How SAS® Enables Warranty Cost Reduction
SAS provides warranty cost reduction capabilities that enable you to integrate and decode data from warranties, sales, call centers, engineers, technicians and more. Identify emerging issues, see early-warning indicators and identify root causes. Then send prioritized alerts to the appropriate people.
- Automatically analyze millions of combinations of products, components and failures with early-warning algorithms.
- Quickly surface emerging issues, uncover potential problems and determine root causes.
- Use advanced analytics to detect and send alerts about increases in warranty claim rates for particular parts.
- Integrate and analyze all types of structured and unstructured data to uncover valuable information hidden in warranty claims and service reports.
- Get real-time customer feedback from external and internal sources to gain product design suggestions and early warning of warranty and recall issues.
- Detect issues that annoy customers but haven't become formal complaints so you can address them before they erode your brand equity and competitive advantage.
- Capture valuable insights from connected devices to enhance service offerings and reliability.
- Provide real-time notifications with analytics at the edge.
- Link real-time product performance to warranty claims.
How does a large automobile company turn service repair data into cost savings?
SAS® helped American Honda Motor Co.:
- Improve warranty claims and forecast usage for parts and services.
- Empower dealers to understand the appropriate warranty processes by providing them with useful information via an online report.
- Create a proprietary process to surface suspicious warranty claims for scrutiny on a daily basis to make sure they are in compliance with existing guidelines.
- Reduce labor costs for 52% of its available labor codes through more complete analysis of warranty claims and more education at the dealerships.
How does a high-tech Japanese company use machine learning to reduce costs and enhance its competitive edge?
SAS® helped Konica Minolta Japan:
- Create and deploy multiple analytical models to improve areas as diverse as malfunction forecasts and optimization of management.
- Attain processing speeds and strong forecasting accuracy at a level that could withstand use in its business environment.
- Speed up its plan-do-check-act (PDCA) cycle and enable global expansion of the data.
- Optimize consumable parts replacement using forecasts attained through machine learning, using data from multifunction devices and other external data.
- Forecast the life of parts based on the status of use and send service personnel to the customer before a problem develops.
How does a Swedish truck manufacturer use sensor data and AI technology to reduce unplanned downtime and keep trucks on the road?
SAS helped Volvo Trucks:
- Maximize customers' vehicles' time on the road and minimize the costs of service disruptions by servicing connected vehicles more efficiently, accurately and proactively.
- Monitor data from each truck for fault codes triggered when something is amiss with one of the vehicle’s major systems, such as the engine, aftertreatment or transmission.
- Quickly diagnose faults and their severity with detailed information and a recommended action plan for addressing it with the least disturbance.