A Machine Learning Framework for Failure Mode Identification from Warranty Data

Warranty reporting system is a goldmine of data on issues reported by customers regarding product problems. Having a customer and dealer feedback allows the OEMs to gather valuable insights from regarding product performance and issues. By systematically storing and analyzing the dealer comments, an OEM can identify recurring problems, understand failure modes, and enhance product reliability. This data-driven approach not only helps in improving product design and quality but also strengthens the commitment to customer satisfaction and continuous improvement. This paper presents a machine learning-based approach to identify Failure Modes from warranty data. Warranty data, a rich source of information, is often underutilized due to its unstructured nature. Our approach leverages natural language processing (NLP) and supervised learning techniques to analyze warranty claims, identifying common failure modes and their associated effects and causes specifically for systems, sub systems and components. By preprocessing text data and employing Machine Learning algorithms, we extract meaningful patterns and insights. Our results demonstrate that ML can significantly reduce the time and effort required for warranty data analysis, leading to improved insights on product reliability and customer satisfaction. By leveraging natural language processing (NLP) and supervised learning techniques, the approach outlined in the paper enhances the identification of failure modes, particularly for systems, subsystems, and components. As noted, the superior performance of the Support Vector Classifier (SVC) model, especially in System 4, highlights its accuracy and reliability in classifying dealer comments. This indicates that the choice of ML algorithm plays a crucial role in achieving precise and effective results. The “RESULTS” section provides concrete evidence of the accuracy improvements achieved by SVC, which outperformed other algorithms like Random Forest, Naive Bayes, and Logistic Regression as depicted in Figure 3, showcasing comparative analysis of the accuracy achieved by different machine learning models across six systems. For instance, System 4 achieved the highest accuracy of 83.2%, which underscores SVC's strength in handling structured datasets derived from unstructured warranty data. This data transformation is essential, as it allows the ML models to process and categorize the complex information within dealer comments more efficiently. In conclusion, the application of ML significantly reduces the time and effort required for warranty data analysis, delivering critical insights into product reliability and customer satisfaction. The findings support the importance of proper data structuring and algorithm selection, with SVC emerging as a key tool in achieving optimal results in warranty claim analysis.