A warranty claim lands in an OEM's inbox. A dealer has replaced a hydraulic pump on a two-year-old excavator. They have attached a service report, a photo of the failed component, and an invoice for parts and labour. Someone at the OEM needs to read it, verify the machine is under warranty, check whether the failure mode is covered, request any missing documentation, and approve or reject the claim.
That process takes, on average, 14 to 21 days. During that time the dealer has absorbed the repair cost. If the claim is rejected, the dealer absorbs it permanently. If the claim is approved slowly and inconsistently, the dealer learns to under-claim — submitting only the clearest, most defensible cases — and the OEM loses visibility into field quality problems that are showing up in the less clear-cut cases.
This is the state of warranty management at most heavy equipment OEMs. The system works but it is slow, expensive to run, and generates less insight than it should. Here is what modern warranty management looks like instead.
The four problems with manual warranty management
Speed destroys dealer trust
When a warranty claim takes three weeks to process, dealers stop submitting borderline claims. They absorb the cost themselves, adjust the invoice to the customer, or find a workaround. The OEM sees a clean warranty ledger but misses a significant portion of actual field failures — exactly the ones that are most valuable for quality improvement because they are the edge cases, the early indicators of systemic problems.
Speed also affects dealer relationship quality directly. In competitive dealer networks — where dealers may carry multiple OEM brands — the OEM with the fastest, most predictable warranty processing gets better treatment in the showroom.
Manual processing is expensive at scale
A heavy equipment OEM shipping 5,000 machines per year will receive 2,000–4,000 warranty claims annually across a 5–10 year warranty period on an installed base of tens of thousands of units. Processing each claim manually — reading, verifying, requesting documentation, adjudicating, recording — costs £80–£200 per claim in staff time. At volume, that is £400,000–£800,000 per year in processing costs, before considering the claims that get processed incorrectly.
Fraud and leakage go undetected
Manual review catches obvious fraud — a claim for a part that was never installed, a serial number outside the warranty period. It does not catch patterns: a dealer consistently claiming for the same failure code across different machines, a part number being claimed at a price above list, a dealer in a specific territory claiming at twice the rate of comparable dealers.
Pattern-based fraud and leakage detection requires data aggregation across hundreds or thousands of claims — something a spreadsheet cannot do and a manual reviewer does not have time for.
Quality data is trapped in the claims system
Every warranty claim contains valuable quality information: which component failed, how, on which machine configuration, at what age and usage level, in which operating environment. This data should be feeding back to engineering, procurement, and product management in real time. Instead it sits in a claims database that the quality team queries once a quarter, aggregated into a report that is already three months old by the time anyone reads it.
What modern warranty management looks like
AI-assisted claim adjudication
Modern warranty management uses AI agents to handle the adjudication workflow:
Validation checks the claim against the warranty entitlement for that serial number. Is the machine under warranty? Is it within the coverage period? Does the dealer have authorisation to submit claims for this machine? These are deterministic checks that should happen in seconds, not days.
Document completeness checks that the required supporting documentation is present and legible. A service report, a photo of the failed component, the dealer's technician credentials. Missing documents trigger an automated request to the dealer with a specific list of what is needed — not a generic "please provide additional information" email.
Fraud detection runs pattern analysis across the claim against the dealer's historical claims, the part number's claim frequency across the fleet, the failure code's expected incidence rate, and the repair cost against benchmarks. Anomalies are flagged for human review. Standard claims are approved automatically.
Adjudication applies the coverage rules for the specific warranty type (OEM warranty, extended warranty, goodwill) and produces an approval, a rejection, or a partial approval with a clear reason code the dealer can understand and contest if needed.
The target state: 70–80% of claims are auto-approved within 24–48 hours. The remaining 20–30% that require human review are surfaced with all relevant data pre-assembled — the adjudicator is reviewing exceptions, not processing paperwork.
Real-time warranty analytics
The data generated by claims processing should be visible in real time, not buried in a quarterly report.
Fleet health dashboard: Which machine models are generating the most claims? Which failure codes are trending up? Which dealers are claiming at anomalous rates? Which geographic regions show higher-than-expected failure rates for specific components?
Quality feedback loop: When a specific failure code exceeds a threshold — say, three claims for the same hydraulic seal failure in the same quarter — an alert goes to engineering and procurement. The quality team does not wait for the quarterly review. They investigate while the issue is still active.
Reserve prediction: Based on the current installed base age distribution, the claim rate history, and the remaining warranty coverage periods, the finance team gets a forward projection of warranty reserves needed. Monte Carlo simulation gives confidence intervals, not point estimates. The CFO stops being surprised by warranty charges.
Supplier recovery: When a component failure is attributable to a supplier defect — confirmed through failure analysis — the warranty management system identifies all claims linked to that part number, calculates the total recovery amount, and generates the supplier recovery claim. Supplier recovery rates for equipment OEMs average 15–30% of total warranty spend; most OEMs recover less than half of what is available because the identification and documentation process is manual.
Goodwill decision support
Goodwill claims — warranty claims submitted after the warranty period has expired, or for failure modes not covered under warranty — require human judgement. But that judgement should be supported by data.
For each goodwill claim, the system shows: the customer's lifetime equipment purchase history, the machine's age and usage at failure, the failure mode's typical incidence rate, the cost of approval vs the customer lifetime value at risk, and a suggested decision based on policy parameters. The adjudicator makes a faster, more consistent decision. The goodwill budget is tracked against policy in real time.
Dealer portal for claim submission
Dealers should be able to submit, track, and manage claims through a portal — not via email. The portal guides them through the required fields and documentation for the specific failure type, shows them the status of all open claims in real time, and provides clear reason codes for rejected claims with an appeal workflow.
When dealers can see their claims in real time, the volume of status enquiry calls to the OEM's warranty team drops significantly. That alone justifies the portal.
The data model that makes this work
None of the above is possible without a connected data model. The warranty management system needs access to:
Installed base: Every machine's serial number, configuration, delivery date, and current owner. Without this, the system cannot validate warranty entitlement.
Contract records: Which warranty type applies to this machine — standard OEM warranty, extended warranty, dealer-funded warranty extension? What coverage does it include? What are the exclusions?
Parts catalogue: What is the list price for the part being claimed? Which supplier is responsible for this part number? What is the expected failure rate at this machine age?
Dealer records: Who is this dealer? What is their authorisation level? What is their historical claim rate? Are they flagged for any anomalies?
Telematics (where available): What are the machine's operating hours at the time of failure? Was it operating outside normal parameters before the failure?
In most OEM organisations, these data sets live in separate systems — SAP for the machine and parts data, a CRM for dealer records, a telematics platform, and a standalone warranty claims system that has no integration with any of them. The manual process fills the gaps. Modern warranty management replaces the manual process with integration.
What to expect from a warranty management implementation
Week 1–2: Data mapping. Connect the warranty system to the installed base model, the parts catalogue, and the dealer records. This is the integration phase and the one most likely to surface data quality issues — machines not in the installed base, part numbers without price records, dealers without authorisation records.
Week 3–4: Workflow configuration. Set the coverage rules for each warranty type, the auto-approval thresholds, the fraud detection parameters, and the escalation routing. This is typically done with the OEM's warranty manager and quality team.
Week 5–6: Dealer portal rollout. Migrate active dealers to the portal. Train dealer warranty contacts. Typically starts with the highest-volume dealers.
Week 7–8: Go-live on new claims. Existing open claims are processed in parallel or migrated, depending on volume.
A full implementation with SAP integration typically completes in 8–12 weeks.
Frequently asked questions
What is warranty management software for heavy equipment OEMs? Warranty management software for equipment OEMs automates the warranty claims lifecycle: dealer claim submission, entitlement validation, document completeness checking, fraud detection, adjudication, payment, and supplier recovery. It also provides real-time analytics on fleet quality, claim patterns, and warranty reserve requirements — data that manual processing cannot generate at speed or scale.
How does AI improve warranty claims processing? AI improves warranty claims processing in three ways: automated validation of entitlement and document completeness reduces the administrative burden on the OEM's warranty team; pattern-based fraud detection identifies anomalies across large claim volumes that manual reviewers cannot detect; and predictive analytics forecasts warranty reserve requirements with higher accuracy than historical averages.
What is warranty analytics for equipment manufacturers? Warranty analytics is the reporting and analysis layer on top of warranty claims data. It shows which components are failing most frequently, which machine configurations generate the highest warranty cost, which dealers are claiming at anomalous rates, and how warranty spend is trending against the forward reserve. Real-time warranty analytics gives quality teams, finance teams, and procurement teams current data rather than quarterly reports.
How does warranty management software integrate with SAP? ServiceCPQ's warranty management integrates with SAP S/4HANA and SAP ECC. Warranty claims create SAP credit memos automatically. Supplier recovery claims create SAP debit memos. The installed base data is pulled from SAP delivery records. Contract entitlement is validated against SAP contract objects. No middleware is required.
What is the typical warranty processing time with modern software? With AI-assisted adjudication, 70–80% of standard warranty claims are auto-approved within 24–48 hours. Claims requiring human review are processed in 3–5 days with pre-assembled documentation. The overall average cycle time drops from 14–21 days to 3–5 days. For dealers, this means faster cash flow and a stronger incentive to submit claims properly.
See how ServiceCPQ handles warranty management →