The client is a Fortune 500 locomotive company that designs and manufactures engines especially in the Heavy Equipment sector.
Key Challenges
The client pays its customers’ warranty claims for the locomotive engines. Detecting these faults took three to five months and another three months for resolving
The process led to a lot of manpower and monetary impact with huge amount factored for this task on an annual basis
To tackle this huge revenue blocking, the client needed a way to predict future failures and warranty claims
The Impact
30%
reduction in budget provisioned for warranty claims by early prediction of the faults after the go-live of Machine Learning (ML) algorithm models in two of the engines.
The Solution
Infosys Advanced Analytics Program: Validation of analytics models
26% of major defects were detected due to well-defined strategy. This ensured that the program’s aim of ensuring around 85% accuracy for prediction was achieved
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Raw data from the sensors placed in the locomotive engines were fed into the ML algorithm/model to detect faults and issues and predict the warranty claims in advance. Infosys Advanced Analytics Program validated this model and made sure the predictions were accurate.
From a ‘no validation program’, the QA strategy has helped to bring a robust validation system to the client’s data analytics landscape.
- Infosys implemented a well-defined QA strategy to authenticate the analytics models
- Team built customized automation solution to speed up execution in one-week sprint duration
- QA validation strategy helped in identifying core defects. ~26% of major defects were detected due to well-defined strategy. This ensured that the program’s aim of ensuring around 85% accuracy for prediction was achieved
Strong knowledge of niche technologies
Infosys’ strong knowledge of niche technologies like ML Algorithm-Weibull, Occurrence Monitoring, SubPopulation, and the QA skills helped in end-to-end exhaustive validation.
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