Context: Global Supply Chain Operations
Stack: SAP · o9 · One Network · UDP
Live Intelligence
SC
STAGE 1
Foundation
RAG via AI Studio
STAGE 2
Learning
Feedback loops active
STAGE 3
Scale
Agent deployment
Active Initiatives
14
↑ 3 this week
Signals Detected
47
↑ 12 new today
Tasks In Progress
31
↓ 4 blocked
Initiatives Completed
8
↑ 2 this month
Functional Area Performance
Before / after intelligence layer
Procurement
82%
+18%
Logistics
74%
+12%
Planning
91%
+24%
Manufacturing
67%
+9%
Inventory Mgmt
78%
+15%
Live Signal Feed
AI-detected supply chain anomalies
Tier-2 Supplier Capacity Constraint — APAC
Source: One Network · 94% confidence · 4 min ago
HIGH
Demand Spike — Server Compute SKUs +34%
Source: o9 · 87% confidence · 18 min ago
MED
Logistics Delay — EU Distribution Hub
Source: UDP · 79% confidence · 1 hr ago
MED
Inventory Rebalance Opportunity — NA Region
Source: SAP · 81% confidence · 2 hr ago
LOW
Initiative Pipeline
Active intelligence initiatives across the loop
InitiativeOwnerImpactProgressStageStatus
Supplier Resilience — APAC Tier-2
Triggered by capacity signal · SAP + One Network
M. Tanaka
VP Supply Chain
9.2
65%
ActionIQ In Progress
Demand Surge Response — Compute SKUs
Triggered by o9 demand signal
R. Patel
Planning Director
8.7
30%
DecisionIQ Planning
EU Logistics Rerouting
Distribution hub delay mitigation
A. Müller
Logistics Lead
7.4
85%
ActionIQ Near Complete
NA Inventory Rebalancing
Cross-region stock optimization
C. Johnson
Inventory Ops
6.8
100%
EffectivenessIQ Complete
Signal Feed
47 active · 7 requiring action
Tier-2 Supplier Capacity Constraint — APAC Region
Source: One Network · 94% conf · Impact: HIGH · 4 min ago
HIGH
Inbound Material Lead Time Variance +22 days
Source: SAP · 91% conf · Impact: HIGH · 12 min ago
HIGH
Demand Spike — Server Compute SKUs +34%
Source: o9 · 87% conf · Impact: MED · 18 min ago
MED
EU Distribution Hub — Port Delay Signal
Source: UDP · 79% conf · Impact: MED · 1 hr ago
MED
Multi-Tier Supplier Price Index Shift +8.4%
Source: External · 76% conf · Impact: MED · 2 hr ago
MED
NA Inventory Rebalance Opportunity Detected
Source: SAP · 81% conf · Impact: LOW · 3 hr ago
LOW
Configure-to-Order Fulfillment Cycle +3.2 days
Source: One Network · 72% conf · Impact: LOW · 5 hr ago
LOW
Select a signal to view AI analysis
Initiatives
Click to plan tasks
#1
Supplier Resilience — APAC Tier-2
M. Tanaka · Critical
9.2
Critical5 tasks
#2
Demand Surge Response — Compute SKUs
R. Patel · High
8.7
High5 tasks
#3
EU Logistics Rerouting
A. Müller · Medium
7.4
Medium4 tasks
#4
NA Inventory Rebalancing
C. Johnson · Medium
6.8
Medium3 tasks
Select an initiative to view task plan
Planning 0
In Progress 2
Supplier Resilience — APAC Tier-2
Critical
M. Tanaka · 2 Blocked
2 Blocked 1 In Progress
9.2
38% →
Demand Surge Response — Compute SKUs
High
R. Patel · No blockers
1 In Progress 4 Pending
8.7
22% →
Near Complete 2
EU Logistics Rerouting
Medium
A. Müller · 2 In Progress
7.4
55% →
NA Inventory Rebalancing
Medium
C. Johnson · 1 In Progress
6.8
80% →
Complete 0
Click any initiative card to drill into task Kanban and AI guidance
NA Inventory Rebalancing — Outcome Analysis
Completed · 14 days · Owner: C. Johnson
Complete
Before Initiative
61%
Fill Rate — NA Region
After Initiative
79%
Fill Rate — NA Region
Delta
+18%
Improvement · Goal was +15%
Expediting Cost Reduction
-$840K
Annualized
Excess Inventory Reduction
-22%
DOH improvement
Decision Cycle Time
-4.2d
Avg response time
Learning Signals Generated
Feeding back into AI model
AI threshold calibration updated
Fill rate signal sensitivity adjusted +8% based on outcome evidence
Today · Auto-applied
Owner recommendation model improved
C. Johnson confirmed as primary for NA inventory — model updated
2 days ago · Human validated
Task pattern reused in new initiative
NA rebalance template applied to EU initiative (3 tasks matched)
5 days ago · AI applied
Root cause annotation logged
SME identified demand forecast bias as primary driver — stored
12 days ago · SME submitted
AI Learning Assessment
Effectiveness: 94%
This initiative exceeded KPI targets by 20% and generated 4 reusable intelligence patterns. AI confidence in similar future initiatives has improved from 79% to 91%.
  • Inventory rebalance playbook codified for reuse
  • 3 owner recommendations validated — model updated
  • Signal detection threshold improved for fill-rate anomalies
Human Feedback Inputs
All Initiative Outcomes
InitiativeGoal KPIActual KPIDeltaLearning SignalsAI Improvement
NA Inventory Rebalancing
C. Johnson · 14 days
Fill Rate +15% +18% +3% over goal 4 signals +12% confidence
EU Distribution Rerouting
A. Müller · 21 days
Delay reduction -5d -6.2d +1.2d over goal 3 signals +8% confidence
APAC Procurement Optimization
M. Tanaka · 30 days
Cost reduction -8% -5.3% -2.7% below goal 6 signals Recalibrating
STEP 1
⊕ Add Agent
Configure & build
STEP 2
▶ Test Agent
Backtest & Q&A
STEP 3
⚡ Deploy Agent
Validate & go live
STEP 4
◯ Monitor Agent
Live feed & metrics
⊞ Signal Type
⚙ Thresholds
Capacity drop alert
%
Lead time variance
d
Confidence minimum
%
Signal frequency
min
🔌 Data Sources
👤 Agent Persona
⬡ Agent Logic Flow
1
Detect Disruptions
Monitor One Network, SAP, UDP every 15 min · Trigger: capacity drop >15% or lead time +10d
Configured
2
Assess Impact
Identify affected SKUs, orders, regions via dependency tree · Score impact 1–10
Configured
3
Recommend Actions
Surface next-best options: replan, expedite, substitute, escalate · Min confidence: 75%
Configured
4
Execute with Approval
Create initiative in TrendIQ · Await human approval before execution
Pending test
5
Learn & Improve
Ingest SME feedback from EffectivenessIQ · Retrain detection model monthly
Post-deploy