Case Studies

Saving Tier 1 Suppliers Money

Base Warranty

The Problem

A Tier 1 supplier client was analysing a historic quality issue for parts supplied to an OEM customer for a particular vehicle model year. All vehicles were on the road and were still in the the 3 year warranty period.

Our client and their OEM customer were uncertain about how many potential claims there could be and how much it would cost.

The OEM customer estimated the final cost would be €250,000.

They gave our client two options: pay the €250,000 or keep the issue open and and become liable for the cost of all subsequent claims.

Our client had already paid £50,000 to resolve previous claims.

The Solution

Our client started using Indico, our base warranty predictive analytics module. Indico takes all relevant claims and vehicle population data to build requisite models that produce accurate forecasts.

These forecasts showed that the issue would cost approximately €85,000 in total by the time the 3 year warranty for all vehicles expired.

With this in mind, our client chose to keep the issue open until all completed their warranty.

The final cost of the issue was £83,000.

The client saved approximately £170k.

The Benefits

Thanks to the forecasts produce by Indico, our client saved approximately £170,000 on a single issue.

Our client had evidence that helped them better understand the problem and defend their position.

Indico helps our clients identify these issues sooner, allowing them to plan resolutions with confidence.

 

Controlling the dealer networks for OEMs

Dealer Analysis

The Problem

We were providing a major NA OEM client with our dealer analytics service when they made a design change to the oil pan on a particular vehicle model.

The standard repair time for the oil pan was originally 4 hours, but unknown to the OEM, the design change shortened the repair time to 1 hour.

A single large dealership discovered this and began booking the repair more frequently, charging 4 hours of labour for a 1 hour repair.

They started booking this repair more frequently and then invoiced for 4 hours labour for a 1 hour repair.

The Solution

The month after the new oil pan design was incorporated into production, we noticed a spike in claims activity by the dealership in question.

We informed the OEM, who started the process for reevaluating the repair procedure. This took some months, and while it was ongoing we noticed a number of dealerships who were in the same region as the original dealership also showed a spike in repair frequencies for the oil pan.

This trend continued for the next 3 months, with more and more dealerships getting involved. Word was spreading among the dealerships who were exploiting the issue.

Soon after, the new repair procedure was put in place and the issue disappeared.

The Benefits

Our predictive analytics help the OEM detect a problem early and identify it before any unusual activity becomes commonplace.

We helped our client contain the issue to a relatively local area, which helped them save an estimated $5,000,000 globally.

OEM Warranty cost saving by earlier issue detection

 

OEM Warranty cost saving by earlier issue detection

Base Warranty

The Problem

Many OEM and Tier 1 suppliers currently use warranty analysis methods which prioritise component fixes based on the highest current claims frequency or costs for vehicles that have been in service for a fixed period.

They must wait for problems to develop above a certain threshold before action is taken. This can take quite some time.

A major OEM identified their top 5 component problems in 3 different vehicle subsystems (Powertrain, Electrical and Chassis) using their current analysis methods.

The cost of these issues to date totalled £180m

The Solution

To demonstrate the ability of Indico to detect issues earlier a project start date for each issue was identified by the OEM when the spend had exceeded their predefined threshold.

For each issue, we used all warranty claims and vehicle population data to retrospectively analyse each component, running forecasts for each month over the 5 previous years.

By plotting the development of the forecast for each of the components, it was possible to identify the earliest month when a strong signal of an impending problem was detected.

The time saving between the month identified by WePredict and the OEM trigger month was calculated.

Based on the spend for each component, we estimated how much money could have been saved if Indico had been used.

The Benefits

The savings identified for these 15 issues by earlier detection were an estimated £55m, based on a total spend of £180m.

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