Inventory & Replenishment

See tomorrow’s inventory before you place today’s order

Multi-echelon inventory optimization built on trustworthy forecasts, per-SKU policies and interactive simulation — less stock, fewer stockouts, faster turns.

10%+
Lower inventory at Unilever: Per SKU
Policies by item and location: Multi-echelon
Plant → DC → store

Inventory Monitoring Agent

Spots stockout and overstock risk across every location
Chooses between transfer and purchase for each gap
Drafts replenishment orders and routes them for approval

Runs continuously on the same ontology and optimizer as the rest of your planning.

Why Convect for inventory & replenishment

Inventory & Replenishment, done right

Starts from forecasts you can trust

Replenishment uses Convect’s backtested, multi-model demand forecasts — so every recommendation stands on reliable demand insight.

Reliable forecasts
Backtested models
Data-driven decisions

A policy for every item

Items are segmented automatically and each segment gets the replenishment logic that fits it — with safety stock that adapts as conditions change.

Policies by segment
Item-level overrides
Dynamic safety stock

Interactive inventory simulation

Change an order quantity or date and instantly see projected inventory, stockout probability and days of cover.

Adjust plans by hand
Projected inventory in real time
Compare strategies side by side

Platform

How the three layers power inventory & replenishment

Inventory & Replenishment isn’t a standalone tool. It runs on the same ontology, optimizer and agents as production scheduling, so plans connect end to end.

AI Planning Agents

An inventory monitoring agent spots stockout and overstock risk and drafts replenishment or transfer proposals for approval.

Contour Ontology

The full network — plant → regional DC → forward DC → store — with lead times, minimum order quantities and transfer rules, modeled once.

Optimization Engine

A multi-period network model optimizes replenishment, transfers and production together, replacing tier-by-tier rules of thumb.

How the platform works →

Scenarios

Typical scenarios

Multi-echelon replenishment

Right-sized safety stock for every SKU at every DC

Store replenishment

Daily or weekly store orders that respect sales and shelf constraints

Stock rebalancing

When stock is uneven, choose optimally between transfer and purchase

Inventory health check

Find excess, obsolete and at-risk stock and size the opportunity

Customer results

Proven by our customers

Consumer goods

Unilever cuts network inventory by 10%+ at the same service level

For a nationwide, multi-echelon distribution network, Convect combined probabilistic demand forecasting, network-wide deployment optimization and simulation to set the right safety stock for every SKU at every DC — cutting inventory by more than 10% in simulation, with service unchanged.

10%+

Lower inventory (simulated)

Same

Service level maintained

Every DC

SKU-level safety stock

Read the story

Retail

Decathlon lifts forecast accuracy from 55% to 85%+

With new products making up more than a third of the range and strong seasonality, Decathlon used Convect’s Algoflow engine to automatically select and blend the right model for each item — giving replenishment and production a forecast they can trust.

55% → 85%+

Forecast accuracy

1/3+

Of range is new products

Auto

Model selection per item