Abstract: The present analysis examines extensive and consistent data from automotive production and service to assess reliability and predict failures in the case of an engine control device. It is based on statistical evaluation of production and lead times to determine vehicle sales. Mileages are integrated to establish the age of the vehicle fleet over time and to predict the censored data. Failure and censored times are merged in a multiple censored data and combined by the Kaplan-Meier estimator for survivals. The Weibull distribution is used as parametric reliability model and its parameters identified to assure precision in predictions (>95%). An average time to failure >80 years and a slightly increasing failure rate ensure a low risk. The study is based on real-world data from various sources, acknowledging that the data are not homogeneous, and it offers a comprehensive roadmap for processing this diverse raw data and evolving it into sophisticated predictive models. Furthermore, it provides insights from various perspectives, including those of the Original Equipment Manufacturer, Car Manufacturer, and Users.

Keywords: automotive; reliability data analysis; failure analysis; reliability engineering; part manufacturing; statistical modeling; Weibull distribution; lead time analysis (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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