Insurance Operations

30% reduction in unplanned downtime, 5x data growth, and increased operational reliability

Background

In heavy industry, unplanned downtime can cost millions. Our client operates across multiple large-scale manufacturing and energy sites and needed a proactive approach to equipment maintenance—one that could scale with their rapidly expanding data infrastructure.

The Challenge

The company was dealing with costly, frequent equipment failures due to undetected wear and tear. Traditional maintenance models—time-based or reactive—were inefficient and inconsistent. With thousands of machines in operation, early fault detection at scale was essential.

The Solution

Azranta worked with the client to implement a full-scale predictive maintenance platform powered by machine learning. The system involved:
📡 Monitoring 85,000+ vibration sensors across critical machinery
🧠 Running 15,000+ machine learning models in parallel to analyze sensor patterns
⏱️ Real-time predictions of part degradation and failure risks
🔁 Automated maintenance alerts integrated into their existing ERP and CMMS workflows

What the Retail Chain Said

With Azranta’s predictive maintenance platform, we’re not reacting to breakdowns—we’re preventing them.

This case showcases how data at scale—when combined with intelligent ML architecture—can turn raw sensor noise into actionable insight. The result? Fewer breakdowns, more uptime, and a truly data-driven maintenance culture.

30% reduction in unplanned equipment downtime, translating into millions in cost savings
5x growth in data volume handled, with scalable infrastructure to support further expansion
Smarter, targeted maintenance interventions, reducing spare part costs and labor hours
Fewer emergency repairs, contributing to safer, more stable operations

Like our Case Studies

Lets Connect to Talk More on this

Request a Newsletter

Ready to Work Together? Build a project with us!

Request a Newsletter

Learn More From

Frequently Asked Questions

Scroll to Top