AI-Driven Predictive Prognostics for Automotive Service: A Machine Learning Framework for Pre-Arrival Parts Ordering
Keywords:
Predictive Prognostics, Connected Vehicle Telematics, Pre-Arrival Parts Ordering, Machine Learning Service Optimization, Automotive Maintenance IntelligenceAbstract
The proliferation of telematics in connected vehicles enables continuous monitoring of vehicle componentconditions. This enables car manufacturers and dealerships to use predictive prognosis methods rather than repair-and-replace methods.
References
Fabio Arena et al., "Predictive Maintenance in the Automotive Sector: A Literature Review," MDPI, Dec. 2021. https://www.mdpi.com/2297-8747/27/1/2
Panagiotis Mallioris et al., "Predictive maintenance in Industry 4.0: A systematic multi-sector mapping," CIRP Journal of Manufacturing Science and Technology, ScienceDirect, Feb. 2024. https://www.sciencedirect.com/science/article/pii/S1755581724000221
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Published
2026-03-25
How to Cite
Ancilia Anthony Dmello. (2026). AI-Driven Predictive Prognostics for Automotive Service: A Machine Learning Framework for Pre-Arrival Parts Ordering . Journal of Computational Analysis and Applications (JoCAAA), 35(3), 557–573. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/5217
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