Warranty management has become a complex problem for companies, with increasing costs and limited resources.
- Fault diagnosis is typically manual, which can lead to human error and time-consuming processing times.
- Nature of faults and causes are often difficult to understand, leading to inaccurate predictions and actions.
Machine Learning Options
Several machine learning techniques can be applied to warranty management, including:
- Supervised learning: trains models on labeled data to predict faults and causes.
- Unsupervised learning: identifies patterns in unlabeled data without prior knowledge of the problem.
- Deep learning: utilizes neural networks to analyze complex fault patterns.
Machine Learning Applications
- Fault diagnosis and prediction using machine learning models.
- Predictive maintenance scheduling based on historical data and anomalies.
- Causal analysis to understand the impact of external factors on warranty claims.