Warranty claim records hold clues about product failures, but those clues are often buried in large volumes of historical data. To let them influence the next generation of products, you need a complete process that runs from claim intake, data integration, and root-cause analysis through to feedback to design and manufacturing.

I. Four Steps from Claim to Forecast

The following summarizes the end-to-end process described in a warranty analytics solution brief.

II. Integrate Scattered Data First

The quality of the analysis depends on how complete the data is. Warranty solutions generally stress combining manufacturing, product, customer geographic information, call center, and work order data into a single view. One solution brief lists even broader sources, including customer complaints, sales reports, maintenance records, and repair technicians' notes.

Once the data is centralized, it is managed by the unit responsible for warranty, but authorized staff in product engineering, manufacturing, procurement, legal, and finance can also access it. This is the precondition for feedback to reach design and manufacturing: different departments look at the same data.

III. Find the Links Between Failures and Processes, Suppliers, and Usage

The focus of the analysis is to find correlations between reported failures and other factors, including process history, who supplied the part, which plant produced it, and how consumers use it. Laboratory testing helps, but the real causes of product failures often require advanced analysis of long-term data to reveal hidden trends.

A software vendor's description notes that field data analysis often has to deal with seasonality, immature products, reporting delays, usage intensity, and missing sales data, and it offers prebuilt calculations and drill-down analysis to identify likely causes and affected populations; the system can also automatically prioritize alerts by their impact on customers. These are features as described by the vendors themselves.

IV. Send the Conclusions to Design, Manufacturing, and Suppliers

Once the cause is found, the conclusions must reach the people who can act on them. Based on the different sources, feedback goes in several directions:

• Design: Incorporate the claim analysis results into the planning and design of the next-generation product so that the most meaningful improvements are implemented first. The results can also serve as reference data for engineering lifecycle systems, helping prevent defects and manage changes.

• Manufacturing: The analysis results can update records in systems such as product data management, manufacturing process planning, and supply chain management.

• Suppliers: If a failure can be attributed to a part provided by a supplier, it is recommended to integrate with the supplier's systems to share engineering data, analysis, and test results, and to co-design when necessary; where the contract provides for it, claim costs can also be recovered based on the identified parts and suppliers.

• Warranty terms: The same analysis can also help determine appropriate warranty periods and usage conditions, and estimate the cost of different warranty approaches.

Conclusion

For warranty data to feed back into design and manufacturing, the prerequisites are data integration and a clearly responsible unit. From creating a claim to forecasting scenarios, every step accumulates the evidence needed for the next improvement; the more complete the evidence, the better the design and manufacturing teams can judge what to change first.

Sources:

https://www.redbooks.ibm.com/abstracts/tips1254.html

https://www.sas.com/en_ca/industry/manufacturing/solution/warranty-cost-reduction.html