Order Otter
Order Otter vs. how you order today.
Spreadsheets and reorder-point formulas get the job done — until demand spikes, an MOQ trips up the order, or a stockout costs more than the business can absorb. Here's exactly where Order Otter pulls ahead, line by line.
Three ways to decide an order quantity
However you're ordering today, it's almost certainly one of these three — often more than one, depending on the SKU. Here's exactly how they stack up.
↔ Swipe to see the Order Otter column
| Spreadsheet / gut feel | Reorder-point app (min/max, EOQ) | 🦦 Order Otter | |
|---|---|---|---|
| Handles demand uncertainty | One guess for next month — no range, no "what if" | A single safety-stock buffer sized off average demand | Simulates hundreds of plausible futures, resampled from your real history |
| Sets the service target | Whatever feels safe this week | One fixed % for every item, regardless of its economics | Derived from that item's own margin, holding cost, and stockout cost |
| Real order constraints (MOQ, pack size, budget) | Adjusted by hand after the fact | Rounded up after the formula runs — the fix quietly undoes the math | Built into the candidate set from the start — every option shown is already orderable |
| Lumpy / intermittent demand | Averages smear the spikes away | Breaks down — a normal-curve buffer badly misjudges the real risk | Resamples your actual weekly history, spikes and quiet weeks included |
| Downside / worst-case awareness | Not tracked | Not tracked | Shown explicitly — expected outcome in your worst 10% of futures |
| Explaining the number | Whoever built it remembers why — until they leave | "Trust the formula" — no visibility into the tradeoff | A full tradeoff table: why this order beat the alternatives |
| Getting started | Free, but fragile and manual every single cycle | Quick to turn on, hard to trust once it's visibly wrong | Your numbers in, a tested order out — no system integration for the preview |
Every tool has its own quirks — this is the general shape. Using something different? Tell us and we'll get the comparison right.
What that actually looks like
One SKU, four candidate orders, the same simulated futures. Illustrative numbers — not a performance guarantee — from the full walkthrough in our white paper.
8-week horizon · sells for $20 · costs $8 landed · 95% service target · 750-unit supplier MOQ
↔ Swipe to see all six columns
| Candidate order | Expected profit | Service level | Stockout prob. | Avg. leftover | Downside (worst 10%) |
|---|---|---|---|---|---|
| 500 units Below MOQ (min 750) | $6,100 | 63% | 91% | 5 | $3,900 |
| 1,000 units | $8,400 | 84% | 52% | 70 | $5,200 |
| 1,500 units | $9,300 | 95% | 16% | 240 | $6,400 |
| 2,000 units | $8,000 | 99% | 3% | 610 | $4,700 |
A reorder-point formula sized to the ~1,000-unit forecast stocks out more than half the time. Order Otter's pick deliberately exceeds the forecast to buy resilience — and still wins on expected profit. The 500-unit candidate is greyed out because this vendor won't accept an order that small: the app scores it like any other, then rules it out rather than quietly rounding it up on you. Full walkthrough in the white paper.
When spreadsheets and formulas are the right call — and when they're not
Spreadsheets and reorder-point formulas aren't bad tools — they're fast and legible, and for plenty of SKUs they're genuinely enough. Knowing which camp an item falls into is the whole game.
Spreadsheets & gut feel
For a handful of slow-moving, stable SKUs you know cold, a spreadsheet is fast and nobody has to trust a black box. It stops paying off once you're covering more items than one person can hold in their head, or the person who built it moves on.
Reorder-point / EOQ formulas
Genuinely fine when demand is smooth, lead times are reliable, and nothing else constrains the order. Add a real MOQ, case pack, or shared vendor minimum, and the formula's answer isn't just imprecise — it's often not even orderable. That's not a spiky-demand edge case; it's most real purchasing.
Where simulation earns its keep
Demand that's lumpy or intermittent, stockouts and overages that cost very different amounts, and real constraints — MOQs, pack sizes, tight cash — that a formula rounds away instead of respecting. That's exactly the case Order Otter is built for.
See it in 90 seconds
Same wizard you'd actually use — numbers in, otter works, recommendation out.
Questions before you try it
Do I need a data science team to use this?
No. Fill in a form or drop in an Excel sheet — the same numbers you already track (on-hand, cost, price, demand history, lead time) — and get a recommendation back. No science team required.
What if my sales history is thin, messy, or intermittent?
The simulation resamples your actual weekly history — spikes and quiet weeks included — instead of assuming a smooth bell curve, so lumpy demand gets treated honestly. No history yet? Start from a mean and standard deviation estimate instead.
Is this a big IT or system-integration project?
Not for the preview — no integration required, just your numbers. A full custom build can connect to your systems later; see Pricing for what that looks like.
How long does it take to get a recommendation?
A few minutes for one SKU — enter your numbers, review them, and the otter runs the simulation right there.
Is my data safe?
The preview app runs the analysis in your browser — your numbers aren't sent to a server to get a recommendation. See our privacy policy for how the site itself handles data.
What does it cost to try it?
The live preview is free. See Pricing for scoped pilots and custom multi-SKU builds.
See what the otter recommends for your item
Drop in your numbers and get a tested order in a couple of minutes — no integration, no commitment, just a second opinion worth comparing against what you'd have ordered anyway.
Takes about 3 minutes. No login, no commitment.