Automating Warranty Claims to Eliminate Manual Reconciliation with a Cloud Data Lakehouse

A machine breaks down on a construction site. A dealer fixes it. A warranty claim is submitted.

That part is simple.

In reality, it’s a few extra minutes per claim. A few more emails back and forth. Another spreadsheet open on someone’s second screen. None of it feels urgent, until the volume grows and the process starts to bend under its own weight.

By the time the claim reached headquarters, its story was split across systems: maintenance details in one place, machine data in another, and calculations handled manually in between.

For one of our clients, a global construction equipment manufacturer, warranty processing had become exactly that: a silent bottleneck. 

Claims were being handled, but only through constant manual effort, cross-checking systems, and stitching together data that was never designed to live apart.

What followed was not a system replacement, but a rethink of how warranty data should actually move.

This case study shows how that process was turned from a manual chase into an automated data flow.

Problem 

Each warranty claim required teams to run their own version of an administrative obstacle course:

• Dealer maintenance logs lived in one system
• Machine specifications and warranty rules lived in SAP
• Validation required constant cross-checking between the two

This setup led to predictable issues:

• Manual reconciliation: Teams spent significant time matching dealer-submitted maintenance data with internal machine records.
• Higher risk of errors: Manual entry and calculations increased the likelihood of mistakes, delays, and rework.
• No historical perspective: With data scattered across systems, identifying long-term trends in machine reliability or recurring warranty issues was nearly impossible.

What should have been a repeatable process felt more like forensic work.

Solution

Instead of introducing yet another tool, we built on what already existed.

SmartCat had previously implemented a centralized Cloud Data Lakehouse on AWS for this client. For this initiative, we extended that foundation and turned it into the operational backbone of warranty processing.

Think of the Lakehouse as a central engine, not just a storage layer.

Here’s how the flow works:

• Real-time ingestion pulls data from SAP and dealer-facing Zendesk logs
• Dealer maintenance records and machine specifications are centralized and aligned
• The platform automatically performs complex warranty calculations
• Results are converted into SAP-readable formats (CSV and IDOC)
• Processed claims are fed back into SAP seamlessly

What used to be a back-and-forth between systems became a closed, automated loop.

The Results

The shift from manual processing to an automated data flow delivered immediate benefits.

Claims are processed faster, with significantly less manual effort. Automated validation and IDOC generation reduced human error and improved consistency across the dealer network.

By connecting the Lakehouse to Power BI, the business gained something it never had before: historical visibility. Teams can now analyze warranty trends, machine reliability, and dealer performance over time instead of reacting to individual claims.

Most importantly, this wasn’t a one-off fix.

The same integrated environment now serves as a scalable foundation for future business cases, without re-architecting the platform again.

About the Client

The client is a global construction group consisting of more than 200 companies operating worldwide, supported by a large and diverse dealer network.

Technologies Used

We used the following technologies to build the foundation:

• AWS
• Databricks
• Apache Spark
• Terraform
• Python

Your warranty processes don’t have to slow the business down.

Explore SmartCat’s data platform solutions and see how fragmented systems become automated data flows, or sign up for our workshops and let us build a working solution in a matter of weeks for your use case.