Development of a Visualization Framework for Automotive Warranty Data Management
DOI:
https://doi.org/10.24191/jcrinn.v11i1.624Keywords:
Artificial Intelligence, AI, Automotive Aftermarket, predictive analytics, warranty managementAbstract
Automotive warranty information represents a critical yet often underutilized resource for evaluating product dependability and customer satisfaction. Despite the advancement of digital analytics, recurring challenges persist, such as fragmented data structures, inconsistent records, and delays in claim processing. This research presents an intelligent visualization framework that converts complex warranty datasets into clear and interpretable insights to support manufacturers in data-driven decision-making. The framework was developed through a structured three-phase approach comprising user requirement identification, interface accessibility refinement, and system integration. It features interactive dashboards for continuous monitoring of cost behaviour, claim frequency, and component performance. Validation using five years of industrial warranty data demonstrated significant improvements in analytical efficiency, trend identification, and visualization accuracy when compared with traditional spreadsheet analysis. The system’s human-centred design effectively connects technical data to managerial action, encouraging proactive quality enhancement, optimized warranty cost control, and improved customer confidence. Overall, this study highlights how visual analytics tools such as Microsoft Power BI can transform conventional warranty management into a more responsive and intelligent process aligned with modern automotive quality practices.Downloads
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