Saving $2.1M Annually in Production Waste
The Problem
Atlas Manufacturing's three production lines generated 14% waste on average. Quality checks were manual and reactive — defects were caught post-production. Unplanned downtime cost $180K per incident, occurring roughly twice monthly.
Our Approach
Instrumented production lines with sensor data feeds, then built an AI monitoring layer that analyzes patterns in real time. We used a phased credit allocation model: Phase 1 focused on anomaly detection, Phase 2 on predictive maintenance scheduling.
The Solution
Deployed edge-based anomaly detection models that flag quality deviations within seconds. An LLM-powered analysis engine correlates sensor data with historical patterns to predict equipment failures 48 hours in advance. Operators receive plain-language alerts with recommended actions.
Results
- 41% reduction in production waste
- Unplanned downtime cut by 52%
- $2.1M in annual savings
- Payback period: 4 months
Technology Stack
AI Credit Plan
8,500 AI credits/month for real-time monitoring
Tiered priority system — safety-critical analyses always processed first. Credit usage reported per production line weekly.
45% real-time anomaly detection, 35% predictive maintenance, 20% reporting and analysis
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