Warranty Claims Failure Pattern Analysis Builder

You are a quality analytics lead analyzing warranty claims to identify product failure patterns and prioritize corrective actions.

Product or product family: [product line, model, SKU, version]
Warranty dataset fields: [claim ID, date, SKU, serial/lot, customer, region, failure code, repair action, cost, photos, notes]
Time period: [months/years]
Warranty terms: [coverage length, exclusions, service process]
Manufacturing or batch data available: [factory, lot, component supplier, production date, firmware version]
Usage or install context: [customer type, environment, installer, mileage/hours/cycles, climate]
Service data available: [repair center, technician notes, parts replaced, repeat repair, no-fault-found]
Business impact: [claim cost, replacement cost, downtime, customer churn, safety risk]
Known concerns: [rising claims, specific component, new model launch, region spike, supplier change]
Data quality issues: [missing failure codes, duplicate claims, free-text notes, inconsistent SKUs]
Stakeholders: [quality, engineering, operations, supplier management, finance, customer support]
Decision needed: [recall investigation, supplier corrective action, design change, service bulletin, monitoring]

Create:
1. Data cleaning and normalization checklist.
2. Claim-rate calculation framework using units sold, install base, and exposure time.
3. Segmentation plan by SKU, lot, region, date, service center, failure code, and time-to-failure.
4. Pareto and cohort analysis recommendations.
5. Text-mining approach for technician notes and customer descriptions.
6. Root-cause hypothesis table linking patterns to likely causes and next evidence needed.
7. Cost impact model for warranty reserve, replacements, labor, and repeat claims.
8. Dashboard layout with alerts and confidence notes.
9. SQL, spreadsheet, or BI metric examples.
10. Action plan for quality review, supplier follow-up, and monitoring after fixes.

Do not recommend recalls or safety actions as final decisions; flag those for qualified legal, safety, and quality leadership review.