DC
DieCastIQ
Enterprise Decision Intelligence Platform · ERP · MES · PLC · Historian · PLM · QMS · CMMS
AI learning from 1,940 manufacturing outcomes
ENV: PROD · ROLE: PLANT EXEC
Dashboard
TrendIQ 10
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 22
OntologyIQ 9
KnowledgeIQ 6
GameTheoryIQ 4
DieCastIQ — Enterprise Operations
Continuous decision intelligence from signal to executive recommendation across engineering, manufacturing, quality, maintenance, supply chain, and finance
◊ Knowledge graph: 18,460 linked entities
Scrap Rate (PF-410)
5.4%
↑ from 3.1% baseline
X-ray Trace Coverage
91%
Of at-risk cavity positions
Initiative Closure Rate
82%
Trailing 90 days
Time-to-Root-Cause
-22%
vs. pre-platform baseline
Open Critical Risks
3
↑ 1 this week
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
Scrap Rate — 12-Week Trend
By product family
Engineering Time Recaptured
Hours/engineer/week, before vs. after AI
Knowledge Graph Linkage Health
Entity freshness across ERP/MES/PLC/QMS/CMMS sources
Signal Mix — Last 30 Days
By owning team
AI Confidence Distribution
Across all active signals
Top Manufacturing Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Manufacturing Signal Detection
Signals surfaced from ERP, MES, PLC, Historian, PLM, QMS, and CMMS across the die casting plant floor
● Connected to 7 plant 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
5
Pending review
Signals 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active die casting 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 1,940 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 plant manager signoff)
EffectivenessIQ — Organizational Learning
What worked, what failed, and how the system is getting better at manufacturing recommendations
● Continuous learning active
Outcomes Captured
1,940
↑ 68 this month
SME Corrections
27
→ model refinement
Initiative Success Rate
84%
↑ from 76% (Q1)
AI Recommendation Accuracy
89%
↑ 7pts 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 — die casting manufacturing Knowledge Graph
A living semantic model connecting every entity and risk relationship across active die casting manufacturing work
◊ 18,460 linked entities · 41,200 relationships
Entity Types Modeled
8
Product Families, Dies, Alloy Lots, Machines, Maintenance Events, Engineering Revisions, Suppliers, Customers
Relationship Types
12
produces · supplies · maintained-by · causes · governed-by · correlates-with…
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 operator and process-engineering 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
7
↑ 2 this month
Experts at Retirement/Rotation Risk
6
3 uncaptured
Capture Coverage
58%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
14d
↓ from 21d 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
GameTheoryIQ — Financial Scenario Modeling
See exactly how each course of action changes your costs and revenue. Adjust the assumptions and watch the numbers move.
◊ 4 active decision scenarios
Total Enterprise Opportunity — Across All 4 Scenarios
Best option in each scenario, under the assumptions currently set below
Net Year-1 Value
—
Annual Cost Savings
—
Revenue Impact
—
Capital Required
—
Scenario
Adjust the Assumptions
These are real business inputs, not abstract priorities — move them to stress-test the case.
Enterprise Reasoning Chain
How a signal becomes an executive decision — this is the "Game Theory" step, now driven by the numbers above rather than a fixed score
Enterprise Ontologies
→
Knowledge Graph
→
Systems of Understanding
→
Systems of Reasoning
→
Goal Reasoning
→
Game Theory
→
Executive Decision Intelligence
Add Custom Task
AI has pre-filled this based on the initiative's closure path
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