Perimattic
Warehouse and distribution center

Demand OS for Distribution & Wholesale

Demand planning AI software built for distribution networks

Node-by-node safety stock, ignored lead time variance, and misaligned replenishment signals are structural — not staffing — problems. The distribution template ships with multi-echelon optimization, lead time variance modeling, and replenishment recommendations on day one.

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What does demand planning AI software do for distribution and wholesale?

Demand planning artificial intelligence software for distribution sets safety stock at every DC and branch jointly instead of node by node, models lead time as a distribution rather than an average, and turns the result into replenishment recommendations a planner or system can act on directly. The measurable outcomes are lower system-wide safety stock at the same service level, fewer stockouts driven by lead time tail risk, and less planner time spent reconciling manual order reviews.

The Distribution Problem

Node-by-node planning adds cost the network never needed

Every node optimizes itself
When the DC and each branch set safety stock independently, the combined system carries far more inventory than the service level requires. The math is right at each node; the network total is wrong.
The plan trusts the average lead time
A safety stock formula built on average lead time is correct on average — which means it fails exactly when a supplier lane runs long. Variance, not the mean, is what drives stockouts.
Replenishment cycles amplify themselves
Order signals generated separately at each node, often from different systems, compound demand variability as they move up the chain — the bullwhip effect, produced by a planning gap.

What the Distribution Template Includes

Capabilities mapped to distribution failure modes

DISTRIBUTION FAILURE MODE
Safety stock set independently at the DC and every branch inflates system-wide inventory even though each node’s local math checks out.
Multi-Echelon Inventory Optimization
Sets safety stock at every DC and branch jointly, accounting for the upstream buffer position, so total network inventory is minimized for the same service level target.
DISTRIBUTION FAILURE MODE
Safety stock formulas built on average lead time produce targets that fail exactly when a lane runs long.
Lead Time Variance Modeling
Models lead time as a distribution by supplier and lane, not a point estimate, and sizes the buffer for tail risk rather than the mean.
DISTRIBUTION FAILURE MODE
Order signals generated separately at each node compound demand variability into a bullwhip effect up the chain.
Scenario Planning
Lets planners model the network-wide impact of a supplier delay, DC capacity constraint, or demand spike before committing to a replenishment run.
DISTRIBUTION FAILURE MODE
A recommendation that still needs manual translation before a planner or ERP can act on it is not a decision, it’s more work.
Replenishment Recommendations
Generates order recommendations per DC from current inventory position, open POs, safety stock targets, and the rolling forecast, ready for review or direct ERP/WMS export.

A Week in the Distribution Workbench

From Monday's network sync to Friday's approved orders

MON
Network position refreshes
Inventory position, open POs, and sales order history sync from ERP/WMS; lead time signals from the TMS update the variance model.
TUE–WED
Exception & lane review
Planners review flagged nodes and supplier lanes showing lead time drift, overriding targets with a logged reason where they know better.
THU
Scenario check
A supplier delay or demand spike is modeled network-wide before any replenishment run is committed.
FRI
Orders write back
Approved replenishment recommendations export to ERP, WMS, or TMS, node by node, with a full audit trail.

In Practice

A wholesale distributor reduces network safety stock by 22% without a service level drop

The Situation
A specialty wholesale distributor with 1 central DC and 8 regional branches had set safety stock independently at each node. Total network inventory was $34M. The central DC was consistently over-stocked on the same SKUs the branches were running out of.
What Demand OS Did
Inventory Optimization calculated safety stock at all 9 nodes jointly using multi-echelon logic. Lead time variance by supplier lane was incorporated from TMS data. The system identified 340 SKUs where central DC buffer duplicated branch safety stock and proposed consolidated targets.
The Outcome
Network safety stock was reduced by $7.5M (22%) over two planning cycles. Service levels remained flat across branches. Replenishment recommendations from Demand OS replaced the weekly manual order review, cutting planner preparation time by 60%.
Distribution center warehouse racking managed by AI-driven replenishment

Measurable Distribution Outcomes

What distribution teams measure after go-live

Demand planning artificial intelligence software earns its keep in the numbers distribution leadership already tracks. Demand OS reports all three continuously, from the pilot's first cycle.

20–30%typical safety stock reduction from multi-echelon vs. node-by-node optimization (Gartner Supply Chain)15–25%of distribution inventory attributable to lead time variance the plan never modeled (McKinsey Operations Practice)5–6 wksto a live pilot on one DC or region — network expansion follows in 4-week phases
Related

Explore Demand OS for Distribution

Inventory Optimization

Service-level-driven safety stock that shows the working capital impact of every recommendation.

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Demand Sensing

Daily signal detection that sharpens the near-term forecast before replenishment decisions are made.

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Demand OS vs ToolsGroup

Probabilistic forecasting depth plus explainability and a planner-first workbench.

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Frequently Asked Questions

Distribution demand planning, answered

What is the best demand planning artificial intelligence software for distribution and wholesale?+
The best distribution demand planning AI sets safety stock across every DC and branch jointly, models lead time as a distribution instead of an average, and turns the result into replenishment recommendations that plug straight into ERP or WMS. Demand OS ships all of that in its distribution template, with a scoped pilot live in 5–6 weeks and published pricing before any sales call.
How does Demand OS handle multi-echelon inventory across DCs and branches?+
Inventory Optimization uses a multi-echelon model that sets safety stock at each network node jointly, accounting for the upstream buffer position. The result is a total system inventory target that is lower for the same service level than node-by-node calculation.
How is lead time variance incorporated into safety stock calculations?+
Demand OS treats lead time as a statistical distribution, not a single average. Historical lead time data by supplier and lane is used to estimate variance, and safety stock is calculated from both demand variance and lead time variance — correctly sizing the buffer for actual supply risk.
Can Demand OS generate replenishment recommendations automatically?+
Yes. Demand OS generates order recommendations at each DC based on current inventory position, open purchase orders, safety stock targets, and the rolling demand forecast. Recommendations are surfaced as a work queue for planner review and can be exported to ERP or WMS systems for execution.
What ERP and WMS systems does Demand OS integrate with for distribution?+
Native connectors are available for SAP EWM, Oracle WMS, Manhattan Associates, Blue Yonder WMS, NetSuite, and Microsoft Dynamics 365. Inventory position, open purchase orders, and sales order history are pulled automatically on a configurable schedule.
What is the typical implementation timeline for a distribution pilot?+
A distribution pilot covering one DC or region typically goes live in 5–6 weeks. Network expansion to additional nodes follows in 4-week phases. The most complex element is usually lead time data extraction from the TMS or carrier records, which can extend the timeline if data quality work is required.

Get Started

See the distribution template live

A scoped pilot with your DC network, your branch locations, and your supplier lanes — live in 5–6 weeks. You leave with accuracy benchmarks, an echelon-aware rollout plan, and published pricing.

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