IG
IntelligentGembaIQ
Business System Intelligence Platform · Innovation Readiness · ACE · Baltimore Field & Service
AI learning from 2,145 Gemba outcomes
ENV: 90-DAY PILOT · ROLE: BUSINESS SYSTEM LEAD
Dashboard
TrendIQ 10
DecisionIQ 8
ActionIQ 8
OntologyIQ 18,460 linked entities
KnowledgeIQ 6
ReasoningIQ 214
EffectivenessIQ 22
Intelligent Gemba Operations
Continuous Business System intelligence across Innovation Readiness, ACE Manufacturing and Baltimore Field & Service
◊ Knowledge graph: 18,460 linked entities
Business System Readiness
87%
↑ 4pts vs last cycle
Requalification Coverage
74%
Of active ECs with linked supplier requal status
Gemba Cycle Closure
81%
Root cause → countermeasure closure rate
Time to Assemble Context
-47%
vs. manual cross-system assembly
Open Quality & Supply Risks
7
2 critical
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
10 active signals
→
DecisionIQ
8 initiatives
→
ActionIQ
7 in execution
→
EffectivenessIQ
22 outcomes learned
Time to Root Cause — 12-week trend
Median hours from signal detection to confirmed root cause
Engineer & Technician Time Recaptured
Hours per week no longer spent manually assembling cross-system context
Requalification & Validation Coverage Health
Share of active engineering changes with a closed requalification loop
Signal Mix — Last 30 Days
Distribution of active signals by Business System domain
AI Confidence Distribution
Recommendation confidence across all active signals
Top Business System Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Business System Signal Detection
Signals surfaced across PLM, MES, ERP, QMS, Service/CRM and IoT/BMS systems feeding the Gemba loop
● Connected to 7 Business System systems
Active Signals
10
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
6
3 high-strength
RCAs in Progress
4
Avg conf 86%
AI Recommendations
6
Pending review
Signals 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active Business System initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 3 AI suggestions pending
AI Continuously Recommending
The system is learning from 2,145 historical Gemba outcomes to improve every recommendation below
Initiative owners
94% acceptance
Task owners
89% acceptance
Closure paths
82% accepted
Follow-on tasks
71% accepted
ActionIQ — Execution Coordination
Two-tier Kanban: initiatives at the top, task drill-down on click
Initiatives Kanban
AI Operational Copilot
Live workflow optimizations across the board
Automation Opportunities
Workflows AI can orchestrate end-to-end (with Business System lead signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at Business System recommendations
● Continuous learning active
Outcomes Captured
2,145
↑ 187 this month
SME Corrections
14
→ model refinement
Initiative Success Rate
84%
↑ from 76% (Q1)
AI Recommendation Accuracy
89%
↑ 6pts vs baseline
Failed Closures Studied
11
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
OntologyIQ — Business System Knowledge Graph
A living semantic model connecting every entity and risk relationship across active Business System work
◊ 18,460 linked entities · 41,900 relationships
Entity Types Modeled
8
Products, Engineering Changes, Components, Suppliers, Validation Records, Quality Records, Plants/Sites, Customers
Relationship Types
12
requires · qualifies · supplies · governed-by · impacts · co-occurs-with · escalates-to…
Graph Freshness
96%
Entities updated within the last cycle
Query Accuracy (validated)
93%
Against SME-reviewed answer set
Ask the Knowledge Graph
Not sure where to start? Try a sample question below, or type your own — answers are traced back to the underlying graph nodes and edges.
◊ Grounded answers only
Knowledge Graph Schema
Core entity and relationship types behind every answer
Highest-Connectivity Entities
Nodes with the most cross-domain relationships — usually the highest-leverage risk points
KnowledgeIQ — Institutional & Tacit Knowledge Capture
Tracks where Gemba know-how is captured, at risk, or missing entirely, and visualizes it as a knowledge graph
◊ Linked to the OntologyIQ knowledge graph
Knowledge Assets Captured
24
↑ 5 this month
Experts at Retirement/Rotation Risk
6
2 uncaptured
Capture Coverage
68%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
11d
↓ from 19d last quarter
Knowledge Graph — Experts, Assets, Programs & Sites
Instance-level view of the graph: who holds the knowledge, what's been captured, and which programs and sites it feeds. Click any node.
At-Risk Expertise
Operators and specialists nearing retirement or rotation, ranked by capture status
Knowledge Asset Library
Captured video, process sheets, and annotated drawings, linked to the program and initiative they came from
ReasoningIQ — Evidence-Backed Business System Recommendations
Executives need recommendations, not just answers — the reasoning engine evaluates multiple Business System factors together to produce a single evidence-backed recommendation
◊ Reasoning over the OntologyIQ knowledge graph
Reasoning Chains Run
214
↑ this week
Avg. Factors Evaluated
4.6
per recommendation
Avg. Recommendation Confidence
86%
Grounded in graph evidence
Executive Recommendations Delivered
12
This month
From Systems of Record to Systems That Reason
No existing system is replaced — each becomes more valuable feeding the reasoning engine
PLM · MES · ERP · QMS · Service/CRM · IoT/BMS
7 systems connected
→
Enterprise Ontology
8 entity types
→
Knowledge Graph
18,460 linked entities
→
Reasoning Engine
214 chains run
→
Business Users
Executive decisions
Reasoning Scenarios
Each scenario evaluates multiple factors across the enterprise before producing a recommendation — click a scenario to see the full evidence chain
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.