Production Planning
Deciding how much to produce is one of the highest-stakes calls a clothing brand makes, and most founders make it on gut. This guide gives you a repeatable way to size a first run, read what actually sells, and reorder winners without freezing cash in dead stock.
Every clothing brand faces the same fork with each style: make too much or make too little. Overproduce and you are left with dead stock, cash frozen in boxes you will eventually mark down. Underproduce and you stock out on the exact product customers wanted, walking away from sales you already earned. Producing on guesswork means you are almost always wrong in one of those two directions.
Put a number on the downside. A bulk production run for a small brand runs roughly $20K to $80K, which is your per-unit cost multiplied by the factory minimum. That is real, committed cash, not a projection. Dead stock is not just the markdown you take to clear it; it is the money you cannot put toward the next drop, the fabric for a proven reorder, or payroll. Frozen inventory is the quietest way a growing brand runs out of runway.
The two errors are not symmetric. On a brand-new style with no demand history, the cost of overproducing (frozen cash plus eventual markdowns) usually outweighs the cost of a short stockout, because a stockout on a winner can be fixed with a reorder while dead stock can only be discounted. That asymmetry is the whole argument for starting small, and it is why the rest of this guide treats your first run as a paid experiment rather than a bet. For more on the cash side of this, see our guide to avoiding deadstock and overproduction.
MOQ, or minimum order quantity, is the smallest run a factory will accept for a given style and colorway. Factories set minimums because the expensive parts of production (fabric milling, marker and pattern setup, machine changeovers, and line time) are largely fixed per style. Spreading those fixed costs across more units is what makes each unit affordable, so below a certain quantity the factory either loses money or has to charge a per-unit price no small brand would pay.
MOQs vary widely by where and how you produce, per style and per color: domestic runs often start at 50 to 100 units, premium small-batch makers in places like Portugal or Turkey tend to sit at 100 to 300, and larger overseas factories in China or Vietnam commonly want 200 to 500 or more. So there is no single good MOQ for a startup; the right floor depends on the maker you choose and how much confidence you have in the style.
The catch is that a factory's minimum can be higher than your confidence in a style. When the floor is above what you would rationally test with, you have three moves: negotiate the minimum down, choose a domestic small-batch maker with a lower floor, or accept the floor and treat that first run as your test tier. Our guides on what MOQ means in clothing and how to negotiate a lower MOQ go deeper on each.
The reliable way to decide how much to produce is to match quantity to confidence, not to hope. You do not know how a new style will sell until it is in front of customers, so the first job of a production run is to buy that information as cheaply as the factory floor allows. Once the style proves itself, you scale. This is the confidence-tiered playbook, and it has three tiers.
Tier one is the test. You produce roughly the minimum needed to put the style in market and read real demand, accepting that some styles will not earn a second run. Tier two is the scale-up, once sell-through confirms the style has legs but before you commit to a large run. Tier three is the proven reorder, where the product has a track record and the main risk is stocking out, not overbuying.
This flips the enterprise model on its head. Large brands forecast a full season, commit to it, and manage markdowns when the forecast is wrong. A founder-led brand does not have the volume for that math to work, and it does not need to. You produce a little, watch what the cohort actually buys, and put your cash behind proven winners instead of a spreadsheet's prediction.
Here is the playbook with numbers. For a brand-new style, produce around 100 units to test. If you work with a domestic maker whose minimum is 50 to 100, you can often test right at the MOQ, which means your experiment costs no more than the floor the factory sets anyway. The goal of this run is not profit; it is a clean read on demand.
If the style sells through at a healthy pace at full price, step up to roughly 200 units on the next run. Once it has a real track record across multiple runs, a proven reorder in the 300 to 500 range is reasonable, because now your bigger risk is running out of a known winner. Notice that you never jumped straight to 500 on a hunch; each tier was earned by the data from the one before it.
The cost side shapes these numbers too. A useful way to read per-unit cost is by line: fabric is typically 40 to 60 percent, labor 20 to 30 percent, 3PL 5 to 15 percent, sampling 5 to 10 percent, and packaging 5 to 8 percent. Materials, with trims, are usually the largest and most cost-sensitive line at roughly 60 to 70 percent. That is why the real commitment in a run is the fabric you buy, not just the units you cut, and why testing small protects the line item that hurts most when a style misses.
Sizing a first run is not about picking a round, comfortable number. Work backward from three constraints: the factory's MOQ floor, the cash you can put at risk without straining the business, and how many styles and colorways you are producing at once. Every colorway is usually its own MOQ, so three colors of one style is three minimums, not one. On a test, fewer colors keeps your total commitment honest.
Within a run, the size breakdown (your size curve) decides how many units go to each size. Most apparel follows a rough bell curve concentrated in the middle sizes, with the tails (your smallest and largest) carrying fewer units. But a generic curve is a starting point, not an answer. Your category, fit, and audience move it, and a size that runs out first while another sits untouched is a curve that needs adjusting.
If you have already shipped a run, you are not guessing at the curve. Your own sell-through by size is the best predictor of the next breakdown. Brands on Shopify already hold this signal in their variant-level sales, which is one reason it helps to keep catalog data and production planning connected rather than scattered across separate files.
The metric that turns gut into data is sell-through: units sold divided by units received, measured over a window like 30, 60, or 90 days. A style that clears most of a small test run at full price in its first weeks is telling you something a forecast never could. One that limps along at a discount is telling you the opposite, early enough to matter.
Watch full-price sell-through and velocity, not just whether the units eventually cleared. Almost anything sells at 60 percent off; the real question is whether it sold at the price you set, fast enough to justify buying more. Read it by size and by color too, since the aggregate can hide that one variant carried the whole run while the rest dragged.
You do not need an enterprise demand-planning system to do this. You need to see your core sellers clearly and act on them. Silhouet surfaces reorder insight on your core sellers so you can restock what is working and skip what is not, and it keeps every collection in one place across its lifecycle so the read stays connected to the product record. For a deeper framework, see our guide to building a clothing brand reorder strategy.
Reordering a proven product should be the easiest decision a brand makes, but it is often the messiest. The demand signal is clear; the friction is operational. Six months after the first run, the tech pack is buried in an email thread, the BOM lives in a spreadsheet someone renamed, and the factory has questions you already answered once. Every reorder that starts from scratch adds cost and delay to your safest bet.
This is where production memory earns its place. When the spec, bill of materials, measurements, and construction notes stay attached to the product and carry forward, a reorder is a quantity decision, not a rebuild. Silhouet keeps that structured production data connected to each product so a known-good style is ready to reproduce, and its factory-readiness check reads the tech pack the way a factory would before you send it, catching the questions that would otherwise come back as a delay.
The payoff compounds. Every product you make teaches the system how your brand builds, so the next reorder and the next new style start closer to done. Sizing the reorder is the demand half of the decision; not having to rebuild the spec is the execution half, and both are what let you restock a winner in weeks instead of reopening a project.
The cheapest way to avoid overproduction is to design so you never overcommit in the first place. That starts with fewer, tighter styles. A focused collection where several pieces share the same fabric and trims lets you meet a factory's per-style minimum without over-buying any single style, because the material commitment is pooled across the line rather than gambled on one cut.
De-risk before you commit fabric. A clean sample or mockup, honest feedback, and even a pre-sell or made-to-order window on a new style all buy you demand signal before you pay for a bulk run. The order that matters is validate, then produce, not produce, then hope.
Silhouet helps at the earliest, cheapest stage of that loop. It turns a rough sketch into a clean mockup in minutes, drafts a factory-ready tech pack that you review and accept before anything is written, and syncs your Shopify catalog so the make side starts from the sell side you already have. Getting the product right before it is cut is the highest-leverage way to keep inventory in line, and it connects directly to turning your Shopify store into a production system.
There is no universal number, but a practical starting point is around 100 units per new style, produced at or near your factory's minimum so the test costs no more than the floor you have to hit anyway. Domestic small-batch makers often let you start at 50 to 100 units, which is ideal for a first style with no demand history. Treat that first run as a paid experiment, then scale the styles that actually sell.
Most apparel follows a rough bell curve with the bulk of units in the middle sizes and fewer at the smallest and largest ends, but the exact split depends on your fit and audience. If you have shipped a run before, your own sell-through by size is a far better guide than any generic curve. Adjust after each run based on which sizes sold out first and which sat.
Inventory is usually the largest single use of cash for a product brand, and a bulk run runs roughly $20K to $80K depending on your minimum and unit cost. Rather than spend a fixed percentage, tier your commitment: put small money behind unproven styles and concentrate your inventory budget on proven reorders. Keep enough cash uncommitted to actually restock a winner when the demand signal is clear.
It depends on where you produce. Domestic runs often start at 50 to 100 units per style and color, premium small-batch makers in Portugal or Turkey at 100 to 300, and larger overseas factories at 200 to 500 or more. For a startup testing new styles, the lower floors are usually worth the higher per-unit cost, because they let you match production to confidence instead of overcommitting.
Think in styles, not one big number. A tight first collection of a few styles that share fabrics and trims lets you meet per-style minimums without overbuying any single piece, and each style can start near its MOQ as a test. Concentrate your cash on the strongest one or two ideas rather than spreading a large run across many unproven styles.
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