Warranty management is a critical function that requires timely and accurate decision-making to minimize losses. With the increasing complexity of warranty claims, traditional manual processes are no longer effective in meeting these demands.
Historically, warranty management has relied heavily on manual data entry and analysis, which can lead to errors and delays. This approach is often time-consuming and resource-intensive, making it difficult to scale with increasing volumes of claims.
In recent years, machine learning (ML) has emerged as a promising solution for improving warranty management. By leveraging ML algorithms and data analytics, companies can identify patterns and anomalies in warranty claims data, enabling more efficient and effective decision-making.
Machine learning-based approaches to warranty management can bring several benefits, including:
For more information on how machine learning is being used in warranty management, refer to the blog post by Stephen Crenshaw at IBM's Community Blog: https://community.ibm.com/community/user/blogs/stephen-crenshaw/2021/08/28/machine-learning-in-warranty-management