Warranty management is a complex process that involves tracking and analyzing various warranty claims to identify patterns and trends.
Machine learning (ML) can be effectively applied to warranty management by leveraging its ability to process large amounts of data quickly and accurately. By training ML models on historical warranty claims data, businesses can gain insights into the types of issues that are most common and develop targeted solutions to prevent similar problems in the future.
One of the key benefits of using ML in warranty management is its ability to reduce false positives and negatives. By analyzing patterns in warranty claim data, ML models can identify high-risk claims and flag them for manual review, reducing the likelihood of issuing unnecessary repairs or replacements.
Martin Krizal, a researcher at IBM Watson Health, has explored the use of ML in warranty management in his 2021 blog post.