MOQ Risk Calculator

Your supplier quotes a minimum order quantity. This tells you how much of it you have to sell before it stops losing money, how likely that is, and what the order is worth in expectation.

Built for buyers, category managers and demand planners at mid-size distributors and manufacturers — HVAC and seasonal equipment, sanitary and building materials, technical distribution (valves, fittings, components), electrical components and professional equipment. It is aimed at the new-item stocking decision: one order, no sales history, a supplier MOQ and a lead time you cannot shorten.

Your numbers

Dead stock typically liquidates at 10–40% of cost; 0 if it is written off
Over the period you would sell this order
45% is normal for a product with no history

Results update as you type. Nothing is sent anywhere — the calculation runs in this browser tab.

To break even on 1,000 units you must sell
542 units
that is 54% sell-through — and you have a 51% chance of reaching it
€3,715
Expected outcome at MOQ
margin earned minus value destroyed
583
Expected units sold
demand P10 318 — P90 962
417
Expected leftover
worth €16,515 at salvage
9%
Stock-out risk
demand exceeds the order
This MOQ is workable
Break-even is 54% sell-through, and demand clears it in 51% of simulated outcomes.

Assumptions used

Everything the answer above rests on. Print this page and the numbers and the assumptions go to your supplier together — a break-even you can point at is a different conversation from an opinion about volume.

Margin per unit sold€41€129 price − €88 cost
Loss per unit left over€48cost minus €40 recovered at salvage
Break-even sell-through54%loss per leftover ÷ (margin + loss) — independent of how demand turns out
Chance of reaching it51%share of 4,000 simulated demand outcomes at or above 542 units
Demand assumed600 units expectedwith a ±45% spread: 10% of outcomes below 318, 10% above 962 units
Leftover value45% of costwhat a liquidator would pay; dead stock typically fetches 10–40%
Not includedcarrying cost, relationship damageholding excess stock costs a distributor roughly a quarter of its value per year, and a stock-out on a new item costs more than the missed margin — both make this estimate optimistic in opposite directions

What a minimum order quantity actually asks of you

A MOQ is usually presented as a price condition: order a thousand and the unit price drops. What it really is, is a transfer of risk. The supplier produces in an efficient batch and you carry whatever the market does not take. The relevant question is therefore not "is the unit price good?" but "what share of this batch do I have to sell before the order stops destroying value, and how likely am I to get there?".

That break-even share is fixed by the economics alone, not by the forecast. One unit sold earns the gross margin; one unit left over destroys the cost minus whatever you recover on it. Break-even sits where those cancel out — the loss per leftover unit divided by the sum of both. With a healthy margin and stock that still has salvage value, half the batch may be enough. With a thin margin on a product that is written off unsold, you can need eighty percent sell-through before the order makes sense, which is a very different conversation to have with your supplier.

Where these numbers come from

  • Sector patterns are built-in assumptions — a launch curve shape and an uncertainty range per sector, written by us and visible on the page. They are not pooled customer data, and nobody else's numbers are mixed into your result.
  • The economics are your inputs, applied openly — every calculation on this page is shown with its formula. There is no model that "knows better" than the numbers you entered.
  • Nothing is uploaded. The calculation runs in this browser tab. If you supply your own launch history, it is parsed here and stored on this device only; there is no server to send it to, and no account to create.
Don't have a demand estimate?

FirstBuy AI simulates one from launches like yours — your own past launches if you upload them, or typical sector patterns if you have none. Same engine, same economics, applied to a real first-buy decision.

Open FirstBuy AI →

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