MO
MeridianOpsIQ
Global Supply Chain & Regional Operations Intelligence Platform · ERP · WMS · TMS · Partner Portal
AI learning from 2,340 supply chain outcomes
ENV: PROD · ROLE: SC OPS LEAD
Dashboard
TrendIQ 11
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 24
OntologyIQ 1,240
KnowledgeIQ 6
ReasoningIQ 4
Global Supply Chain Operations
Continuous intelligence across regional hub performance, backlog resilience, and partner/customer sentiment
◊ Knowledge graph: 4,380 relationships across 1,240 entities
Backlog SLA Performance
71%
↓ 4pts vs last month
Dual-Sourcing Coverage
42%
↑ 8pts this quarter
Order Visibility Adoption
58%
↑ 12pts since portal pilot
Escalation Response Cycle Time
-19%
Faster vs Q1 baseline
Open Critical Risks
4
2 net-new this week
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
11 active signals
→
DecisionIQ
8 initiatives
→
ActionIQ
7 in execution
→
EffectivenessIQ
24 outcomes learned
Backlog SLA Performance — 12-week trend
Weekly SLA attainment across compute, networking, and storage categories
Ops Time Recaptured
Hours/week reclaimed by automation, by function
Dual-Sourcing & Visibility Coverage Health
Stacked coverage across at-risk parts and account tiers
Signal Mix — Last 30 Days
Distribution of active signals by domain
AI Confidence Distribution
Confidence spread across active recommendations
Top Supply Chain Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Supply Chain & Partner Signal Detection
Signals surfaced continuously from ERP, WMS, TMS, partner portal, and community/sentiment feeds across the regional hub network
● Connected to 6 supply chain systems
Active Signals
11
↑ 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 supply chain initiatives created from accepted signals, with AI-suggested follow-on tasks
8 active initiatives 8 AI suggestions pending
AI Continuously Recommending
The system is learning from 2,340 historical supply chain 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 supply chain recommendations
● Continuous learning active
Outcomes Captured
2,340
↑ 186 this month
SME Corrections
14
→ model refinement
Initiative Success Rate
79%
↑ from 68% (Q1)
AI Recommendation Accuracy
85%
↑ 9pts vs baseline
Failed Closures Studied
6
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 — supply chain Knowledge Graph
A living semantic model connecting every entity and risk relationship across active supply chain work
◊ 1,240 entities · 4,380 relationships
Entity Types Modeled
8
Signals, Initiatives, Owners/Teams, Component Families, Supplier Regions, Sites/Hubs, Analytical Insights, Recommendations
Relationship Types
7
sourced-from · root-caused-by · mitigated-by · owned-by · correlates-with · addressed-by · historically-matches
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 supply chain operations 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
64%
Of active work with a linked knowledge asset
Avg. Time-to-Capture
9d
↓ from 15d 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 supply chain Recommendations
Executives need recommendations, not just answers — the reasoning engine evaluates multiple supply chain factors together to produce a single evidence-backed recommendation
◊ Reasoning over the OntologyIQ knowledge graph
Reasoning Chains Run
214
↑ this week
Avg. Factors Evaluated
4.4
per recommendation
Avg. Recommendation Confidence
84%
Grounded in graph evidence
Executive Recommendations Delivered
16
This month
From Systems of Record to Systems That Reason
No existing system is replaced — each becomes more valuable feeding the reasoning engine
ERP · WMS · TMS · Partner Portal · CRM
5 systems connected
→
Enterprise Ontology
8 entity types
→
Knowledge Graph
1,240 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.