PI
PlanningIQ
Eye Health Manufacturing Decision Intelligence · Rochester Operations
AI learning from 1,860 manufacturing outcomes
ENV: PROD · ROLE: OPS LEAD
Dashboard
TrendIQ 9
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 28
OntologyIQ 18,460
KnowledgeIQ 6
ReasoningIQ 214
Rochester Manufacturing Operations
Continuous decision intelligence across tooling, supply chain, quality, and capacity for an eye health manufacturer's Rochester operation
◊ Knowledge graph: 18,460 linked entities
Customer Delivery Performance
96%
↑ 2pts vs last quarter
Tooling Dependency Coverage
88%
12 shared assets mapped
Initiative Closure Rate
84%
+6pts this quarter
Planner Decision Cycle Time
-24%
↓ from 3.1 days avg
Open Capacity Risks
4
Down from 7 last month
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
10 active signals
→
DecisionIQ
9 initiatives
→
ActionIQ
8 in execution
→
EffectivenessIQ
28 outcomes learned
Planner Decision Cycle Time — 12-week trend
Time from signal detection to initiative acceptance
Planner Time Recaptured
Hours/week saved moving from SKU-level to family-level triage
Tooling & Supplier Coverage Health
Percent of shared assets and single-source materials with monitoring coverage
Signal Mix — Last 30 Days
By domain
AI Confidence Distribution
Across all active signals and recommendations
Top Manufacturing Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Rochester Manufacturing Signal Detection
Signals surfaced across ERP, MES, APS, SAP, Quality, and Excel/BI systems feeding Rochester operations
● Connected to 7 manufacturing systems
Active Signals
10
↑ 4 this week
Pattern Clusters
6
3 emerging
Cross-Domain Correlations
7
3 high-strength
RCAs in Progress
4
Avg conf 86%
AI Recommendations
7
Pending review
Signals 12
Patterns 6
Correlations 7
Root Cause Analysis 4
Recommendations 7
DecisionIQ — Initiative Management
Active Rochester manufacturing initiatives created from accepted signals, with AI-suggested follow-on tasks
9 active initiatives 6 AI suggestions pending
AI Continuously Recommending
The system is learning from 1,860 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 — 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 ops lead signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at Rochester manufacturing recommendations
● Continuous learning active
Outcomes Captured
1,860
↑ 84 this month
SME Corrections
37
→ model refinement
Initiative Success Rate
82%
↑ from 74% (Q1)
AI Recommendation Accuracy
88%
↑ 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 — manufacturing Knowledge Graph
A living semantic model connecting every entity and risk relationship across active manufacturing work
◊ 18,460 linked entities · 41,200 relationships
Entity Types Modeled
9
Product Families, Assets, Tooling, Materials, Suppliers, Plants, Quality Events, Capacity, Regulatory Reqs
Relationship Types
12
constrains · supplies · used-by · shares-tooling-with · affects · owns…
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 manufacturing 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
31
↑ 6 this month
Experts at Retirement/Rotation Risk
6
3 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 manufacturing Recommendations
Executives need recommendations, not just answers — the reasoning engine evaluates multiple manufacturing factors together to produce a single evidence-backed recommendation
◊ Reasoning over the OntologyIQ knowledge graph
Reasoning Chains Run
226
↑ this week
Avg. Factors Evaluated
4.8
per recommendation
Avg. Recommendation Confidence
89%
Grounded in graph evidence
Executive Recommendations Delivered
13
This month
From Systems of Record to Systems That Reason
No existing system is replaced — each becomes more valuable feeding the reasoning engine
ERP · MES · APS · SAP · Quality · Excel · BI
7 systems connected
→
Enterprise Ontology
9 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.