SUCCESS STORY

Predictive
maintenance with
Machine Learning
generates ~4.6M in
ROI for a global
leader in energy

We developed Machine Learning models for predictive maintenance on critical assets of a multinational energy company, anticipating operational failures, optimizing costs, and generating ~4.6M in projected ROI.

Machine Learning
XGBoost
LightGBM

Results

in projected return with predictive maintenance
~ 0 M
gain in efficiency and access to strategic indicators
0 %

Daily monitoring of critical assets to anticipate operational failures

Global governance with standardization of corporate metrics and data

Overview

A global leader in energy solutions faced high costs resulting from emergency corrective maintenance and unscheduled downtime in critical equipment. The scenario required transforming historical operational data into actionable intelligence to predict incidents, increase field team efficiency, and protect operational continuity.

The challenges

Unscheduled downtime: frequent interruptions in critical equipment compromised operational efficiency.
Corrective maintenance costs: high expenses generated by the need for emergency and reactive repairs.
Lack of predictability: absence of predictive analytical mechanisms to identify failure trends before breakdowns occur.
Limited planning: difficulty for technical teams in structuring data-driven preventive schedules.

Journey
and solutions

To solve the problem of unavailability and operational costs, we structured a data-driven predictive maintenance solution. We developed daily pipelines for training, validation and inference using supervised algorithms, processing the technical history to anticipate anomalies and issue preventive alerts directly to operational teams through analytical dashboards.

What did we deliver?

Predictive models: implementation of XGBoost and LightGBM algorithms for early anomaly detection in critical assets.
Automated pipeline: daily routines of training, validation and continuous inference on historical data.
Analytical dashboards: panels for visualizing metrics, asset health and supporting maintenance decisions.

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