DI
DistributionIQ
Automotive Distribution Operational Intelligence · ERP · WMS · TMS · CRM · Supplier Systems
AI learning from 2,310 distribution outcomes
ENV: PROD · ROLE: OPERATIONS LEAD
Dashboard
TrendIQ 9
DecisionIQ 8
ActionIQ 8
EffectivenessIQ 19
StaffingIQ 8
Architecture
Distribution Operations
Adaptive operational intelligence across inventory, pricing, supply chain, and customer operations — from signal to organizational learning
◊ Knowledge graph: 52,180 linked operational artifacts
Branch Fill Rate
96%
↑ 3pts this quarter
Inventory Record Accuracy
94%
Across all regions
Margin Capture
91%
Recovering lagging SKUs
Decision Latency
-34%
↓ vs 2024 baseline
Open Operational Risks
9
AI-prioritized
Continuous Intelligence Loop
Signal → Decision → Execution → Outcome → Organizational Learning
● Active across all modules
TrendIQ
9 active signals
→
DecisionIQ
8 initiatives
→
ActionIQ
7 in execution
→
EffectivenessIQ
19 outcomes learned
Operational Decision Latency — 12-week trend
Average time from signal to action, hours
Planner Time Recaptured
Hours/week returned by automation, by activity
Operational Data Linkage Health
Coverage across ERP, WMS, TMS, CRM, supplier systems
Signal Mix — Last 30 Days
Operational signals by category
AI Confidence Distribution
Across active recommendations
Top Operational Risks — AI-prioritized
Highest-impact items pulled from TrendIQ
AI Operational Copilot
Live workflow optimizations the system is recommending
TrendIQ — Operational Signal Detection
Continuously detecting stockout risk, margin erosion, inventory mismatch, supplier drift, and service escalations across ERP, WMS, TMS, CRM, and supplier systems
● Connected to 6 operational systems
Active Signals
9
↑ 3 this week
Pattern Clusters
5
2 emerging
Cross-Domain Correlations
7
3 high-strength
RCAs in Progress
3
Avg conf 86%
AI Recommendations
5
Pending review
Signals 12
Patterns 5
Correlations 7
Root Cause Analysis 4
Recommendations 9
DecisionIQ — Initiative Management
Active operational initiatives created from accepted signals, with AI-suggested follow-on tasks and recommended root causes
8 active initiatives 4 AI suggestions pending
AI Continuously Recommending
The system is learning from 2,310 historical distribution 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 distribution operations recommendations
● Continuous learning active
Outcomes Captured
2,310
↑ 168 this month
SME Corrections
57
→ model refinement
Initiative Success Rate
87%
↑ from 75% (Q1)
AI Recommendation Accuracy
92%
↑ 10pts vs baseline
Failed Closures Studied
13
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
System Architecture
The adaptive operational intelligence reference architecture — an orchestration layer over the existing distribution systems, not a replacement for them
● 6-layer reference architecture
Data flows top to bottom — source, ingest, contextualize, reason, deliver — while Closed-Loop Learning feeds back to the Knowledge and IntelligenceIQ layers, turning a distribution network into an intelligent operating network that improves with every execution.
Adaptive Operational Intelligence Architecture
Layer 4 maps directly to the modules in the top navigation
1
Operational Systems Layer
Existing systems of record across the distribution network
Source
ERPWMSTMSCRMProcurementSupplier SystemsPlanning Platforms
The enterprise systems the business already runs — including multiple ERPs inherited through acquisition. IntelligenceIQ orchestrates across them rather than replacing any of them, so transformation doesn't wait on consolidation.
2
Data & Event Layer
Moves operational data and events into the platform in motion
Ingest
Streaming EventsAPIsETL / ELTOperational TelemetryLakehouse
Captures inventory movements, orders, pricing changes, carrier events, and supplier updates as they happen, landing them in a lakehouse so intelligence runs on current reality, not last night's batch.
3
Knowledge & Context Layer
Gives raw data shared meaning across systems and regions
Contextualize
Ontology ModelKnowledge GraphHistorical IntelligenceOperational Relationships
Normalizes terminology across ERPs and regions and links parts, suppliers, branches, and customers in a knowledge graph — so a signal in one system can be related to an effect in another (e.g. a supplier lead-time drift to a forward stockout risk).
4
IntelligenceIQ Layer — the Modules
Where context becomes detection, decision, action, and learning
Reason & Act
TrendIQDecisionIQActionIQEffectivenessIQStaffingIQ
The modules you navigate in this demo. TrendIQ detects operational signals, DecisionIQ converts accepted signals into owned initiatives with recommended root causes, ActionIQ coordinates execution, and EffectivenessIQ measures outcomes. StaffingIQ overlays the workforce capacity needed to deliver the work.
5
Operational Experience Layer
Where intelligence reaches the people doing the work
Deliver
Executive DashboardsTeams WorkflowsMobile AlertsAI CopilotsWorkflow Orchestration
Surfaces recommendations and orchestrated workflows where teams already operate — executive dashboards for leadership, Teams and mobile for planners and branch staff, and digital co-workers that draft transfers, alerts, and escalations.
6
Closed-Loop Learning
Feeds outcomes back so the system keeps improving
Learn
Operational FeedbackInstitutional Knowledge CaptureContinuous ImprovementAdaptive Recommendations
Closed initiatives, SME corrections, and what-worked / what-failed patterns flow back into the Knowledge and IntelligenceIQ layers. This is what EffectivenessIQ captures — and it's how institutional knowledge is preserved before it walks out the door, a stated workforce risk for the business.
Architectural Principles
Design choices that make this credible for a fragmented, multi-ERP distribution enterprise
Orchestrate, don't replace

The platform reads from existing ERP, WMS, TMS, CRM, and supplier systems. No source system is ripped out, so value arrives without waiting for system consolidation across acquired regions.

Intelligence rapidly, not perfect data first

Per the reference model, the goal is surfacing operational intelligence quickly while the learning system keeps improving — rather than blocking on a multi-year data-cleanup program.

Reduce decision latency

The biggest gains come from shortening the time between signal and action, not from adding dashboards. The architecture is built to compress that latency end to end.

Human-in-the-loop

AI proposes; people decide. Owner assignment, initiative acceptance, and task closure all require human confirmation, and every recommendation is overridable with the original preserved.

Cross-region by default

The knowledge graph spans regions and ERPs, so a root cause learned in one region becomes a reusable pattern everywhere — standardizing workflows without forcing identical local systems.

Capture knowledge before it's lost

Closed-Loop Learning preserves institutional operational knowledge as it's used — directly addressing the workforce risk of experienced talent and tribal knowledge leaving the organization.

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.
StaffingIQ — Team Capacity & Workforce Intelligence
AI continuously monitoring team load, skill gaps, attrition risk, and hiring requirements across distribution operations domains
● Analyzing 8 teams · 169 engineers
Teams Over Capacity
7
↑ 3 vs last quarter
Avg Team Load Index
118%
↑ 14pts — above threshold
Critical Skill Gaps
18
Across 6 domains
Open Headcount Needed
24
To restore healthy load
Attrition Risk — High
9
Staff flagged
Team Overview
Capacity Signals
Demand Forecast
Recommended Actions
Team Load Heatmap — click any card to drill in
Load Index = (Active workload hours) ÷ (Available capacity hours) × 100. AI flags teams above 105%.
● >120% overloaded ● 110–120% stressed ● 100–110% at capacity ● <100% healthy
Load Index Trend — Last 6 Quarters
Average team load across all AV program domains
Headcount Gap by Domain
FTEs needed to restore load index below 100%
AI-detected capacity and staffing risk signals — ranked by program delivery impact
Workload Demand Forecast — Next 4 Quarters
AI projection based on program roadmap, ODD expansion, and historical velocity patterns
● AI projection
Hiring Priority Queue
Roles AI recommends sourcing first based on bottleneck impact
Skill Coverage Matrix
Current coverage vs. program-required proficiency by domain — red cells indicate critical gaps
11 critical gaps identified
AI-recommended actions to improve team capacity, reduce load index, and close skill gaps — ranked by delivery impact