Predictive Maintenance at Scale with Machine Learning
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.
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We specialize in designing AI-driven and automation-first solutions that eliminate inefficiencies and boost business performance. From robotic process automation (RPA) to custom AI integrations, we help you move faster, smarter, and leaner.
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