The automotive sector faces mounting pressure to improve product quality while reducing operating costs in an
intensely competitive environment. Global manufacturers must process thousands of customer comments, warranty
data, and service reports which, despite containing critical insights, are often underused due to the lack of predictive systems.
Background and Challenges
October 2025
The main challenges identified were:
▪ Reactive detection : Failures were identified only after affecting multiple customers, with economic and reputational consequences.
▪ Unstructured data: Much of the information was in free text, without deep semantic analysis to efficiently identify patterns.
▪ Isolated design decisions : Component changes were made without a predictive assessment of their long -term impact.
▪ High recall costs : Millions in repairs, loss of customer trust, and brand damage.
In this context, the EFD System emerged as a solution to transform fragmented data into a predictive engine capable
of anticipating failures weeks or even months before they escalate into widespread issues.
The motivation behind this project was not only technological but also strategic : to drive a cultural shift within the company—from a reactive approach to a predictive, data-driven one. This positioned the client as an
innovation leader in the automotive industry.