Build — Factory Operations & Quality
Factory Operations Work Flow
ConceptBuild
Central operational intelligence agent that gives production managers, process engineers, and factory workers instant natural-language access to unified OT/IT data — enabling real-time production visibility, root-cause analysis, OEE optimization, and proactive quality management across the factory floor.
Schaeffler deployed this pattern using Manufacturing Data Solutions in Microsoft Fabric + Factory Operations Agent in Azure AI Foundry, achieving improved machinery uptime, quality, and yield.
Agentic behaviors
- Continuously aggregates data from MES, ERP, QMS, and IoT platforms into a unified context layer
- Responds to natural language queries about production status, OEE, downtime reasons, defect trends, and yield metrics
- Proactively surfaces exceptions (OEE drops, yield deviations, safety alerts) without waiting for user queries
- Performs multi-source root-cause analysis when production KPIs breach thresholds
- Identifies worker skill gaps based on production data and recommends targeted training
Features & capabilities
| Natural Language Production Q&A | Responds to queries such as "What caused the OEE drop on Line 3 this morning?" with data-grounded, cited answers |
| Real-Time OEE Dashboard Chatbot | Surfaces availability, performance, and quality components of OEE by line, shift, and product in conversational format |
| Root Cause Analysis Engine | Cross-correlates MES batch data, sensor historian trends, and QMS inspection records to generate ranked root-cause hypotheses with supporting evidence |
| Yield & Defect Trend Analyzer | Tracks defect patterns by product, line, operator, and material lot; flags statistical control violations |
| Production Bottleneck Identifier | Analyzes cycle times, queue depths, and throughput rates across production steps to surface constraint points |
| Asset Maintenance Integrator | Surfaces pending and overdue maintenance tasks from CMMS in context of current production schedule |
| Worker Skill Gap Detector | Correlates production error patterns with operator assignments to identify training needs |
| Proactive Exception Alerts | Pushes Teams notifications to responsible roles when monitored KPIs breach configurable thresholds |
| Shift Handover Report Generator | Auto-generates structured shift summary reports from production data, incidents, and maintenance events |
Agents powering this workflow
Reusable AI capabilities from our Agent Store.
Who benefits
- Plant Managers
- Production Supervisors
- Process Engineers
- Frontline Operators
- Quality Engineers
KPIs impacted
Required connectors
Configured per-organization in the Control Tower once this workflow is enabled for your team.
Configurations & Options
Set up in the Control Tower once this workflow is enabled — shown here so you know what to expect.
Configurations
- OEE drop alert threshold (%)
Options
- Allow autonomous line-shutdown recommendationsWhen on, the agent may proactively recommend line shutdowns without waiting for plant manager confirmation. When off (recommended), all shutdown recommendations require plant manager sign-off.
Governance requirements
- Canonical metric definitions (OEE, scrap rate, MTBF, MTTR, energy per unit) must be locked in a governed metric layer before agent deployment — ambiguous definitions cause inconsistent outputs
- Each data source must carry freshness SLA metadata so the agent can surface data currency to users
- Sensor feeds with calibration issues must be tagged as "suspect" — agent must escalate rather than act on degraded data
- Root-cause hypotheses must include a confidence level and data lineage reference (not just a conclusion)
- Agent cannot recommend line shutdowns autonomously — these require plant manager confirmation