Supply Chain Signals delivers configurable AI workflows that connect your existing TMS, WMS, ERP, and planning systems — turning fragmented data into decisions in real time.
Supply chains generate data. We turn it into decisions.
Most enterprises have invested heavily in TMS, WMS, and planning platforms — yet still rely on spreadsheets and tribal knowledge to bridge the gaps between them. Supply Chain Signals closes that gap.
Inventory signals trapped in your WMS never reach your TMS. Supply Chain Signals connects these signals across every system and acts on them.
02 — Reactive Operations
You're managing yesterday's exceptions today.
By the time a disruption surfaces in a dashboard, your options are limited. Supply Chain Signals detects risk early, while you still have room to act.
03 — Automation at Scale
High-volume decisions require machine speed.
Carrier tendering, replenishment, PO acknowledgments — at enterprise volume, these can't wait for human review. We automate the routine, escalate the exceptional.
04 — Configuration, Not Customization
No two supply chains are the same.
Supply Chain Signals runs on configurable logic engines, not rigid templates. Your rules and constraints drive the AI — not the reverse.
0%
avg. inventory cost reduction
0×
faster procurement cycles
0%
improvement in network visibility
0+
pre-built platform connectors
Ready to wire intelligence into your supply chain?
Start with one workflow. Scale across the full network.
From statistical baselines to ML-powered sensing, align supply with demand before gaps become disruptions. Ensemble models ingest order history, POS data, and external signals — then reconcile at the consensus level for S&OP review.
Ensemble ML across ARIMA, gradient boosting, and neural nets
Short-horizon sensing from POS and downstream signals
Native sync to Kinaxis, SAP IBP, o9, and Blue Yonder
Global Trade, Automated
Compliance that keeps up with the world.
Tariff schedules shift, sanctions lists update, and trade lanes reroute faster than manual teams can track. Supply Chain Signals monitors regulatory change continuously and flags risk before it reaches your operations.
Automated HTS classification with confidence scoring
Real-time OFAC and restricted-party screening
FTA utilization and duty drawback identification
Distribution in Motion
Floor decisions at machine speed.
Order waves, slotting, and labor allocation that respond to what demand is actually doing — not yesterday's static plan. Built for high-velocity fulfillment where the right call can't wait for the next shift.
AI-driven slotting by velocity, weight, and pick adjacency
Adaptive wave sequencing by SLA and carrier cut time
Native integration with Manhattan, Blue Yonder, SAP EWM
Every workflow. Fully configurable.
Deploy one or connect all seven as a unified intelligence layer.
AI that works the way your operations actually do.
Supply Chain Signals is a configurable AI layer — not a replacement for your stack. It connects to the systems you've already invested in and turns their data into action.
How It Works
Connect, configure, operate.
1Connect
Integrate in days, not months.
Pre-built connectors for the major enterprise platforms your teams already run, with event-driven data flow in real time.
REST, GraphQL, and webhooks
Full EDI X12 & EDIFACT support
40+ pre-built connectors
2Configure
Your business rules. Your logic.
Every workflow is configurable through a no-code interface — thresholds, approval hierarchies, escalation paths, and AI autonomy levels.
No-code workflow studio
Role-based approval rules
Autonomy levels per action
3Operate
AI acts. Humans stay in control.
Supply Chain Signals recommends, automates, and escalates — with full explainability and a complete audit trail at every decision point.
Plain-language rationale
Full decision audit trail
Exception-based review queues
Core Capabilities
Built for enterprise-grade operations.
Explainability by Design
Every AI decision carries a plain-language rationale and confidence score. No black boxes. Full audit trail for governance.
Configurable Autonomy
Set automation thresholds per workflow and decision type. The AI acts within your parameters and escalates outside them.
Continuous Learning
Models retrain on your operational outcomes. The longer it runs, the sharper recommendations get — calibrated to your network.
Security & Compliance
SOC 2 Type II certified. Data residency across US, EU, and APAC. SSO/SAML, MFA, and full data lineage tracking.
Real-Time Processing
Event-driven architecture processes signals in milliseconds. No nightly batch delays — decisions on what's happening now.
Tenant Isolation
Complete data isolation per client. Your models, training data, and configurations are never shared or co-mingled.
See Supply Chain Signals in action.
We demo against your workflows and integration environment — not a generic sandbox.
Strategy, build, and run — at whatever depth you need.
Supply Chain Signals is both a platform and a practice. Some clients want a roadmap. Others want us to build. Many want us to run the intelligence layer as an extension of their team.
Advisory
Strategy & roadmap
For teams who need clarity before committing capital. We assess your current state, find the highest-ROI opportunities, and build the business case.
Supply chain AI maturity assessment
Opportunity identification & prioritization
Technology selection & vendor evaluation
ROI modeling & executive business case
Typical engagement: 4–8 weeks
Most popular
Implementation
Build, integrate & deploy
For teams ready to deploy. We configure the workflows, build the integrations to your TMS, WMS, ERP, and planning systems, and stand it up in production.
Workflow configuration & AI model tuning
System integration (API, EDI, event streams)
Data pipeline design & validation
UAT, parallel run, cutover & hypercare
Typical engagement: 12–24 weeks
Managed AI Operations
Run it as your team
For teams who want outcomes, not overhead. We operate the intelligence layer continuously — monitoring, tuning models, managing exceptions, and reporting on results.
Continuous model monitoring & retraining
Exception management & escalation handling
Quarterly business reviews & optimization
Dedicated success & engineering pod
Typical engagement: Ongoing retainer
Delivery Methodology
A proven path from signal to outcome.
1
Phase 01
Discover
We map your current state — systems, data flows, decision points — and quantify the opportunity before a line of config is written.
2
Phase 02
Design
We architect workflows, integration points, and decision logic. Your rules, approvals, and autonomy thresholds are defined and signed off.
3
Phase 03
Deploy
We build, integrate, and validate in a controlled environment, run in parallel against your current process, then cut over with hypercare.
4
Phase 04
Optimize
Models learn from outcomes, recommendations sharpen, and we expand scope. Quarterly reviews tie results back to the business case.
Not sure where to start?
A discovery conversation costs nothing and gives you a clear point of view.
Pre-built connectors across every supply chain domain — plus open REST APIs and full EDI support. No rip-and-replace. Supply Chain Signals works alongside your existing technology investments and starts moving data on day one.
Live Connector ActivityLive
0records synced todayacross 40+ connectors
Transportation (TMS)
Blue Yonder TMSOracle OTMSAP TMMercuryGateManhattan TMSDescartes3Gtms
Built by practitioners who've lived inside supply chains.
We've worked inside logistics operations, implemented TMS and WMS platforms at scale, managed post-go-live hypercare through the hard parts, and navigated the gap between what enterprise software promises and what operations actually experience.
Every Supply Chain Signals deployment starts from your operating model, your data, and your decision rights — not a template. Configurable by principle, human-in-the-loop by design.
Our team brings deep practitioner experience across automotive, aerospace and defense, consumer goods, pharmaceutical, and third-party logistics.
Let's talk about your supply chain.
Every engagement starts with a discovery conversation — no pitch decks.
Cut freight spend while improving on-time delivery. The AI handles carrier selection, mode optimization, and real-time exception management — autonomously, at the volume and speed your operations require.
Intelligent order wave planning, slotting, and labor allocation that responds to what demand is actually doing — not yesterday's static plan. Built for high-velocity fulfillment environments.
From statistical baselines to ML-powered sensing, align supply with demand before gaps become disruptions. Native S&OP integration connects forecast outputs directly to procurement and production signals.
ML forecastingDemand sensingS&OPScenario modeling
What it does
Ensemble ML forecasting
ARIMA, gradient boosting, and neural-net methods with automated retraining.
Short-horizon sensing
POS, order-book, and downstream inventory signals refine the near-term view.
Consensus & S&OP
Consensus forecast workflow with override tracking and assumption logging.
Scenario modeling
What-if analysis for promotions, disruptions, and new product introductions.
Navigate tariffs, sanctions, and compliance requirements with an AI layer that monitors regulatory change continuously and flags risk before it reaches your operations. Built for cross-border complexity.
Model, stress-test, and redesign your physical footprint with AI-driven scenario analysis. From greenfield site selection to nearshoring — capital decisions backed by operational intelligence, not spreadsheet assumptions.
Automate the full PO lifecycle from requisition to receipt. Supplier collaboration, spend analytics, and deviation alerts — all connected and in sync with your ERP and demand signals.
PO lifecycleSupplier collab3-way matchSpend analytics
What it does
Demand-driven PO generation
Configurable buyer review and approval thresholds tied to demand signals.
Supplier collaboration
Portal for PO acknowledgment, ASN submission, and change management.
3-way match automation
Invoice processing with exception routing and dispute management.
Supplier scorecards
On-time delivery, fill rate, and lead time by supplier and commodity.
Integrates with
ERP & OMS
SAP S/4HANAOracle ERP CloudMS Dynamics 365IBM Sterling OMS
Supply Chain Signals evaluates velocity, cube fit, weight, ergonomics, co-pick affinity, and replenishment cost against every open face — then proposes the moves with the highest effort-recovery per labor minute.
Slotting Command Center — live optimization posture for DC-ATL-03
Travel Distance
−23.4%
▼ 4.1 pts vs last wave
Golden Zone Util.
78%
▲ A/B SKUs in ergo band
Picks / Labor Hr
142
▲ from 118 baseline
Annualized Savings
$1.24M
▲ realized + pipeline
How the AI re-slots
Fewer touches. Shorter trips. Better ergonomics.
Two of the highest-impact move types the optimizer runs automatically.
Co-pick consolidation
Pack SKUs picked together — together.
SKUs frequently ordered on the same line are scattered across the building, forcing long trips and two separate touches. The market-basket model finds the pairs and co-locates them — collapsing travel and merging touches into one.
Velocity + replenishment
High movers → golden-zone pallet.
It's really about getting high movers out of flow rack and into the golden zone. A fast-moving SKU in a shallow flow-rack lane empties constantly — every depletion is another replenishment touch. The engine promotes it to a deep pallet position at waist height: far more units per face means replenishment drops from several times a day to about once a week. Right-size the equipment to the velocity, and the touches disappear.
Inside the platform
A full slotting command center.
AI Slot Optimizer
Ranked moves, scored by effort recovery.
Every recommendation is scored on confidence and ft/day saved, tagged by driver — golden zone, affinity, congestion relief, or replenishment reduction — so planners accept the top moves and build a reslot wave in one click.
Effort-recovery per labor minute ranking
Constraint-validated, 91% avg confidence
Accept top moves → auto-build reslot wave
Velocity Heatmap · Digital Twin
Every pick face, color-coded by velocity.
A live digital twin of the facility where each cell is a pick face. Color encodes pick velocity against travel cost — click any face to inspect the SKU, its ergonomic band, and the AI reslot signal.
Velocity, utilization, and congestion layers
Slot-level inspector with reslot signal
Co-Pick Affinity
Market-basket pairs, ranked by lift.
Co-occurrence across millions of order lines surfaces the strongest SKU pairs. High-affinity pairs slotted near each other collapse travel and consolidate touches — with a one-click "Pair" action.
Co-pick rate, lift, and current distance
Co-locate / adjacent opportunity flags
Velocity & ABC
ABC × XYZ classification keeps fast movers in prime faces and demotes dead stock to bulk reserve.
Scenario Modeling
Test reslot strategies against the effort curve before committing labor to the floor.
Reslot Waves
Approved moves bundle into labor-balanced waves with full move history and compliance tracking.
A unified execution layer of autonomous agents that plugs into your WMS and host systems over microservices — orchestrating order release, allocation, floor routing, and exceptions. It meters the wave into the building just-in-time instead of dropping a 5,000-order batch on the floor at 8 AM.
Instead of releasing a 5,000-order batch at 8 AM, the Order Flow Agent meters orders into the WMS just-in-time — holding releases to keep each zone under its WIP cap and smoothing pack-bound flow so cutoffs stay comfortably reachable.
Just-in-time release against live WIP targets
Throttles pack-bound orders to smooth inflow
Peak floor load cut 41% vs. batch release
Allocation & Routing Agent
Pick the node that hits SLA cheapest.
Before an order is released, the agent scores the network — inventory thresholds, transit time, fulfillment cost — and picks the node that meets the SLA at the lowest cost. If the primary node is backlogged, it autonomously splits or reroutes.
Scored, multi-node fulfillment evaluation
Auto split / reroute on backlog
Exception Resolution Agent
Resolve exceptions in seconds, not queues.
When inventory comes up short or a carrier misses a pickup, traditional systems throw an error into a human queue. This agent acts — re-allocating shortages, upgrading ship method, or alerting CS — within seconds, with no human intervention. 91% straight-through, only low-confidence cases escalate.
149 exceptions auto-resolved per day
2.3s avg resolution vs. ~12 min manual
Order Trace
Every agent interaction, timestamped.
A full lifecycle trace for any order — received, allocated, metered into WMS, pick-routed, exceptions resolved, packed, shipped. Five agent touches, zero human touches, cutoff met with a 21-minute buffer.
End-to-end agent + human touch accounting
Full audit trail for every decision
Plugs into your WMS
Sits over your WMS and host systems via microservices and an event bus — no rip-and-replace.
Autonomy you control
Run each agent in Observe, Co-pilot, or Autonomous — set the level per agent and per action.
Explainable decisions
Every autonomous action carries a "why," a confidence score, and a full timestamped trace.
An agent that watches the floor and moves labor to meet it.
The Labor Agent watches demand and flow in every functional area, rebalances cross-trained associates where congestion builds, and pushes directed work to their handhelds — no supervisor walk-around, no waiting for the next shift huddle.
Live rebalancingDirected workLabor forecastFloor heatmapCross-training aware
Labor Command Center — live workforce posture across DC-ATL-03
Associates on floor
41/46
5 on break / flex
Avg utilization
84%
balanced by agent
Units / labor hr
138
▲ 9% vs standard
Areas flagged
3
Pick A · Pack · Ship
How the Labor Agent works
Sense the imbalance. Move the right people. Tell them why.
Rebalancing Workflow
Move cross-trained labor before it backs up.
The agent continuously compares demand to staffing in each area. When an imbalance crosses threshold — Sorting at 135%, Pick at 109% — it moves cross-trained associates from areas with slack and dispatches the instruction, with the reason attached. No supervisor walk-around.
Live utilization vs. headcount per functional area
Autonomous moves with a plain-language "why"
Reacts to pick surges, inbound trucks, pack slowdowns
Associate Directives
Directed work, straight to the handheld.
Every rebalance becomes a directed-work instruction pushed to the associate's RF handheld or wearable — where to go, what to do, and why it matters — with acknowledgement tracked back to the agent. Instructions are skill-checked, so the agent only directs associates cross-trained for the target area.
"Accept & go" assignments with priority and walk time
Acknowledgement tracking — acknowledged, en route, pending
Labor Forecast
Pre-position flex labor ahead of the curve.
The agent forecasts labor demand by area across the shift versus staffed headcount, spots the gaps before they happen, and builds a flex plan — pulling associates from areas with slack and dispatching them ahead of the spike, not after it.
Demand vs. staffed-capacity curve by hour
Pre-positioning flex moves scheduled ahead of demand
Floor Heatmap
A live spatial view of where work is piling up.
Toggle between congestion health — where work is backing up — and velocity health — where flow is healthy versus stalling. Click any area to inspect headcount, demand, throughput, and the agent's staffing recommendation.
Congestion and velocity layers across every zone
Area inspector with the agent's staffing call
Skill-aware moves
The agent only directs associates who are cross-trained for the target area — every move is feasible on arrival.
Cutoff-aware
Decisions weigh ship cutoffs and SLA pressure — protecting pick capacity and staging the right heads in time.
Roster & forecast aware
Ties live floor posture to the associate roster and the shift forecast — staffing decisions, not guesswork.
Every echelon, optimized at once — not one tier at a time.
Localized agents at every echelon — Plant, CDC, RDC, and Store — sense, negotiate, and rebalance inventory concurrently. No sequential, siloed planning cycle, so demand signals propagate in minutes instead of months and safety stock is pooled to hit target service at minimum cost.
Network Intelligence — node agents negotiating across Plant, CDC, RDC, and Stores
Network fill rate
98.6%
▲ across all tiers
Bullwhip index
1.3×
vs 3.8× sequential
Safety stock held
$4.1M
−18% at equal service
Forecast accuracy
91.2%
MAPE 8.8%
See it run
Agents negotiating across the network, live.
A walkthrough of the MEIO agents sensing a disruption, simulating the ripple across echelons, and rebalancing in real time.
MEIO in action — cross-node sensing, negotiation, and rebalancing
Inside MEIO
Sense, simulate, negotiate, rebalance.
MEIO Safety Stock
Pool safety stock across every tier.
Agents continuously recompute safety stock at every tier simultaneously. When a Store agent senses a localized spike, it negotiates with the RDC and CDC agents to shift stock dynamically — holding the network at target service with the minimum pooled safety stock, not on the next monthly cycle.
Current vs. agent-optimized target by echelon
Live cross-tier rebalancing on demand spikes
97–99% service at minimum pooled stock
Bullwhip Analysis
Damp the bullwhip across tiers.
Because variance is shared across the RDC tier and demand signals propagate upstream in minutes, order amplification collapses — a 1.3× bullwhip index versus 3.8× in a sequential, siloed planning world.
Variance shared across the echelon, not amplified
Signals propagate upstream in minutes
Autonomous Demand Sensing
React to the true signal, fast.
Demand sensing reads the real signal at the edge and feeds it straight into the network agents, so allocation reacts to what's actually selling — not a stale weekly forecast. Forecast accuracy holds at 91.2% (8.8% MAPE).
Edge demand signals fed to node agents
Allocation reacts to true demand, not stale plans
Cross-Node Exception Management
Simulate the ripple, then mitigate.
When a disruption hits — a port delay, supplier outage, or regional demand surge — the agents simulate the ripple across all echelons. The Planner consults the Supply agent for alternative capacity and the Finance agent for margin impact, then presents a prioritized, scored list of mitigations with SLA, margin, and lead-time trade-offs.
Multi-agent consult — planner, supply, finance
Ranked mitigations with SLA / margin / lead-time
Concurrent, not sequential
Every echelon plans at once. Demand signals propagate in minutes instead of waiting on the monthly cycle.
Margin-aware mitigations
The finance agent models margin and lost-sales risk on every option, so the recommended move protects the P&L.
Champion–challenger
Models compete continuously, so the network always runs on the best-performing policy for current conditions.
