UZ Auto Motors
UZ Auto Motors manufacturing facility needed an intelligent monitoring system to predict equipment failures, reduce downtime, and optimize maintenance costs for AGV (Automated Guided Vehicles) and production machinery across their industrial operations.
We developed a comprehensive AI-based technical monitoring concept with predictive maintenance models. The solution includes integration scenarios with PLC, sensors, and existing factory systems, real-time dashboards with KPI analytics, fault probability predictions, and a complete technical roadmap from MVP to full-scale implementation.
Project Details
Key Features
Comprehensive healthcare solutions designed for patient care excellence
AI-based predictive maintenance model
AGV and production equipment monitoring
PLC and sensor integration architecture
Real-time status and fault probability dashboards
Technical service forecast KPIs
Business & ROI calculations
Downtime reduction optimization
Technical documentation (TZ, roadmap)
MVP to full-scale implementation plan
Industrial IoT data pipeline
Results & Impact
Measurable outcomes that transformed healthcare delivery
Predictive maintenance model for AGV and machinery
Real-time monitoring dashboard with fault probability
ROI model for downtime reduction
MVP → Pilot → Full-scale implementation roadmap
Technology Stack
Modern technologies powering world-class healthcare solutions
"The predictive maintenance concept will transform how we manage equipment reliability. The ROI model clearly shows significant cost savings from reduced downtime and optimized maintenance."
Technical Director
Head of Operations
UZ Auto Motors