Problem Statement
Unexpected equipment failures across 2,400+ machines in 12 facilities cause $5.8M annual downtime costs, while manual quality control processes 500K+ monthly inspections with inconsistent defect detection rates.
Solution Architecture
Deployed vector search architecture processing 100M+ daily sensor readings from 15K+ IoT devices, using custom embeddings (512-dimensional vectors) trained on 3 years of historical sensor data, maintenance logs, and quality reports. Predictive maintenance agents analyze vibration patterns, temperature fluctuations, and operational parameters to identify failure patterns 4-6 weeks before breakdown with 89.7% accuracy. Agentic workflows automate quality control through computer vision and vector similarity matching, while supply chain optimization agents analyze 800K+ inventory transactions to optimize procurement decisions.
Impact Metrics
Unplanned downtime reduced 75% through predictive maintenance, saving $5.8M annually in production losses and extending equipment lifespan by 28%
Overall Equipment Effectiveness (OEE) improved from 68% to 88% (30% absolute increase) through intelligent process optimization
Quality defect detection accuracy improved to 96.2%, reducing quality-related returns by 40% and improving first-pass yield from 87% to 94%
Maintenance costs optimized by 34% through precision scheduling (92% accuracy) while processing 100M+ sensor readings daily in real-time
Inventory carrying costs reduced 27% and on-time delivery rates improved from 78% to 94% through supply chain optimization workflows
Production throughput increased 22% and energy consumption reduced 19% through data-driven process optimization across all facilities
Waste reduction of 31% achieved through real-time production monitoring and intelligent decision-making across 12 manufacturing facilities
Technology Stack
Ready to transform your manufacturing?
Let's discuss how we can help you achieve similar results for your specific needs.