An automotive parts manufacturer with 12 production lines experienced an average of 8 unplanned breakdowns per month, each costing $50K per hour in lost production. The maintenance team relied on calendar-based preventive schedules that either replaced parts too early (wasting 30% of useful life) or too late (causing failures).

IoT sensors on 2,000+ pieces of equipment generated 500GB of telemetry daily that nobody analyzed. The 4-person maintenance engineering team couldn't process the data volume. Spare parts were either overstocked (tying up $3M in inventory) or unavailable when needed (extending downtime by days waiting for emergency shipments).

The manufacturer needed AI that could predict failures, coordinate maintenance during planned windows, and ensure parts were available.

The Solution

Agents that monitor sensors, predict failures, order parts, and schedule maintenance autonomously

Vijan.AI deployed 6 agents across all production lines. The Sensor Ingestion agent streams IoT telemetry from 2,000+ sensors in real-time. The Anomaly Detector identifies degradation patterns using vibration, temperature, and power consumption signatures. The Failure Predictor estimates remaining useful life for each critical component. The Work Order agent creates maintenance tickets in CMMS with priority and instructions. The Parts agent checks spare parts inventory and triggers procurement for items needed within the forecast window. The Scheduler agent coordinates maintenance during planned windows to minimize production impact.

Autonomous Agents

How each agent reasons, decides, and acts

Step 1 · Prediction

Predictive Maintenance Agent

Intelligent Failure Prediction

Continuously analyzes sensor telemetry using ML models and physics-based simulations to predict equipment failures 72 hours in advance, autonomously scheduling preventive interventions and routing critical predictions to quality inspection.

Input

Real-time sensor data (vibration, temperature, pressure, acoustic)

Output

Failure probability scores with recommended maintenance actions and urgency levels

Calls ML scoring API with rolling 30-day sensor feature vectors for predictive analysis
Invokes physics simulation tool for remaining useful life estimation based on equipment specs
Autonomous decision: schedule maintenance, issue critical alert, or continue monitoring based on risk threshold
Routes high-severity predictions to Quality Inspection agent for validation and impact assessment

Step 2 · Validation

Quality Inspection Agent

Automated Quality Validation

Validates predicted failure modes against recent quality metrics and visual inspection data, autonomously confirming maintenance necessity and escalating to production scheduling for downtime coordination.

Input

Failure predictions with equipment IDs and probability scores

Output

Validated maintenance requirements with production impact assessment

Queries vision API for recent thermal and visual inspection images of flagged equipment
Cross-references defect database for historical failure patterns matching current predictions
Autonomous decision: confirm maintenance need, request manual inspection, or downgrade urgency
Forwards confirmed cases to Production Scheduling agent with estimated downtime windows

Step 3 · Scheduling

Production Scheduling Agent

Production Schedule Optimization

Dynamically adjusts production schedules to accommodate maintenance windows, autonomously rerouting work orders to alternative equipment and notifying OEE optimizer of capacity changes.

Input

Validated maintenance requests with priority levels and time estimates

Output

Optimized production schedule with maintenance windows and capacity adjustments

Calls MES API to retrieve current production queue and equipment utilization rates
Executes calendar optimization tool to find minimal-impact maintenance windows within 48-hour horizon
Autonomous decision: reschedule jobs, shift to backup equipment, or delay non-critical orders
Notifies OEE Optimization agent of schedule changes and expected capacity reductions

Step 4 · Optimization

OEE Optimization Agent

Overall Equipment Effectiveness Tuning

Recalculates OEE targets accounting for scheduled maintenance, autonomously balancing availability, performance, and quality metrics while requesting parts from supply chain agent.

Input

Schedule adjustments with maintenance duration and affected equipment

Output

Revised OEE targets and resource allocation recommendations

Invokes analytics engine to recalculate availability, performance, and quality loss percentages
Calls optimizer tool to rebalance production targets across remaining equipment capacity
Autonomous decision: approve schedule, request additional shifts, or prioritize high-margin products
Sends parts requisition to Supply Chain Visibility agent for maintenance materials

Step 5 · Inventory

Supply Chain Visibility Agent

Proactive Parts Procurement

Checks real-time parts inventory and vendor lead times, autonomously triggering expedited procurement for critical maintenance components and confirming availability to work order manager.

Input

Parts requisition lists with equipment specs and urgency flags

Output

Parts availability status with procurement timelines and vendor confirmations

Queries ERP system for current stock levels and reorder points of required maintenance parts
Calls warehouse API to verify physical location and condition of available components
Autonomous decision: release from stock, expedite vendor order, or suggest alternative parts
Confirms parts availability and delivery timeline to Maintenance Scheduler agent

Step 6 · Execution

Maintenance Scheduler

Work Order Coordination

Generates and assigns maintenance work orders with parts, procedures, and technician schedules, autonomously tracking completion and feeding results back to predictive model for continuous improvement.

Input

Confirmed maintenance requirements with parts availability and schedule windows

Output

Executed work orders with completion reports and feedback data

Creates work orders in CMMS with detailed procedures, safety protocols, and required parts lists
Calls notifier API to dispatch assignments to technicians with real-time updates and checklists
Autonomous decision: assign to available technicians, escalate if overdue, or adjust priority
Captures completion data and actual failure modes to retrain Predictive Maintenance ML model

Results

Measurable impact within 90 days of deployment

45%

Less Unplanned Downtime

Unplanned breakdowns reduced from 8 to 4.4 per month. Mean time to repair reduced 60% with predictive preparation.

$4.8M

Annual Savings

Combined savings from reduced downtime, optimized maintenance schedules, and lower spare parts inventory.

92%

Prediction Accuracy

Failure predictions accurate to within 48 hours for 92% of critical equipment. False positive rate below 5%.

40%

Parts Inventory Reduction

Spare parts inventory optimized from $3M to $1.8M while improving parts availability to 99%.

Implementation

From pilot to production in 12 weeks

Week 1-4

Agent Design & Tool Integration

Defined agent capabilities, connected ML model, rules engine, graph DB, and chargeback API tools. Configured orchestrator routing logic.

Week 5-8

Shadow Mode & Autonomous Tuning

Agents ran in shadow mode on 10% of transactions. Tuned decision thresholds, tool call parameters, and feedback loop retraining frequency.

Week 9-12

Full Autonomous Deployment

Production rollout across all channels. Agents operating fully autonomously with human-in-the-loop for critical escalations only.

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