In today’s competitive landscape, industries are relying heavily on the use of warranty data analytics techniques to manage and improve warranty performance. Warranty analytics is important since it provides valuable insights into product quality and reliability. It must be noted here that by systematically looking into warranty claims and related information, industries can identify patterns and trends that indicate potential issues with the products. This analysis helps in early detection of defects, enabling timely corrective actions that improve product performance and customer satisfaction. This paper introduces a comprehensive framework that combines conventional methods with advanced machine learning techniques to provide a multifaceted perspective on warranty data. The methodology leverages historical warranty claims and product usage data to predict failure patterns identify root causes. By integrating these diverse methods, the framework offers a more accurate and holistic understanding of warranty data, enabling manufacturers to make efficient data-driven decisions that improve product quality and customer satisfaction. Conventional tools, like statistical analysis and basic data mining, provide foundational insights into warranty trends. However, advanced techniques such as machine learning and predictive analytics enable detection of potential issues in a more efficient manner. This approach supports decision-making processes related to product design, manufacturing, and quality control, leading to overall improvements in product reliability. In this paper we will illustrate the application and effectiveness of this hybrid approach using sample data, highlighting significant advantages such as deeper insights into failure mechanisms which in turn helps in more effective warranty management strategies in combination with the conventional analytics techniques. Ultimately, this paper underscores the potential of a comprehensive warranty data analytics system that integrates both traditional and modern techniques, efficiently driving continuous improvement in product development and customer services.
Quadri et al. (Thu,) studied this question.