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CASE STUDY

Machine Learning in Manufacturing

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At a glance

Challenge

Help a heavy equipment manufacturer predict warranty claims that are supplier-at-fault to recover millions in lost claims.

Result

Pariveda developed a cloud-based solution using a Machine Learning model to predict which claims would be accepted by suppliers and to mark these claims for submission.

Impact

Pariveda’s model predicted that over $13M worth of claims that were not submitted to suppliers would have been accepted, with a potential recovery amount of over $7M.

Technologies used

Amazon SageMaker, Databricks Apache Spark™ 

One of the US’s largest corporations and one of the largest heavy equipment manufacturers in the world.

Heavy equipment manufacturers routinely deal with the assembly timeline and various suppliers while trying to keep customers satisfied. But, when there is a fault in the product, how do they collect from suppliers when their parts cause a warranty claim?

 

The Challenge

Leveraging machine learning to recover millions from the supplier-at-fault claims process for a leading heavy equipment manufacturer.

When our client gets a warranty claim, often a part supplier is at fault, and some contracts allow them to recover money from the supplier. Today, this manufacturer recovers money on only 3% of claims. It is estimated that 12-13% of claims could be recoverable, amounting to a reduction in warranty liability in the $10M range.

The Result

How Pariveda developed a cloud-based solution using a Machine Learning model:

The Impact

In initial back-testing of warranty claims submitted during 2018, the model predicted that over $13M of claims that were not submitted to suppliers would have been accepted, with a potential recovery amount of over $7M.

Learn how we deliver on the essential, strategic needs that enable companies to sustain a resilient and impactful business.

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