Multi-Echelon Inventory
Set Stock for the Whole Network, Not One Site at a Time

When stock sits at more than one tier, the tiers depend on each other. A regional DC is only as reliable as the central DC that refills it. Multi-echelon inventory sets stock across all the tiers together, and simulation shows what those settings do over time.

What an echelon is

An echelon is one tier of stocking locations. A typical consumer goods network has three:

• The plant, which holds finished goods as they come off the line;
• A central or national DC, which takes full loads from the plant;
• Regional DCs or stores, which serve customers and reorder from the tier above.

Each tier is the supplier of the tier below it. A regional DC’s order from the central DC ships right away if the central DC has stock, and waits if it doesn’t. The Three-tier network demo shows the shape: suppliers, two plants, two DCs and three customer regions.

Why setting stock one site at a time over-stocks

The usual way to set targets is site by site. Each planner takes their own site’s demand and lead time and sizes a buffer, often with the safety stock formula. That formula treats the site as if it stood alone, with a supplier that always has stock.

Do that at every tier and the network pays for the same uncertainty more than once. The regional DC buffers against a late refill from the central DC. The central DC buffers so the regional DC never waits. The plant buffers so the central DC never waits. Each buffer is sensible on its own. Together they cover the same risk two or three times.

The opposite mistake happens too. A site that looks lean on its own can be exposed, because it assumed a supplier tier that is itself running close to empty.

How stock at one echelon buffers the next

Stock held upstream pools variation. A slow week in one region and a busy week in another partly cancel out at the central DC, so one pooled buffer can be smaller than the sum of the regional ones. Stock held downstream sits next to customers and answers a surprise right away, but each site has to cover its own swings.

The upstream tier also sets the effective lead time for everyone below it. If the central DC keeps enough stock to ship most orders the same day, a regional DC’s lead time is just the transit time. If the central DC often runs short, the regional DC’s real lead time includes the wait, and it needs more stock to cover it.

Multi-echelon optimization weighs those against each other and picks a target at every site at once. There’s no single right answer. In the Safety stock demo, the optimizer puts all of the safety stock at the three regional DCs and none at the plant or the central DC. A network with different lead times and costs can come out the other way. Where stock sits also depends on the form it’s in, which the postponement case study settles product by product.

A worked example

One plant feeds a central DC, which feeds four regional DCs. Planners set targets site by site. Each regional DC holds two weeks of its own demand as safety stock. The central DC holds two weeks of the whole network’s demand, and the plant holds one more week.

The whole-network policy holds about half the safety stock. The question is whether it holds up. So run both policies through the same year of demand, including a fall peak, and watch every site.

In this illustration the leaner policy holds through most of the year. In the two busiest weeks the central DC drops close to zero. Regional orders placed that week wait a few days for stock, and two regional DCs fall below their order points before the refill lands. Raising the central DC’s target by 500 units fixes it in the re-run. At 5,400 units, the total is still close to half the site-by-site figure.

Neither table alone told you that. The optimizer gave a starting point, and the run showed the one week where it was thin.

What simulation shows across the tiers

A simulation plays the whole network forward. Demand draws stock at the regional DCs. They reorder from the central DC on their own review schedule and in real order sizes. The central DC ships what it has and reorders from the plant. Nothing is averaged away.

• Stock at every site against its order point, over the whole run, so you can see which site drains first and when.
• The knock-on from upstream. When the central DC runs low, the regional orders that wait show up in the run.
• Idle stock. A site whose stock never approaches its order point is holding more than it needs.
• Orders and shipments on every lane, so you can see how the policy changes what moves and when.

Run two or three candidate policies against the same demand, so the only difference between runs is the policy. That’s how you compare reorder points, order quantities and review periods across tiers, not just safety stock. The Simple supply chain demo shows what that view looks like, with stock at each location plotted against its order point through a demand spike.

A practical sequence

1. Map the tiers and who reorders from whom.
2. Get a first set of targets from a multi-echelon optimizer, not from each site’s formula on its own.
3. Validate the model against a period of history. How to know your supply chain model is right covers how.
4. Simulate the policy through your worst season and adjust the tier that drains or the one that never moves.

If the number or location of DCs is also changing, settle that first. Distribution network design covers the footprint and allocation side. The supply chain simulation software page covers what ReliaSim’s tools do, and the supply chain simulation guide covers when to use them.