Distribution Network Design
Redesign the Network You Have, Then Watch It Run

Distribution network design decides how many DCs you run, where they are, which customers each one serves and where the inventory sits. Most of the time you’re changing a network that already exists. An optimizer proposes the new design. A simulation shows how it behaves week to week before anyone signs a lease or moves a customer.

The four decisions

A distribution network design comes down to four linked choices:

• How many DCs. More sites put stock closer to customers. Each one also adds a building, a crew and its own pile of inventory. How many distribution centers do you need? covers the count in detail.
• Where they are. Which buildings, cities or candidate sites. This includes which existing DCs stay open.
• Which customers each DC serves. The allocation. Two networks with the same sites can perform very differently if customers are assigned differently.
• Where inventory sits. At the plant, at a central DC, at regional DCs, or split across them. This decides how much stock the network carries and how fast it can recover from a surprise.

The decisions pull on each other. Close a DC and its customers move to neighbors, those neighbors need more stock, and the plant’s shipments change shape. That’s why it pays to look at them together.

Most projects start from a network you already have

A blank-sheet study asks where you’d put DCs if you started over. That’s greenfield network design, and it’s a useful baseline. But most companies aren’t new entrants. They have leases, automation, key customers and a footprint that grew by acquisition. The real questions sound like this:

• Which of our DCs could we close, and where would their customers go?
• If we add one site, which customers should move to it?
• Are customers assigned to the DC that serves them best, or to the one that served them first?
• Should the new regional DCs carry safety stock, or pull from a central one?

That’s brownfield work: you pin the sites that can’t close, mark the rest optional, and re-draw the allocation. It often turns up savings before anything closes, just by moving customers to a better DC. The Footprint rationalization demo shows that step on a network of sixteen DCs.

Optimization proposes, simulation tests

Network design uses two kinds of tools, and they do different jobs.

Optimization searches the choices and returns the best combination under its assumptions: which sites open, which customers go where, how much moves on each lane. It works from averaged demand, usually annual or monthly. Supply chain optimization vs simulation covers the math and where it stops.

Simulation takes one candidate design and runs it forward through time. Demand varies day to day. Sites reorder on their own schedules and in real order sizes. Shipments take as long as their lanes take. You see inventory at every site over the run, and every shipment.

An average hides the weeks that matter. A DC that looks fine over a year can run dry every peak season, because its replenishment from the plant can’t keep up with the customers it just inherited. The optimizer can’t see that. The simulation shows it.

A worked example

A company runs one plant and six DCs serving about 300 customer points. An optimizer, working on annual demand, proposes closing two DCs and moving their customers to the nearest remaining site. On paper the four-DC network ships the same volume with two fewer buildings.

The average said Atlanta could take the extra customers. The run shows it can in most weeks, but not during the peak, when its weekly reorders from the plant arrive too late. Now there are three options to compare:

1. raise Atlanta’s order point and order quantity;
2. move twenty of Atlanta’s new customers to Dallas;
3. keep one of the two closing DCs open through the peak.

Run each against the same year of demand, so the only difference between runs is the change. In this illustration, moving customers to Dallas fixes most of the peak without adding stock, and the idle stock at Reno can come down. The point isn’t the specific answer. It’s that you saw the problem and tested the fixes before the leases ended.

What to look for when a candidate runs

• Stock against the order point at every site. Does it stay above zero through the worst season, or drain before the replenishment lands?
• The upstream tier. When the plant or a central DC runs low, the DCs below it wait. Multi-echelon inventory covers how stock at one tier buffers the next.
• Stock that never moves. A site that never draws down is carrying more than it needs.
• Shipments on the map. Lanes that carry far more or less than expected often point to an allocation problem.

Before you trust any of it, check the model against a period of history. How to know your supply chain model is right covers how.

How to do it with ReliaSim

ReliaSim’s supply chain tools keep every step on one model, so the design the optimizer proposes is the same design the simulator tests.

1. Lay out the network you have. Plants, DCs, customers and lanes as a graph, drawn from your site list and demand history.
2. Pin what can’t change. Mark the sites that must stay open and let the engine choose among the rest, the way the Brownfield · EU demo does.
3. Optimize the candidates. Solve for footprint, allocation and flow. Keep the two or three best designs, not just the top one.
4. Simulate each candidate through a year that includes your worst season, with its inventory policy in place. Adjust where stock drains or sits idle, and run it again.

Models are desktop files on your own machine. The supply chain simulation software page covers what the tools do, and the supply chain simulation guide covers when to use them.