Preview policy change impact across locations, handling units (LPNs), and chain of custody before rollout
The Impact Intelligence Verification Graph Engine (VGE) maps warehouse policy change propagation across:
- Replenishment rules, slotting configurations, and directed putaway
- LPN hierarchies (pallet-case-unit lineage)
- Transfer queues and cross-dock schedules
- Multi-client segregation and chain-of-custody rules
Preview mode lets you catch inbound backlogs, inventory imbalances, and segregation violations before you deploy.
LPN hierarchy
Predict handling unit migration impacts:
- Pallet-case-unit lineage traversal
- Re-label and repack task generation
- Cube constraint and slotting violations
Location policy
Trace replenishment rule changes across your facility:
- Zone, aisle, and storage type impacts
- Reserve-to-forward replenishment effects
- Directed putaway rule conflicts
Chain of custody
Verify handoff integrity through warehouse workflows:
- Receiving, putaway, and transfer integrity
- Client segregation and quarantine compliance
- Lot and expiry control chain validation
Console preview
Change intelligence report
Seed change
Location policy change: Zone A3 pallet slot consolidation - 18 nested LPNs
Change impact
38 of 190 nodes (20%)
across 3 domains
Severity hotspots
3
critical
NRE estimate
$22K – $48K
likely $34K
Schedule delta
+5d
critical path
Impact cascade / sample path
Pallet slot PZ-A3-0712 - 18 nested case LPNs
via location_policy
3 replenishment triggers need threshold update
via replenishment
12 active pick paths rerouted
via pick_optimization
2 container policies conflict with new layout
via container_policy
4 client billing rules affected
via billing
1 another zone change overlaps this location cluster
- └─ WMS-0761 Put-away rule change - oversized pallet routing 2 shared nodes testing
Verification pack / draft
- 18 LPN relocations
- 3 replenishment threshold updates
- 12 pick path re-optimizations
- 2 container policy revisions
Cost estimate: NRE $22K – $48K (likely $34K)
- ├─ LPN re-slotting labor: $6K – $12K (18 nested LPNs, 3 crew shifts)
- ├─ Pick path disruption: $4K – $8K reduced throughput during transition
- ├─ Schedule penalty: +5d delays zone go-live by 1 week
- ├─ Throughput recovery: −$2K/mo savings after consolidation completes
The problem
Warehouse policy changes break workflows because dependencies aren't modeled
A min/max change, zone restructure, or handling-unit standard update can cascade through directed putaway, replenishment triggers, transfer queues, and client segregation rules. Without a dependency model, teams discover the breakage in receiving or wave execution, during peak volume, then pay for expedites, labor spikes, and reconciliation.
Hidden cost of blind changes
- Replenishment threshold increase: Raising forward-pick minimums at the primary DC. Triggers higher replenishment frequency, floods receiving and putaway work queues, and starves pick faces during the transition
- Handling-unit standard change: Switching pallet footprint from 48x40 to 42x42 at receiving. Breaks slotting and cube constraints, directed putaway rules, and pack configuration assumptions; requires re-label and repack tasks for affected handling units (LPNs)
- Client isolation policy change: Enabling commingled storage where prohibited by contract, mixing lots or expiry-controlled inventory, or zone reconfigurations risk breaking quarantine or hold segregation
- Transfer route reconfiguration: Rerouting transfers through new consolidation hub. Invalidates 5 receiving workflows, breaks 2 cross-dock schedules, and creates 48-hour backlog at destination
Key capabilities
Active intelligence for warehousing + 3pl changes.
LPN Hierarchy Traversal
Map parent-child relationships across pallet-case-unit lineage to predict how handling-unit migrations cascade through nested inventory structures. Identify which receiving workflows, directed putaway rules, and custody transfer events depend on specific LPN types before changing container standards.
Location Policy Propagation
Trace replenishment thresholds, slotting rules, and storage constraints across zones, aisles, and bin locations. Preview how min/max adjustments at primary locations trigger directed replenishment across satellite sites and identify conflicting rules before deployment.
Replenishment Cascade Analysis
Simulate how min/max threshold changes propagate through transfer queues, purchase order triggers, and cross-dock schedules. Identify which downstream locations will experience increased replenishment frequency and quantify inventory rebalancing volume.
Chain-of-Custody Verification
Validate handoff integrity through receiving, directed putaway, picking, and transfer workflows. Detect when policy changes break custody transfer events, create audit gaps, or violate client isolation requirements for regulated inventory.
Transfer Queue Impact
Analyze how location policy changes affect in-transit inventory, pending transfer orders, and cross-dock schedules. Preview queue backlogs, identify route conflicts, and quantify freight cost impacts before reconfiguring transfer workflows.
Client Isolation Checking
Verify that inventory policy changes maintain strict separation boundaries for multi-client 3PL operations. Detect when commingling where prohibited by contract, mixing lots or expiry-controlled inventory, or zone reconfigurations risk breaking quarantine or hold segregation.
Stock Rebalancing Preview
Quantify inventory redistribution volume triggered by location policy changes. Calculate transfer order counts, freight costs, and labor hours required to rebalance stock across the network after replenishment rule adjustments.
Receiving and Directed Putaway Rule Impact
Map dependencies between location policies and inbound workflows. Identify which ASN processing rules, directed putaway strategies, and receiving dock assignments will break when storage constraints or handling-unit standards change.
How it works
From change signal to verified action.
Define location policy change
Specify the inventory policy adjustment: replenishment threshold revision, slotting rule update, handling-unit standard change, or storage constraint modification. Include location scope (zone/aisle/bin) and affected SKU ranges.
Map LPN hierarchy dependencies
The graph engine traverses pallet-case-unit lineage to identify which nested handling units, receiving workflows, and custody transfer events depend on the policy change. Detects pack configuration mismatches and directed putaway rule conflicts.
Trace location policy propagation
Impact analysis propagates replenishment rules across parent-child location relationships, identifying which satellite sites will trigger rebalancing transfers and quantifying inventory redistribution volume.
Simulate replenishment cascades
The engine recalculates min/max thresholds across all affected locations, predicting which sites will experience increased replenishment frequency and modeling transfer queue backlogs.
Verify chain-of-custody integrity
Audit workflows check that policy changes maintain custody transfer events through receiving, directed putaway, picking, and transfer steps. Flags broken custody assignments and client isolation violations.
Generate interactive impact report
Visual dependency graph shows LPN lineage, location policy cascades, and transfer queue impacts. Drill down to specific receiving workflows, directed putaway rules, and custody records affected by the change.
Export remediation plan
Receive prioritized action list: handling-unit relabeling tasks, slotting rule updates, transfer order adjustments, and custody record corrections required before deploying the location policy change.
Hybrid graph model
The warehouse dependency graph models locations (zones/aisles/bins), LPNs (license plates for handling units such as pallets, cases, and units, analogous to GS1 SSCC identifiers in broader supply chain labeling), SKUs with pack configuration and UOM hierarchy (each/case/pallet), transfer routes, custody records, and client assignments. Edges represent slotting rules, directed replenishment policies, handling-unit nesting, custody transfer workflows, and isolation boundaries. Policy changes trigger graph traversal to identify cascading impacts across the distribution network.
SLOTTED_IN
Links SKUs to storage locations with directed putaway rules, replenishment thresholds, and capacity constraints. Used to predict how location policy changes affect SKU assignments.
NESTED_WITHIN
Represents LPN hierarchy: cases within pallets, units within cases. Enables handling-unit migration impact analysis by traversing parent-child lineage.
TRANSFERS_TO
Connects locations via transfer routes with lead times, freight modes, and cross-dock schedules. Maps how directed replenishment changes propagate through multi-site networks.
ISOLATED_FOR
Links inventory and locations to client ownership and segregation rules. Enforces multi-client isolation boundaries in 3PL operations.
RECEIVES_AT
Links ASN and dock-door workflows to storage locations and directed putaway strategies. Maps inbound processing dependencies.
SCHEDULED_FOR
Links locations to cycle count calendars and scheduled verification work. Detects conflicts when policy changes overlap active count windows.
CUSTODY_TRANSFERRED_TO
Records custody transfer events between parties and workflow steps. Tracks chain-of-custody integrity through receiving, putaway, picking, and transfer handoffs.
Warehousing + 3PL impact scenarios
Real change scenarios in warehousing + 3pl.
Impact Intelligence adapts to your domain’s change patterns, compliance frameworks, and verification workflows. These are representative output examples from the VGE computation pipeline.
Warehousing + 3PL
Zones affected · LPN relocations · Pick path efficiency deltaTrigger
Location rule change
Impact
LPN placement, zone capacity, replenishment triggers, pick path efficiency
Verification Pack
Zone impact report, capacity analysis, pick path simulation
Metrics
Zones affected · LPN relocations · Pick path efficiency delta
Warehousing + 3PL
Transfer volume change · Receiving impact · Buffer level shiftsTrigger
Replenishment threshold update
Impact
Transfer order frequency, receiving capacity, inventory buffer levels
Verification Pack
Replenishment simulation, transfer volume forecast, receiving capacity check
Metrics
Transfer volume change · Receiving impact · Buffer level shifts
Warehousing + 3PL
LPNs requiring restructure · Custody gaps · Ledger adjustmentsTrigger
Container policy revision
Impact
LPN nesting rules, chain-of-custody records, item lineage, ledger entries
Verification Pack
Nesting compatibility report, custody gap analysis, ledger adjustment plan
Metrics
LPNs requiring restructure · Custody gaps · Ledger adjustments
Warehousing + 3PL
Items requiring relocation · Zones affected · Transition timelineTrigger
Zone restructure
Impact
Inventory positions, pick zones, staging areas, replenishment paths
Verification Pack
Zone transition plan, inventory relocation schedule, pick zone reassignment
Metrics
Items requiring relocation · Zones affected · Transition timeline
Warehousing + 3PL
Sites affected · Transfer queue changes · Reconciliation complexityTrigger
Multi-site transfer policy change
Impact
Inter-site transfer queues, custody transfers, receiving SLAs, ledger reconciliation
Verification Pack
Cross-site impact report, transfer queue simulation, reconciliation plan
Metrics
Sites affected · Transfer queue changes · Reconciliation complexity
Impact Intelligence for Warehousing + 3PL
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 Warehousing + 3PL
Preview change impact, severity scoring, and verification packs before approvals.
Change impact
38 nodes
Projected change
Severity hotspots
3
Projected change
NRE estimate
$34K
Projected change
Schedule delta
+5d
Projected change
Sample finding
Predict handling unit migration impacts:
Impact cascade
Define location policy change
SLOTTED_IN
NESTED_WITHIN
TRANSFERS_TO
ISOLATED_FOR
RECEIVES_AT
SCHEDULED_FOR
CUSTODY_TRANSFERRED_TO
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 an impact analysis for a replenishment threshold change, container policy revision, or zone restructure.
POST /api/v1/change-controls/{id}/impact/runThe ChangeControl record (created separately) carries the zone reconfiguration details: merging pick zones PZ-A3 and PZ-A4 into consolidated zone PZ-A34, updating replenishment triggers for 14 slot assignments, and revising wave planning templates for wave WV-2026-0089.
Request
{
"detect_collisions": true
}Response
{
"schema_version": "vge.graph_result.v1",
"run_id": 156,
"nodes": [
{
"node_ref": {
"resource_type": "zone",
"resource_id": 30041,
"display_name": "Pick Zone PZ-A3 - Ambient Fast-Move",
"display_code": "PZ-A3",
"status": "Active - 42 slot assignments",
"tags": [
"Ambient",
"Fast-Move",
"Wave-Eligible"
]
},
"severity": 0.94,
"depth": 1
},
{
"node_ref": {
"resource_type": "lpn",
"resource_id": 78234,
"display_name": "Pallet LPN-00478234 - 18 cases nested",
"display_code": "LPN-00478234",
"status": "In-Zone - PZ-A3-0712",
"tags": [
"Pallet",
"Client-Acme",
"Replen-Source"
]
},
"severity": 0.87,
"depth": 2
},
{
"node_ref": {
"resource_type": "pick_path",
"resource_id": 5019,
"display_name": "Pick Path PP-A3-FAST - 23 stops",
"display_code": "PP-A3-FAST",
"status": "Active - 6 waves/day",
"tags": [
"Zone-PZ-A3",
"Batch-Pick",
"Conveyor-Fed"
]
},
"severity": 0.91,
"depth": 2
},
{
"node_ref": {
"resource_type": "replenishment_trigger",
"resource_id": 6102,
"display_name": "Replen Trigger RT-A3-0712 - min 24 cases",
"display_code": "RT-A3-0712",
"status": "Armed - threshold breach in 4 hrs",
"tags": [
"Forward-Pick",
"Case-Level",
"Auto-Wave"
]
},
"severity": 0.85,
"depth": 3
}
],
"edges": [
{
"source": {
"resource_type": "zone",
"display_code": "PZ-A3"
},
"target": {
"resource_type": "lpn",
"display_code": "LPN-00478234"
},
"edge_type": "SLOTTED_IN",
"provider": "location_policy",
"label": "Pallet slot PZ-A3-0712 - 18 nested case LPNs"
},
{
"source": {
"resource_type": "zone",
"display_code": "PZ-A3"
},
"target": {
"resource_type": "pick_path",
"display_code": "PP-A3-FAST"
},
"edge_type": "ROUTED_THROUGH",
"provider": "wave_planning",
"label": "Fast-move pick path - 23 stops across PZ-A3 aisles"
},
{
"source": {
"resource_type": "lpn",
"display_code": "LPN-00478234"
},
"target": {
"resource_type": "replenishment_trigger",
"display_code": "RT-A3-0712"
},
"edge_type": "REPLENISHES",
"provider": "inventory_policy",
"label": "Pallet-to-forward replen - min 24 cases, current 27"
}
],
"stats": {
"node_count": 38,
"edge_count": 61,
"provider_counts": {
"location_policy": 18,
"wave_planning": 9,
"inventory_policy": 14,
"container_hierarchy": 7
},
"truncated": false,
"collisions": {
"collision_count": 0,
"collision_severity": "NONE"
}
}
}GET Retrieve impact snapshot
Get the full change impact with severity scores across zones, LPN records, transfer queues, and custody assignments.
GET /api/v1/change-controls/{id}/impactGET Explain proof path
Trace why a specific zone, LPN record, or custody assignment is impacted, auditable at every dependency hop.
GET /api/v1/change-controls/{id}/impact/explain?node_key=pick_path:5019:headResponse
{
"run_id": 156,
"target_node_key": "pick_path:5019:head",
"path_node_keys": [
"zone:30041:head",
"slot_assignment:30041_0712:head",
"pick_path:5019:head"
],
"path_edges": [
{
"edge_type": "SLOTTED_IN",
"provider": "location_policy",
"label": "Zone PZ-A3 contains 42 slot assignments including PZ-A3-0712"
},
{
"edge_type": "ROUTED_THROUGH",
"provider": "wave_planning",
"label": "Slot PZ-A3-0712 is stop 14 of 23 on pick path PP-A3-FAST. Zone merge invalidates stop sequence"
}
],
"notes": "2-hop path: zone → slot assignment → pick path. Merging PZ-A3 into PZ-A34 invalidates the existing 23-stop sequence; PP-A3-FAST must be re-optimized with PZ-A4 slots appended."
}GET Detect cross-change collisions
Find where concurrent location policy changes create overlapping impacts on shared zones or client isolation boundaries.
GET /api/v1/change-controls/{id}/impact/collisionsResponse
{
"collision_count": 2,
"colliding_change_ids": [
149,
153
],
"collision_severity": "MEDIUM",
"top_overlapping_nodes": [
{
"node_key": "replenishment_trigger:6102:head",
"severity": 0.85,
"change_ids": [
156,
149
],
"display": "RT-A3-0712 Replen Trigger - overlaps with CC-149 (replenishment threshold reduction for forward-pick zone PZ-A3)"
},
{
"node_key": "lpn:78234:head",
"severity": 0.79,
"change_ids": [
156,
153
],
"display": "LPN-00478234 Pallet - overlaps with CC-153 (container policy migration from 48×40 to 42×42 pallets in zone PZ-A3)"
}
]
}POST Generate verification pack
Generate zone impact reports, custody gap analyses, LPN relabeling plans, and transfer volume forecasts in preview or apply mode.
POST /api/v1/change-controls/{id}/verification-pack/generateRequest
{
"mode": "preview"
}Response
{
"proposed_validations": [
{
"validation_type": "automated_test",
"validation_meta": {
"description": "Re-optimize pick path PP-A3-FAST for consolidated zone PZ-A34: merge 23 stops from PZ-A3 with 19 stops from PZ-A4, recalculate conveyor-fed batch sequence",
"affected_nodes": [
"pick_path:5019:head",
"pick_path:5024:head"
],
"wave_ids": [
"WV-2026-0089",
"WV-2026-0090"
]
}
},
{
"validation_type": "inspection",
"validation_meta": {
"description": "Validate 14 replenishment triggers after zone merge: verify min/max thresholds remain correct for consolidated slot assignments in PZ-A34",
"affected_nodes": [
"replenishment_trigger:6102:head",
"replenishment_trigger:6108:head",
"replenishment_trigger:6115:head"
],
"threshold_checks": [
"Forward-pick case minimums",
"Pallet reserve-to-forward ratios",
"Auto-wave trigger intervals"
]
}
},
{
"validation_type": "checklist",
"validation_meta": {
"description": "LPN relabeling plan: update zone references on 47 pallet LPNs and 312 nested case LPNs currently slotted in PZ-A3 and PZ-A4, reassign to PZ-A34 zone code",
"affected_nodes": [
"lpn:78234:head",
"lpn:78251:head"
]
}
}
],
"proposed_external_acknowledgements": [
{
"target_type": "WMS_INTEGRATION",
"target_id": 2201,
"reason": "Zone merge PZ-A3 + PZ-A4 → PZ-A34 requires WMS location master update and RF scanner zone-code refresh for 8 picking stations"
}
]
}POST Compute cost estimate
Estimate NRE costs (zone reconfiguration, documentation updates, relabeling labor) and recurring impact (transfer cost changes, receiving efficiency deltas) with min/likely/max uncertainty bounds.
POST /api/v1/change-controls/{id}/cost-estimateResponse
{
"estimate_id": 483,
"impact_analysis_run_id": 156,
"line_items": [
{
"cost_driver_type": "nre",
"description": "Re-slot 42 positions from PZ-A3 and 36 positions from PZ-A4 into consolidated zone PZ-A34. Includes physical signage, bin label replacement, and WMS location master updates",
"quantity": 78,
"unit_rate": 185,
"cost_phase": "nre",
"min_cost": 11700,
"likely_cost": 14430,
"max_cost": 18700,
"confidence": 0.85
},
{
"cost_driver_type": "nre",
"description": "Update zone references on 47 pallet LPNs and 312 nested case LPNs. Print replacement labels, scan-verify pallet-case-unit lineage, update chain-of-custody records",
"quantity": 359,
"unit_rate": 24,
"cost_phase": "nre",
"min_cost": 6800,
"likely_cost": 8616,
"max_cost": 11500,
"confidence": 0.88
},
{
"cost_driver_type": "nre",
"description": "Re-optimize pick paths PP-A3-FAST and PP-A4-STD into merged path PP-A34-OPT. Recalculate stop sequences, update conveyor sort logic, reprogram RF scanner routing for 8 stations",
"quantity": 2,
"unit_rate": 3100,
"cost_phase": "nre",
"min_cost": 4800,
"likely_cost": 6200,
"max_cost": 8400,
"confidence": 0.8
},
{
"cost_driver_type": "nre",
"description": "WMS zone master update, wave planning template revision for WV-2026-0089, replenishment trigger recalibration for 14 forward-pick slots, and integration testing across 3 picking shifts",
"quantity": 1,
"unit_rate": 4800,
"cost_phase": "nre",
"min_cost": 3800,
"likely_cost": 4800,
"max_cost": 6400,
"confidence": 0.82
},
{
"cost_driver_type": "recurring",
"description": "Per-wave pick path efficiency gain: consolidated zone reduces travel distance by ~12%, saving 8 minutes per wave across 6 waves/day",
"quantity": 1,
"unit_rate": -18.5,
"cost_phase": "recurring",
"min_cost": -22,
"likely_cost": -18.5,
"max_cost": -12,
"confidence": 0.72,
"justification": "Time-motion study of current PP-A3-FAST (23 stops) and PP-A4-STD (19 stops) vs projected PP-A34-OPT (34 stops with optimized sequence)"
}
],
"nre_range": {
"min": 27100,
"likely": 34046,
"max": 45000
},
"recurring_range": {
"min": -22,
"likely": -18.5,
"max": -12,
"currency": "USD",
"description": "Per-wave recurring cost reduction from consolidated pick path efficiency"
},
"schedule_impact": {
"min_schedule_days": 3,
"likely_schedule_days": 5,
"max_schedule_days": 8,
"critical_path_nodes": [
"pick_path:5019:head",
"replenishment_trigger:6102:head"
]
},
"confidence": 0.84,
"confidence_notes": "Estimate calibrated from your operational data. Pick path re-optimization and RF scanner reprogramming across 3 shifts are the primary schedule drivers.",
"justification_summary": "Zone merge PZ-A3 + PZ-A4 → PZ-A34 drives $34K NRE (slotting reconfiguration for 78 positions, LPN relabeling for 359 pallet-case containers, pick path reprogramming for 2 routes, WMS configuration updates) with $18.50/wave recurring efficiency gain from consolidated pick path. Pick path re-optimization across 8 stations is the critical path at 5 days."
}GET Export impact graph
Export the full impact graph as JSON, CSV, or GraphML for integration with WMS platforms or inventory planning tools.
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 warehousing + 3pl.
How does Impact Intelligence maintain multi-client isolation in 3PL operations?
The graph model enforces strict client boundaries through ISOLATED_FOR edges linking inventory to client assignments. When analyzing location policy changes, the engine validates that commingling where prohibited by contract, mixing lots or expiry-controlled inventory, or zone reconfigurations risk breaking quarantine or hold segregation. Reports flag any chain-of-custody breaks or segregation risks before deployment.
Can Impact Intelligence trace LPN lineage through nested container hierarchies?
Yes. NESTED_WITHIN edges represent pallet-case-unit relationships, enabling full handling-unit hierarchy traversal. When migrating handling-unit standards (e.g., 48×40 to 42×42 pallets), the engine identifies which child LPNs require relabeling, which slotting and cube constraints break, and which directed putaway workflows need reconfiguration.
How does the system predict receiving workflow disruptions from location policy changes?
Impact analysis maps RECEIVES_AT edges linking ASNs, dock doors, and directed putaway workflows to storage locations. When location policies change (e.g., replenishment thresholds, slotting rules), the engine identifies which directed putaway strategies, dock assignments, and receiving sequences will break, helping prevent inbound backlogs.
Does Impact Intelligence account for cycle count schedules when analyzing location changes?
Yes. The system tracks SCHEDULED_FOR edges linking locations to cycle count calendars. Replenishment threshold changes or zone reconfigurations that conflict with active cycle counts trigger warnings, preventing inventory accuracy disruptions during audit windows.
How does the tool integrate with existing WMS systems for real-time impact analysis?
Impact Intelligence ingests location master data, LPN hierarchies, and transfer queues via WMS APIs (REST/SOAP) or flat file exports (CSV/XML). The graph model syncs nightly or on-demand, ensuring impact previews reflect current warehouse state. Results export as actionable task lists compatible with WMS work order formats.
Can I simulate replenishment cascades across a multi-site distribution network?
Yes. TRANSFERS_TO edges model inter-site relationships with lead times and freight modes. When adjusting replenishment thresholds at a primary DC, the simulation engine predicts which satellite locations will trigger rebalancing transfers, quantifies inventory redistribution volume, and estimates freight costs before deployment.
How does Impact Intelligence handle emergency rollbacks for broken location policies?
The rollback complexity calculator analyzes which transfer orders are in-flight, which LPNs are physically relabeled, and which receiving workflows are actively processing ASNs. It generates a prioritized reversal plan: cancel pending transfers, restore slotting rules, update LPN records, and reconfigure directed putaway workflows, minimizing operational downtime.
Does the system detect when handling-unit changes violate chain-of-custody requirements?
Yes. Chain-of-custody verification cross-references handling-unit migrations against CUSTODY_TRANSFERRED_TO edges linking LPNs to client requirements. If a regulated SKU's pallet type changes in a way that breaks audit traceability or violates handling protocols, the analysis flags the violation before deployment.