What question did this study set out to answer?

The central aim is to modernize warranty management processes by integrating AI with data visualization techniques.

Development of warranty management visualisation for automotive aftermarket with integration of AI

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Demonstrates improved warranty management in the automotive aftermarket, suggesting AI enhances insights and efficiency.

The central aim is to modernize warranty management processes by integrating AI with data visualization techniques.
Utilized 11,000 historical warranty claim records from OEM customers (2019-2023)
Employed a four-phase methodology: Information Collection, Concept Generation, Product Configuration, and Parametric Analysis
Developed a system architecture using Model View Controller design with SQL and Python backend
Implemented Weibull distribution modelling for failure prediction and Python-based anomaly detection algorithms
Created integrated dashboards for real-time monitoring of warranty metrics.
Achieved a warranty cost reduction of RM 23.5K, representing a 75% improvement over five years
Enhanced visibility and strategic foresight for manufacturers
Real-time monitoring of key performance indicators such as Warranty Claim Rate, Average Claim Cost, and Claim Resolution Time.

Abdul Hamid Abdul Hamid (2026) studied this question.

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