Multi-Echelon Inventory Allocation Across Distribution Centers
When inbound stock lands and needs splitting across multiple distribution centers, allocation usually goes to whichever DC's replenishment request landed first or whichever regional manager pushes hardest, not to where network-wide demand and service-level risk actually justify it. Each DC optimizes its own stock position independently, so the network ends up with one region overstocked and shipping costly inter-DC transfers to cover another region's stockout — a problem multi-echelon planning is specifically meant to prevent, but that most mid-size operations don't have the tooling or the planner time to run manually across more than a couple of locations.
STARTING PRICE
From €799
Complex tier · Multi-system orchestration, custom logic, and higher-volume or higher-risk processing.
Get a quote →Saves roughly 8-12 hrs/week for a supply chain planning team.
How the automation works
We allocate inbound and re-balancing inventory across distribution centers based on network-wide demand signals, each DC's current service-level risk, and the actual cost of positioning stock at each location — not a simple even split or first-request-first-served rule. The allocation model treats the network as one system: a DC's real need is measured against its own forecast, lead time from source, and current stock position relative to its demand variability, so DCs serving less predictable demand carry proportionally more buffer than DCs with stable, easy-to-forecast demand. Inter-DC transfer recommendations are generated only when rebalancing genuinely reduces network-wide stockout risk by more than the transfer cost, so the system doesn't create unnecessary transfer volume chasing a marginal improvement.
Process flow
- 01
Network demand and stock data syncs trigger
Demand forecast, current stock position and service-level status per DC sync in from all connected distribution centers.
- 02
Model network-wide demand ai
Demand is modeled at the network level and disaggregated to each DC's service area, rather than each DC forecasting independently off only its own local history.
- 03
Assess DC-level service risk ai
Each DC's stockout risk is assessed against its own demand variability and lead time from source, so higher-variability DCs are recognized as needing proportionally more buffer.
- 04
Allocate inbound stock ai
New inbound stock is split across DCs based on relative network need and service risk, not an even split or whichever request arrived first.
- 05
Recommend inter-DC transfers ai
Rebalancing transfers between existing DC stock positions are recommended only when the reduction in network-wide stockout risk outweighs the transfer cost.
- 06
Issue allocation and transfer orders output
Final allocation quantities and any recommended transfers are issued as orders to each DC, with the underlying network rationale attached for planner review.
Inputs
- Demand forecast and history per DC
- Current stock position by DC and SKU
- Inter-DC transfer cost and lead time
- Service-level targets per DC or region
Outputs
- Inbound allocation quantity per DC
- Recommended inter-DC transfer list
- Network-wide service-risk report
- Allocation rationale log for planner review
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Allocating stock evenly across DCs regardless of actual regional demand systematically overstocks low-demand regions and understocks high-demand ones — allocation has to weight by each DC's real demand share, not headcount or an even percentage split.
- Optimizing each DC's stock position independently, without a network-wide view, produces exactly the overstock-in-one-region-stockout-in-another pattern this automation is meant to prevent — the model has to treat the network as one system, not a collection of independently optimized nodes.
- Recommending inter-DC transfers whenever any imbalance exists, without weighing transfer cost against the actual risk reduction, creates excessive transfer volume that erodes the margin the rebalancing was supposed to protect — transfers need a net-benefit threshold, not a trigger on any imbalance.
- A DC serving a region with genuinely more volatile demand needs a structurally larger safety buffer than a DC with stable demand, even at the same average volume — allocating by average demand alone under-serves the volatile region during its predictable spikes.
Frequently asked questions
How many distribution centers does this handle?
It scales to as many DCs as are connected; the network model is designed specifically for operations with more than two or three locations, where manual allocation becomes impractical.
Does it override each DC manager's local judgment?
It provides a network-optimized recommendation with the rationale attached; local managers can flag known local factors (a regional promotion, a facility constraint) that feed into the next allocation run.
How are inter-DC transfer costs factored in?
Actual transfer lead time and cost per lane are used, and a transfer is only recommended when the network-wide stockout risk reduction exceeds that cost, not on any detected imbalance.
What happens when a new DC comes online?
A new DC starts with an initial allocation based on comparable regional demand until it accumulates enough of its own history to be modeled directly.