Every routing or promise change has a ripple effect. See yours before you deploy.
The Impact Intelligence Verification Graph Engine (VGE) traverses your omnichannel graph to surface impacts across:
- Inventory nodes, allocation policies, and ATP calculations
- Fulfillment routing, ship-from-store, and DC capacity
- Returns workflows and disposition rules
- Margin models and cost-to-serve projections
Preview mode lets you ask 'what if?' before committing, with uncertainty bands calibrated from your actual sales and returns data.
Inventory exposure map
Surface allocation impacts across your network before rollout:
- Stock-out risk and excess exposure by location
- Cancellation probability and ATP impacts
- Store, DC, and online pool rebalancing effects
Fulfillment routing preview
Draft capacity and cost analyses without committing changes:
- SLA impact summaries and cost-to-serve projections
- Ship-from-store readiness assessments
- Split-shipment probability and carrier allocation
Margin impact estimate
Estimate financial impact with uncertainty bands (min/likely/max):
- Margin deltas and shipping cost shifts
- Channel conflict exposure
- Markdown cascade risk
Console preview
Change intelligence report
Seed change
Allocation policy change: Store cluster reassignment - 18 stores, SKUs HM-4401-HM-4488
Change impact
41 of 220 nodes (19%)
across 3 domains
Severity hotspots
3
critical
NRE estimate
$62K – $118K
likely $87K
Schedule delta
+8d
critical path
Impact cascade / sample path
Store cluster assignment - 18 stores, SKUs HM-4401-HM-4488
via allocation
3 channel routing rules need update
via channel_routing
5 promotion tiers affected
via pricing
2 DC allocation splits require rebalancing
via fulfillment
4 delivery SLAs at risk of breach
via sla_management
1 another allocation change overlaps this store cluster
- └─ ALC-0294 Promotional endcap reflow - holiday SKU bundle 3 shared nodes in planning
Verification pack / draft
- 18 store allocation updates
- 3 channel routing rule revisions
- 2 DC rebalancing validations
- 4 delivery SLA impact checks
Cost estimate: NRE $62K – $118K (likely $87K) · Recurring +$0.12/unit
- ├─ Inventory rebalancing: $18K – $28K (18 stores, 88 SKUs)
- ├─ Markdown risk: $12K – $22K stranded inventory in old clusters
- ├─ Schedule penalty: +8d delays seasonal allocation rollout
- ├─ Recurring: +$0.12/unit from split-shipment surcharge
The problem
Routing and promise changes are governed in tickets and spreadsheets, not in your OMS.
An omnichannel ops lead proposes a routing rule change to reduce store excess and shipping cost. The OMS configuration team says 'just a simple promise tweak.' Two weeks later, you discover it triggered stockouts at 23 locations, shifted order volume that overwhelmed three store pick teams, invalidated margin targets for two channels, and the cost-to-serve delta is 5x the original estimate.
Hidden cost of blind changes
- Store stockouts discovered weeks after routing or allocation rule changes go live.
- Ship-from-store capacity limits breached without forecast or planning.
- Promise accuracy drops and cancellations spike from routing changes nobody modeled end-to-end.
- Returns disposition bottlenecks triggered by policy updates missed during review.
Key capabilities
Active intelligence for retail + omnichannel changes.
Multi-node inventory modeling
Map inventory positions across stores, DCs, and online pools, linking allocation rules, ATP thresholds, and replenishment triggers to each location.
Allocation cascade analysis
Trace allocation rule changes through ATP logic, safety stock policies, replenishment triggers, and channel exposure to reveal the full downstream impact before deployment.
Fulfillment routing simulation
Model routing rule changes across order volume, node capacity, SLA targets, and shipping costs to surface capacity risks and margin impacts.
Store readiness assessment
Identify every store affected by ship-from-store expansion with pick capacity, inventory buffer, and labor allocation impact before launch.
Returns cascade tracking
Map which disposition workflows, DC receiving queues, and restock timelines are invalidated by a returns policy change before processing backlogs appear.
Margin model delta estimation
Calculate cost-to-serve, channel margin, and promotion impact deltas with uncertainty ranges that calibrate from your actual fulfillment costs and sales data.
Channel conflict detection
Verify whether allocation or pricing changes create channel conflicts: online vs. store exposure, margin cannibalization, or customer experience risks across touchpoints.
Markdown cascade modeling
Assemble markdown impact packages linking every impacted node to its pricing rules, clearance thresholds, and margin floor compliance.
How it works
From change signal to verified action.
Seed the change
A change record identifies what's changing: routing rule, promise logic, allocation policy, ATP threshold revision, or returns policy.
Traverse the graph
Domain providers walk the dependency graph across inventory nodes, allocation policies, fulfillment queues, returns workflows, margin models, and capacity constraints.
Score exposure
Each impacted node receives an exposure score (0.0–1.0) based on stock levels, order volume, SLA sensitivity, margin impact, and channel priority.
Detect collisions
Cross-change collision detection reveals when concurrent allocation updates or routing changes create overlapping impacts that require triage.
Generate readiness packs
Assemble capacity analyses, SLA impact summaries, store readiness checklists, and margin projection reports. Preview them before committing.
Estimate margin impact
Cost-to-serve deltas, margin shifts, SLA penalty exposure, and channel conflict risk computed with min/likely/max uncertainty bands.
Act or iterate
Apply the change with idempotency keys to persist readiness packs, or adjust parameters and re-run the analysis in preview mode.
Hybrid graph model
The engine analyzes your existing operational data directly, with no data migration required.
Virtual edges
Inferred dependencies from allocation hierarchies, fulfillment routing rules, and replenishment policies.
Explicit edges
Tenant-defined dependencies with rationale and supporting context, e.g., linking a store pick capacity limit to specific allocation zones.
Policy edges
Rules mapping channel governance frameworks (margin floors, SLA commitments, customer experience standards) to required validation work for each change type.
Retail + Omnichannel impact scenarios
Real change scenarios in retail + omnichannel.
Impact Intelligence adapts to your domain’s change patterns, compliance frameworks, and verification workflows. These are representative output examples from the VGE computation pipeline.
Retail + Omnichannel
Cost: $87K (range: $62K–$118K) · 18 locations affectedTrigger
Allocation rule change
Impact
Store and DC stock levels, ATP accuracy, replenishment triggers, transfer queues, 18 locations with stockout risk, 3 channels with margin exposure.
Verification Pack
Allocation impact report, node exposure map, replenishment adjustment plan, channel conflict summary.
Metrics
Cost: $87K (range: $62K–$118K) · 18 locations affected
Retail + Omnichannel
Schedule: +12 days · 12 nodes replannedTrigger
Fulfillment routing update
Impact
Order volume shift across 12 nodes, 3 stores over pick capacity, SLA risk for 2-day shipping, promise accuracy drop, cancellation risk elevated, cost-to-serve delta across 4 channels.
Verification Pack
Routing simulation report, capacity analysis, SLA impact summary, cost-to-serve projection.
Metrics
Schedule: +12 days · 12 nodes replanned
Retail + Omnichannel
23 stores expanded · Schedule: +18 daysTrigger
Ship-from-store expansion
Impact
23 stores affected, pick volume increase 40%, inventory buffer needs recalculated, labor allocation impact across 6 regions.
Verification Pack
Store readiness assessment, capacity model, inventory buffer plan, labor reallocation schedule.
Metrics
23 stores expanded · Schedule: +18 days
Retail + Omnichannel
Cost: $54K (range: $38K–$72K) · 5 DCs affectedTrigger
Returns policy update
Impact
Disposition workflows at 5 DCs affected, receiving capacity limits breached, restock timelines extended by 6 days, refund processing backlog risk.
Verification Pack
Returns impact report, DC capacity analysis, disposition workflow update, restock timeline projection.
Metrics
Cost: $54K (range: $38K–$72K) · 5 DCs affected
Retail + Omnichannel
8 stores impacted · Schedule: +9 daysTrigger
Replenishment threshold change
Impact
Transfer frequency increase 35%, DC outbound volume spike, 8 stores with receiving capacity risk, supplier lead time impact.
Verification Pack
Replenishment simulation, transfer volume forecast, receiving schedule update, supplier coordination plan.
Metrics
8 stores impacted · Schedule: +9 days
Impact Intelligence for Retail + Omnichannel
Operational scale that makes impact analysis possible.
VGE runs on tenant-owned data: schema depth, API breadth, and deterministic telemetry that keeps change reviews consistent.
Domain providers
15+
5 cross-industry baseline + 10 domain-specific providers (composition structures, compliance, verification, 3D/geometric, procurement, inventory, capital assets, execution chains), each self-describing with SemVer and cost tiers.
Sync analysis
≤2s
Typical graph traversal (≤1K nodes) with batch-first providers and per-request caching.
Async analysis
≤30s
Complex traversals (≤10K nodes) with optional Redis acceleration and per-provider timing.
Impact demo
Impact Intelligence for Retail + Omnichannel
Preview change impact, severity scoring, and verification packs before approvals.
Change impact
41 nodes
Projected change
Severity hotspots
3
Projected change
NRE estimate
$87K
Projected change
Schedule delta
+8d
Projected change
Sample finding
Surface allocation impacts across your network before rollout:
Impact cascade
Seed the change
Virtual edges
Explicit edges
Policy edges
API preview
Schema-stable endpoints for impact intelligence.
Impact Intelligence is designed as a tenant-owned API surface with preview-first semantics, deterministic run snapshots, and export-ready results.
Preview vs apply
Every request can run in preview mode to generate impact results without mutating data. Apply mode uses idempotency keys to persist verification packs safely.
View developer docsPOST Start impact analysis
Seed a new analysis for an allocation change, fulfillment routing update, or returns policy shift. Preview mode is the default.
POST /api/v1/change-controls/{id}/impact/runThe change record (created separately) carries the change details: allocation policy ALLOC-NE-SEASONAL updated to shift store cluster NE-Tier1 (18 stores) from DC-pool EFC-East to regional split across EFC-East and EFC-Central, with Buy Online, Pick Up in Store (BOPIS) priority override and clearance/markdown policy adjustments for SKUs HM-4401 through HM-4488.
Request
{
"detect_collisions": true
}Response
{
"schema_version": "vge.graph_result.v1",
"run_id": 445,
"nodes": [
{
"node_ref": {
"resource_type": "store_cluster",
"resource_id": 30100,
"display_name": "NE-Tier1 Store Cluster",
"display_code": "SC-NE-T1",
"status": "Active - 18 stores",
"tags": [
"Northeast",
"BOPIS-enabled",
"ship-from-store"
]
},
"severity": 0.94,
"depth": 1
},
{
"node_ref": {
"resource_type": "dc_pool",
"resource_id": 20410,
"display_name": "EFC-East Distribution Pool",
"display_code": "DC-EFC-EAST",
"status": "Active - 3 DCs",
"tags": [
"Primary allocation",
"2-day SLA zone"
]
},
"severity": 0.91,
"depth": 1
},
{
"node_ref": {
"resource_type": "channel_routing_rule",
"resource_id": 50822,
"display_name": "BOPIS Priority Override - NE-Tier1",
"display_code": "RTG-BOPIS-NE-T1",
"status": "Pending reconfiguration",
"tags": [
"BOPIS",
"channel-routing",
"SLA-critical"
]
},
"severity": 0.87,
"depth": 2
},
{
"node_ref": {
"resource_type": "planogram",
"resource_id": 61204,
"display_name": "Seasonal Planogram - NE Apparel",
"display_code": "PLN-NE-APP-S26",
"status": "Active - 12 stores affected",
"tags": [
"Apparel",
"markdown-eligible",
"clearance-threshold"
]
},
"severity": 0.72,
"depth": 3
}
],
"edges": [
{
"source": {
"resource_type": "allocation_policy",
"display_code": "ALLOC-NE-SEASONAL"
},
"target": {
"resource_type": "store_cluster",
"display_code": "SC-NE-T1"
},
"edge_type": "ALLOCATES_TO",
"provider": "allocation",
"label": "Store cluster assignment - 18 stores, SKUs HM-4401-HM-4488"
},
{
"source": {
"resource_type": "allocation_policy",
"display_code": "ALLOC-NE-SEASONAL"
},
"target": {
"resource_type": "dc_pool",
"display_code": "DC-EFC-EAST"
},
"edge_type": "SOURCED_FROM",
"provider": "allocation",
"label": "DC pool sourcing - routing split to EFC-East + EFC-Central"
},
{
"source": {
"resource_type": "store_cluster",
"display_code": "SC-NE-T1"
},
"target": {
"resource_type": "channel_routing_rule",
"display_code": "RTG-BOPIS-NE-T1"
},
"edge_type": "ROUTES_VIA",
"provider": "fulfillment",
"label": "BOPIS priority override - ship-from-store fallback"
},
{
"source": {
"resource_type": "store_cluster",
"display_code": "SC-NE-T1"
},
"target": {
"resource_type": "planogram",
"display_code": "PLN-NE-APP-S26"
},
"edge_type": "DISPLAYS_ON",
"provider": "merchandising",
"label": "Assortment presentation - clearance thresholds and in-stock targets affected"
}
],
"stats": {
"node_count": 41,
"edge_count": 67,
"provider_counts": {
"allocation": 18,
"fulfillment": 12,
"merchandising": 7,
"margin": 4
},
"truncated": false,
"collisions": {
"collision_count": 0,
"collision_severity": "NONE"
}
}
}GET Retrieve exposure map
Get the full impact graph with exposure scores, stock-out risk, and affected stores/DCs for a retail policy change.
GET /api/v1/change-controls/{id}/impactGET Trace proof path
Explain why a specific store, DC, or allocation zone is impacted, auditable at every policy hop.
GET /api/v1/change-controls/{id}/impact/explain?node_key=channel_routing_rule:50822:headResponse
{
"run_id": 445,
"target_node_key": "channel_routing_rule:50822:head",
"path_node_keys": [
"allocation_policy:3010:head",
"store_cluster:30100:head",
"channel_routing_rule:50822:head"
],
"path_edges": [
{
"edge_type": "ALLOCATES_TO",
"provider": "allocation",
"label": "ALLOC-NE-SEASONAL assigns SKUs HM-4401-HM-4488 to store cluster NE-Tier1"
},
{
"edge_type": "ROUTES_VIA",
"provider": "fulfillment",
"label": "NE-Tier1 BOPIS orders route through RTG-BOPIS-NE-T1 - DC split changes ship-from-store fallback priority"
}
],
"notes": "2-hop path: allocation policy → store cluster → BOPIS routing rule. DC pool split changes the ship-from-store fallback order, requiring BOPIS priority reconfiguration across 18 stores."
}GET Detect cross-change collisions
Find where concurrent allocation or routing changes create overlapping impacts on shared inventory nodes.
GET /api/v1/change-controls/{id}/impact/collisionsResponse
{
"collision_count": 3,
"colliding_change_ids": [
441,
449
],
"collision_severity": "HIGH",
"top_overlapping_nodes": [
{
"node_key": "store_cluster:30100:head",
"severity": 0.94,
"change_ids": [
445,
441
],
"display": "SC-NE-T1 Store Cluster - overlaps with CC-441 (markdown cadence acceleration for NE Apparel)"
},
{
"node_key": "dc_pool:20410:head",
"severity": 0.91,
"change_ids": [
445,
449
],
"display": "DC-EFC-EAST - overlaps with CC-449 (ship-from-store expansion adding 6 NE locations)"
}
]
}POST Generate readiness pack
Assemble capacity analyses, SLA impact summaries, and store readiness checklists in preview or apply mode.
POST /api/v1/change-controls/{id}/verification-pack/generateRequest
{
"mode": "preview"
}Response
{
"proposed_validations": [
{
"validation_type": "data_validation",
"validation_meta": {
"description": "Store pick capacity assessment for NE-Tier1: BOPIS volume shift from DC-pool split requires pick team rebalancing across 18 locations",
"affected_nodes": [
"store_cluster:30100:head",
"channel_routing_rule:50822:head"
],
"check_dimensions": [
"Pick rate vs. projected BOPIS volume",
"Ship-from-store fallback capacity",
"Staging area throughput"
]
}
},
{
"validation_type": "document_review",
"validation_meta": {
"description": "2-day SLA risk assessment: EFC-East to EFC-Central routing split may push 7 zip codes outside guaranteed delivery window",
"affected_nodes": [
"dc_pool:20410:head",
"dc_pool:20415:head"
],
"sla_tiers_at_risk": [
"2-day ground",
"next-day BOPIS"
]
}
},
{
"validation_type": "checklist",
"validation_meta": {
"description": "Seasonal assortment revalidation: planogram PLN-NE-APP-S26 presentation quantities and in-stock targets shift under new DC-pool split",
"affected_nodes": [
"planogram:61204:head"
]
}
}
],
"proposed_external_acknowledgements": [
{
"target_type": "CARRIER",
"target_id": 8820,
"reason": "DC-pool routing split changes parcel origin mix for NE-Tier1 zone. Carrier rate table and pickup schedule may require update"
}
]
}POST Estimate margin impact
Estimate one-time change cost (rule reconfiguration, documentation, replanning) and recurring impact (cost-to-serve deltas, margin shifts) with min/likely/max uncertainty bounds.
POST /api/v1/change-controls/{id}/cost-estimateResponse
{
"estimate_id": 2087,
"impact_analysis_run_id": 445,
"line_items": [
{
"cost_driver_type": "nre",
"description": "Channel routing reconfiguration: BOPIS priority override and ship-from-store fallback rules for 18 NE-Tier1 stores",
"quantity": 18,
"unit_rate": 1250,
"cost_phase": "nre",
"min_cost": 18000,
"likely_cost": 22500,
"max_cost": 30000,
"confidence": 0.85
},
{
"cost_driver_type": "nre",
"description": "Store pick team rebalancing: labor allocation updates across 18 locations for shifted BOPIS and ship-from-store volume",
"quantity": 18,
"unit_rate": 950,
"cost_phase": "nre",
"min_cost": 14000,
"likely_cost": 17100,
"max_cost": 22000,
"confidence": 0.82
},
{
"cost_driver_type": "nre",
"description": "DC-pool split activation: inventory rebalancing between EFC-East and EFC-Central for SKUs HM-4401-HM-4488",
"quantity": 88,
"unit_rate": 320,
"cost_phase": "nre",
"min_cost": 22000,
"likely_cost": 28160,
"max_cost": 38000,
"confidence": 0.78
},
{
"cost_driver_type": "nre",
"description": "Assortment and clearance documentation: seasonal planogram PLN-NE-APP-S26 presentation revalidation and clearance threshold updates",
"quantity": 12,
"unit_rate": 1600,
"cost_phase": "nre",
"min_cost": 15000,
"likely_cost": 19200,
"max_cost": 25000,
"confidence": 0.8
},
{
"cost_driver_type": "recurring",
"description": "Per-order cost-to-serve increase: split DC sourcing adds avg $0.38/order for NE-Tier1 zone from parcel origin shift",
"quantity": 1,
"unit_rate": 0.38,
"cost_phase": "recurring",
"min_cost": 0.28,
"likely_cost": 0.38,
"max_cost": 0.52,
"confidence": 0.88,
"justification": "Carrier rate modeling for EFC-Central origin to NE-Tier1 zip codes: +$0.38 avg vs. EFC-East single-source baseline"
}
],
"nre_range": {
"min": 69000,
"likely": 86960,
"max": 115000
},
"recurring_range": {
"min": 0.28,
"likely": 0.38,
"max": 0.52,
"currency": "USD",
"description": "Per-order recurring cost-to-serve increase from split DC-pool sourcing"
},
"schedule_impact": {
"min_schedule_days": 5,
"likely_schedule_days": 8,
"max_schedule_days": 12,
"critical_path_nodes": [
"dc_pool:20410:head",
"channel_routing_rule:50822:head"
]
},
"confidence": 0.83,
"confidence_notes": "Estimate calibrated from your operational data. DC inventory rebalancing timeline and carrier rate finalization are the primary uncertainty drivers.",
"justification_summary": "Allocation policy ALLOC-NE-SEASONAL DC-pool split drives $87K one-time cost (routing reconfiguration across 18 stores, pick team rebalancing, DC inventory rebalancing for 88 SKUs, assortment revalidation) plus $0.38/order recurring cost-to-serve increase. DC transition and BOPIS routing reconfiguration are the critical path at 8 days."
}GET Export impact graph
Export the full impact graph as JSON, CSV, or GraphML for integration with OMS, WMS, or merchandising systems.
GET /api/v1/impact-analysis-runs/{run_id}/export?format=graphmlPreview endpoints reflect the planned VGE surface. Final routes may adjust as the engine deploys to production.
FAQ
Common questions about Impact Intelligence for retail + omnichannel.
How does VGE handle multi-channel inventory allocation?
The engine traverses the full allocation hierarchy (stores, DCs, online pools, and transfer networks) using domain providers that understand parent-child, regional, and channel-specific allocation relationships. Scope is configurable per analysis run.
Can Impact Intelligence preview allocation cascades before deployment?
Yes. Preview mode is the default. Seed an analysis with your proposed routing or allocation rule and VGE computes the full exposure map, readiness work, and margin impact without persisting any changes. Iterate as many times as needed before applying.
What happens when a fulfillment routing change shifts order volume?
VGE detects the volume shift as a change signal and computes the downstream impact: affected nodes, capacity limits breached, SLA risk, and cost-to-serve delta. A readiness pack assembles capacity analyses and store readiness checklists for review.
Does it integrate with our existing OMS or WMS?
Impact Intelligence runs on your EquatorOps operational data. Results export as JSON, CSV, or GraphML for integration with order management systems (Manhattan, Blue Yonder), warehouse management platforms, and merchandising tools.
How are concurrent allocation changes detected?
Cross-change collision detection compares exposure maps of all active and in-review changes. When overlapping nodes are found, high-exposure collisions automatically create triage queue entries with evidence paths for human resolution.
Are margin estimates reliable for merchandising decisions?
Margin estimates produce uncertainty ranges (min/likely/max) with confidence scores, not false precision. Ranges are calibrated from your actual data: fulfillment costs, shipping rates, SLA penalties, and historical allocation outcomes.
Can we customize exposure scoring for our channel strategy?
Yes. Exposure parameters are tenant-configurable to match your risk profile. Map them to your channel governance framework so scoring reflects your actual business priorities.
What about changes that affect markdown and clearance flows?
Markdown providers traverse pricing-to-allocation-zone relationships, clearance thresholds, and margin floor rules. A markdown policy change surfaces every channel, location, and SKU that depends on those pricing constraints.
How does Impact Intelligence handle promise accuracy and cancellation risk?
When routing or promise rules change, VGE computes the downstream effect on order promising: which zones lose 2-day coverage, where split-shipment probability increases, and which stores face cancellation risk from insufficient inventory. The exposure map surfaces these signals before the rule goes live.
Does Impact Intelligence replace our OMS or distributed order management (DOM) system?
No. Impact Intelligence is an analysis layer that sits alongside your OMS/DOM. It reads the routing rules, promise logic, and allocation policies your OMS enforces, then models the change impact of proposed changes before you deploy them. Results export as JSON, CSV, or GraphML for integration back into your order management workflow.