ML &amp; AI Application for the Automotive Industry

Fernández-López, Antía; Fernández-Castro, Bruno; García-Coego, Daniel

doi:10.1007/978-3-030-91006-8_4

Cited by 1 publication

(1 citation statement)

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“…Despite limited research on predicting customer complaints from production and repair data for industrial processes, this study leverages insights from related fields. Prior studies underscored the role of ML and AI in the automotive industry (Fernández-López et al. , 2022), the significance of ML in predicting and analysing customer complaints (Alarifi et al.…”

Section: Resultsmentioning

confidence: 99%

Enhancing quality 4.0 and reducing costs in lot-release process with machine learning-based complaint prediction

Lobo,

Sampaio,

Novais

2024

TQM

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PurposeThis study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.Design/methodology/approachThis study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.FindingsThe framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.Practical implicationsThe framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.Originality/valueTo the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

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Section: Resultsmentioning

confidence: 99%