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Аналитика в промышленности

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Warranty Cost Reduction

Minimize risk and improve profitability.

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.      

Early-warning signals

  • 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.

Perceptual quality

  • 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.

Connected Service

  • 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.   

Why SAS® for warranty cost reduction?

Achieve exceptional customer service and a positive brand image. Gain control over service costs. And take a smarter approach to tackling service issues.    

Detect and resolve field quality issues faster

Analyze warranty claims data in concert with production, sales and telematic data for a single, consolidated view. Then apply machine learning to automatically detect emerging issues faster – before they have a significant impact on performance.

Prioritize issues based on business impact

Combine warranty data with financial or risk information. Use patented analytical models to isolate failure modes and identify their true root causes so you can make well-informed corrective action decisions. Use advanced forecasting and reliability techniques (including Weibull) to understand how issues will affect costs and customers so you can prioritize them and focus resources where they will have the greatest impact.

Increase customer satisfaction and brand equity

 Drive measurable improvements in total customer experience metrics by delighting your customers, who will reward you with repeat buys and recommend your products and services to others.

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.
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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.
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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.
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