How Many Distribution Centers Do You Need?
We Stepped One Network From 16 DCs Down to 1

Most networks weren’t designed. They grew, one DC at a time. We took the footprint demo in the ReliaSim® Sandbox, sixteen DCs serving North America, and asked the engine to re-plan it at every count from 16 down to 1. Here’s what the cost curve showed.

Sixteen DCs, the way they run today

The network serves 658 customer demand points across North America, with a total demand of 257,255,779. Sixteen DCs serve them, from Vancouver to Mexico City. Each customer is assigned to a DC as the network runs today.

The engine scores a network by demand times distance: every customer’s demand, multiplied by how far it travels from its DC, added up. Today’s sixteen DCs score 62,946,533,361. The engine’s own table shortens that to 62,947M, and so do we from here on.

The same sixteen, re-planned

First, close nothing. Keep all sixteen DCs open and let the engine move each customer to its best DC. The score drops from 62,947M to 48,132M, a cut of 14,815M, with the same buildings.

That’s worth checking before any closure talk starts. Part of what looks like a footprint problem can be an assignment problem.

The cost curve, 16 DCs to 1

Next, the engine closes DCs one count at a time. At each count it picks which DCs stay open and re-assigns every customer.

Swipe sideways to see the whole chart.

Loading the engine results…

Re-planned at each count Today’s sixteen DCs, 62,947M

Eight DCs, placed well, cost less than today’s sixteen

With eight DCs open, the score is 61,447M. That’s 1,500M below today’s sixteen at 62,947M. The eight the engine keeps are Atlanta, Chicago, Dallas, Denver, Mexico City, Newark, Phoenix and Seattle.

Seven DCs is where it stops beating today: 65,841M.

The engine picks the set again at every count, so the best four aren’t just the best eight with four more closed. At four DCs it keeps Atlanta, Dallas, Reno and Toronto. Reno and Toronto aren’t in the best eight. At one DC it keeps Memphis.

Past the bend, every closure costs more

Near the top of the curve, a closure costs little. From 16 DCs to 15, the score goes from 48,132M to 48,477M, up 345M. Lower down, each closure costs far more:

• 5 DCs to 4: 81,530M to 95,530M, up 14,000M.
• 2 DCs to 1: 145,038M to 208,417M, up 63,379M.

This score counts distance only. It has no rent, labor or inventory in it. Those costs fall as DCs close, so the right count sits somewhere on this curve where the savings from a closure still beat the added distance. The curve tells you what each closure costs in distance. Your finance team fills in the other side.

What else changes when you add or close a DC

More DCs shorten the trip to customers, so outbound freight falls and deliveries get faster. But each DC carries fixed costs for the lease, the staff and the systems. Inbound freight rises because full trucks from the plant get split across more destinations. Total inventory rises too, because every site needs its own buffer.

The inventory penalty of more DCs

Each DC has to cover the demand swings of its own customers. When one DC serves the whole country, a slow week in the West and a busy week in the East partly cancel out. Split the same demand across several DCs and each one faces its own swings alone, so each needs its own safety stock.

A common rule of thumb, the square root law (Maister, 1976), says total safety stock grows with the square root of the number of stocking locations. Under its assumptions, going from one DC to four roughly doubles the safety stock needed for the same service. Those assumptions include demand split evenly, independent from region to region, and the same lead time and service target everywhere. Real networks break most of them, so treat the number as a warning, not an answer.

Put the other costs on the curve

• fixed cost per DC, including lease, labor and systems;
• outbound freight by lane, with real rates in place of distance;
• inbound freight from plants or ports to each DC;
• inventory carrying cost at each site, including the safety stock each one needs;
• any service rule you have to meet, such as next-day delivery to a share of customers.

With those in the model, the question becomes a facility-selection optimization that weighs every cost together. Supply chain optimization vs simulation covers how that works, and greenfield, brownfield and footprint explains how the questions differ.

Test the count before you commit

An optimizer picks a count from averages. It can’t show how stock behaves when demand swings week to week, or whether a DC runs short while it waits for the plant. Before you sign leases, run the chosen network through a year of realistic demand in a simulation and watch stock at each site. How to know your supply chain model is right covers making that run trustworthy.