MI
ManufacturingIQ
Aerospace & Specialty Materials Manufacturing Intelligence · MES · PLM · ERP · Shop Floor
AI learning from 2,140 manufacturing outcomes
ENV: PROD · ROLE: MFG OPS LEAD
Dashboard
TrendIQ 10
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 24
StaffingIQ 7
Manufacturing Intelligence Operations
Continuous intelligence across MES, digital thread, and shop floor — high-performance materials manufacturing
◊ Knowledge graph: 38,420 linked manufacturing artifacts
Throughput Visibility
94%
↑ 18pts vs Q4 baseline
Traceability Coverage
89%
↑ 12pts — gap closure in progress
Issue Resolution Cycle
2.4d
↓ from 4.1d (Q1)
Production Reporting Delay
-31%
↓ vs pre-program baseline
Open Production Risks
6
3 critical · 3 high
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
10 active signals
→
DecisionIQ
8 initiatives
→
ActionIQ
6 in execution
→
EffectivenessIQ
24 outcomes learned
Issue Resolution Cycle Time — 12-week trend
Days from signal detection to initiative closure across all four plants
Engineering Time Recaptured
Hours saved per week by AI-assisted manufacturing workflows
MES↔ERP↔PLM Linkage Health
Coverage of bidirectional sync across all integrated systems
Signal Mix — Last 30 Days
Distribution of operational signals by manufacturing domain
AI Confidence Distribution
Confidence levels across all current AI recommendations
Top Manufacturing Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
Connected Manufacturing Intelligence Architecture
Five-layer stack: data flows up from the plant floor, intelligence flows back down to operators and dashboards
● Reference architecture
Experience
Executive Ops Dashboards Plant Intelligence Views AI Recommendations Workflow Coordination
▼ insights · recommendations · workflows
Intelligence
Manufacturing Ontology Operational Knowledge Graph AI Orchestration Engine Manufacturing Copilots TrendIQ · DecisionIQ · ActionIQ · EffectivenessIQ
▲ events · context · correlations     ▼ guidance · escalation
Engineering
Teamcenter PLM CATIA / NX CAD-CAM MBE Digital Thread Engineering Change Workflows
Manufacturing
Siemens Opcenter MES SCADA PLCs OPC UA Gateways IIoT Devices PI Historian
Enterprise
SAP ERP QMS SPC / LIMS Snowflake Mfg Data Lake Supply Chain Systems
TrendIQ — Manufacturing Signal Detection
Live signals from Opcenter MES, SAP ERP, Teamcenter PLM, PI Historian, SCADA telemetry, and QMS
● Connected to 14 manufacturing systems
Active Signals
10
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
6
3 high-strength
RCAs in Progress
3
Avg conf 86%
AI Recommendations
5
Pending review
Signals 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Manufacturing Initiative Management
Active manufacturing initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 6 AI suggestions pending
AI Continuously Recommending
The system is learning from 2,140 historical manufacturing outcomes to improve every recommendation below
Initiative owners
94% acceptance
Task owners
89% acceptance
Closure paths
82% accepted
Follow-on tasks
71% accepted
ActionIQ — Manufacturing Execution Coordination
Two-tier Kanban: initiatives at the top, task drill-down on click
Initiatives Kanban
AI Operational Copilot
Live workflow optimizations across the manufacturing board
Automation Opportunities
Workflows AI can orchestrate end-to-end (with engineer signoff)
EffectivenessIQ — Manufacturing Organizational Learning
What worked, what failed, and how the system is getting better at manufacturing recommendations
● Continuous learning active
Outcomes Captured
2,140
↑ 184 this month
SME Corrections
67
→ model refinement
Initiative Success Rate
78%
↑ from 64% (Q1)
AI Recommendation Accuracy
84%
↑ 11pts vs baseline
Failed Closures Studied
12
Root-caused
Recent Learning Events
Outcomes from closed initiatives feeding back into the recommendation model
Improvement Over Time
Recommendation acceptance rate by quarter
What's Working (Reinforced)
Patterns the AI is doubling down on
What's Not Working (Down-weighted)
Patterns the AI is moving away from
StaffingIQ — Team Capacity & Workforce Intelligence
AI continuously monitoring team load, skill gaps, attrition risk, and hiring requirements across manufacturing domains
● Analyzing 11 teams · 161 engineers
Teams Over Capacity
7
↑ 3 vs last quarter
Avg Team Load Index
118%
↑ 14pts — above threshold
Critical Skill Gaps
11
Across 5 domains
Open Headcount Needed
24
To restore healthy load
Attrition Risk — High
9
Engineers flagged
Team Overview
Capacity Signals
Demand Forecast
Recommended Actions
Team Load Heatmap — click any card to drill in
Load Index = (Active workload hours) ÷ (Available capacity hours) × 100. AI flags teams above 105%.
● >120% overloaded ● 110–120% stressed ● 100–110% at capacity ● <100% healthy
Load Index Trend — Last 6 Quarters
Average team load across all AV program domains
Headcount Gap by Domain
FTEs needed to restore load index below 100%
AI-detected capacity and staffing risk signals — ranked by program delivery impact
Workload Demand Forecast — Next 4 Quarters
AI projection based on program roadmap, ODD expansion, and historical velocity patterns
● AI projection
Hiring Priority Queue
Roles AI recommends sourcing first based on bottleneck impact
Skill Coverage Matrix
Current coverage vs. program-required proficiency by domain — red cells indicate critical gaps
11 critical gaps identified
AI-recommended actions to improve team capacity, reduce load index, and close skill gaps — ranked by delivery impact
Add Custom Task
AI has pre-filled this based on the initiative's closure path
AI Pre-fill — Suggestions below are editable. Owner defaults to AI's recommendation.