Center of Gravity Analysis
Where Demand Pulls a Warehouse, and Where the Method Stops

Center of gravity analysis finds the spot on a map that sits closest to your demand, with each customer weighted by how much it buys. It’s the quickest defensible first answer to “where should the warehouse go?”, and it’s worth knowing exactly what it leaves out.

What the method does

Picture your customers as weights on a flat map, each one as heavy as its annual volume. The center of gravity is the point where the map would balance. A big customer pulls the point toward it and a small one barely moves it. Put a single warehouse there and the total of volume times distance to customers is about as low as it can go.

It answers one question well: if distance to customers were the only thing that mattered, where would one facility go? That’s a narrow question, but it’s often the right place to start a siting conversation, because anyone can follow the logic.

How to run one

1. List the customers or delivery points with a latitude and longitude for each. Group small ones by ZIP or city if there are thousands.
2. Pick the weight. Annual units, weight shipped or shipments per year. Use whatever drives your outbound freight cost, and use the same measure for every customer.
3. Take the weighted average of the latitudes and of the longitudes. Each customer’s coordinate counts in proportion to its weight.
4. Refine it if you need to. The weighted average is close to the true minimum-distance point but not exactly on it. A few rounds of an iterative correction close the gap. The formulas are in greenfield network design.
5. Snap it to a real place. The math can land in a lake or a field. Move the answer to the nearest town with the roads, labor and buildings a DC needs.

A small worked example

Three customers on a simple grid, with made-up volumes to show the arithmetic:

x = (0·100 + 10·300 + 0·100) / 500 = 6
y = (0·100 + 0·300 + 10·100) / 500 = 2

The point (6, 2) sits much nearer B than A or C, because B buys three times as much as either. Double C’s volume and the point moves up and to the left. That sensitivity is the whole method. The answer is only as good as the weights you give it.

What it leaves out

Center of gravity is a starting point. It ignores several things that decide real siting.

• Inbound freight. It only looks at customers. A site close to demand but far from the plants or ports that supply it can cost more overall.
• Cost differences. Freight rates, labor, land and taxes vary by location. Distance stands in for all of them.
• Roads and geography. Straight-line distance ignores mountains, rivers and the highway network.
• Capacity. It will put one building where the math says, however big that building would have to be.
• More than one site. The single-point answer for a national network often lands somewhere no one would choose. Real networks need several DCs, and the question becomes how to split customers between them.
• Time. It uses one year of volume. It says nothing about seasons, growth or what happens to stock and service when demand varies from week to week.

More than one DC

With several DCs, you assign each customer to a DC and put each DC at the center of gravity of its own customers, then repeat until the assignments stop changing. That’s the greenfield method. The Greenfield · US demo runs it on 189 US demand points for two to eight DCs. How many DCs to use is a separate decision, covered in how many distribution centers do you need?

Run it in the Sandbox

The DC placement demo lays out one plant, four candidate DCs and five customer regions across the US, so you can see the geography a center of gravity would weigh. Greenfield · US then lets an engine do the weighting on real demand points, one DC count at a time.

Then simulate the answer

A center of gravity tells you where demand is. It doesn’t tell you whether a network built around that point will keep customers supplied. Once you have candidate sites, put them in a network model with real demand, lead times and ordering policies and run it through a year. That run shows how stock moves at each site, which the static answer can’t. How supply chain simulation works covers what a run does, and the supply chain simulation guide covers when to use it.