The Challenge
A DACH-based industrial automation client operating 12 production cells across two facilities faced mounting pressure from unplanned downtime events - averaging 4.2 hours per week per facility. Legacy monitoring systems generated over 200 alerts per shift, creating alert fatigue that caused operators to miss critical signals.
The client needed an intelligent layer that could distinguish signal from noise, surface actionable recommendations, and route equipment-critical decisions through a structured approval process that met their ISO 13849 safety requirements.
Approach
Phase 1 · Week 1-2
Environment Mapping
OT/IT network topology assessment, sensor data audit across 47 data points per cell, operator workflow ethnography across 3 shifts, safety interlock documentation review, and identification of 8 high-value anomaly patterns from 6 months of historical maintenance logs.
Phase 2 · Week 3-4
Intelligence Layer Design
Multi-signal anomaly detection model design, recommendation confidence scoring methodology, alert taxonomy, human approval workflow design mapped to existing role structure and ISO 13849 requirements, and operator UX design optimized for factory floor conditions.
Phase 3 · Week 5-6
Pilot Design & Governance
Single production cell pilot specification, success metrics framework, post-deployment monitoring cadence, incident classification and escalation protocol, and board-level reporting template for the AI system's performance and safety record.
Interactive Prototype
This prototype simulates the operator decision support interface: production-cell monitoring, anomaly detail, confidence-scored AI recommendations, human approval routing, PLC dispatch confirmation, and audit logging.
Manufacturing Intelligence Console
Operator Decision Support
Production Floor Overview
Affected Components
AI Recommendation
Pause feed and inspect servo drive
The feed axis is showing a sharp vibration increase with a simultaneous temperature spike. The signature indicates servo drive overload rather than normal tool wear.
68% downtime risk in the next 30 minutes; estimated loss of 1,180 units if ignored.
Human Approval Workflow
Four-tier action control
Operator Review
Shift Supervisor
Maintenance Engineer
Plant Manager
Audit Log
Critical anomaly detected
D-2 · AI monitor
Spindle recommendation generated
B-1 · AI monitor
Coolant flow recommendation generated
E-3 · AI monitor
Cycle variance cleared
A-1 · Operator
Tool wear review completed
C-2 · Engineer
Technical depth
Decision 1
Human-in-the-loop for all equipment actions
The design kept every equipment action behind human approval, not just safety-critical ones. ISO 13849 requirements, union agreements, and operator trust-building all mattered in the early deployment phase.
Decision 2
Confidence scoring over raw accuracy
In industrial AI, confidence behavior matters more than headline accuracy. A 94% accurate system that is wrong at the wrong moment is more dangerous than an 88% accurate system that escalates uncertainty correctly.
Decision 3
Edge inference vs. cloud inference
Latency-sensitive anomaly detection was placed near the production cell, while reporting, monitoring, and governance evidence could live outside the OT control loop. The split reduced cloud dependency for operational decisions.