Suggestions Are Only as Real as Your Supplier Setup

A suggested quantity is a statement about your supplier

When the Smart Buying Table proposes a number for a SKU, that number is not an independent opinion about the product. It is an assertion about how a particular supplier behaves: how long they take, how often you go back to them, how much cover you want on the shelf while you wait. Every suggestion is computed from your sales velocity, lead times, and incoming stock, then rounded to your case sizes and capped at your maximum stock levels — and the lead time in that list is not a property of the product. It is a property of whoever ships it.

Which is why an empty supplier record is more dangerous than it looks. It does not produce no suggestion. It produces a confident one, computed against whatever is sitting in those fields — and if it surfaces as a finding, it arrives ranked by money at stake with a one-tap fix attached, looking exactly like every other finding. Nothing about it looks provisional.

The four fields the number actually moves on

Suppliers in Assisty aren't a contact list. Each one carries the lead times, order cycles, and minimums that decide when you reorder and how much. Set a supplier's Lead Time, Reorder Cycle, Min Stock Cover Days, and order minimums and maximums once, and the buying table's suggested quantities use them automatically.

Two of those four are self-explanatory. Two get skipped on the assumption they're optional. A reorder cycle is a review period — the interval between the moments you actually sit down with this supplier and place an order. It matters because the stock you buy today has to survive not just the lead time but the gap until the next order can go out. Min stock cover days is a buffer expressed in time rather than units: how many days of demand you want still standing when the replenishment lands. Stating it in days means it tracks the item's velocity instead of going stale as sales move.

Then the exception. Where one product differs from the rest of a supplier's range, you override it per variant: supplier SKU, cost, minimum order quantity, case quantity.

The supplier record's fields — and the per-variant exception

Why the supplier record is the field most likely to be stale

Vendor master data has a characteristic failure mode: entered once at onboarding, then owned by nobody. The record carrying contact details, terms, lead times and performance history sits still while the reality moves — suppliers change factories, change freight forwarders, change how fast they acknowledge an order.

A planning system will not correct you. Parameter accuracy is the planner's responsibility, not the engine's. Tell a system a supplier takes 14 days when it now takes 25 and you get arithmetic that is internally perfect and externally wrong — every day, quietly, for as long as the field says 14. (Illustrative numbers.) There is no error state for a parameter that is merely plausible.

Shopify's own vendor field doesn't close the gap. Each product can be assigned to one vendor, and if a new product isn't assigned one, the field defaults to your store name — and you can sort, filter and report your product list by vendor. It knows roughly who. It has no way to know how long.

Getting the setup in without a setup weekend

Your suppliers are probably already in your store data, and there are three ways in. Import them from a CSV with a guided wizard and template. Detect them straight from Shopify vendor fields, the fastest route if your vendor values are clean. Or detect them from product metafields, which is often where a store that outgrew the vendor field keeps the real supplier information. Both detection paths show a preview — you see the proposed suppliers before anything is created, rather than finding out afterwards what got made.

Then the linking. Auto-assign products by vendor name to attach a catalogue to its supplier in bulk, or save rule conditions so future products link themselves — so the assignment stays true as the catalogue grows instead of decaying from the day you finish.

Detection is an excellent starting point and a poor finishing point: the vendor field gets you the names, but it cannot get you the lead times, because Shopify never had anywhere to put them. Let detection do the tedious half — who your suppliers are, which products belong to whom — and spend your actual attention on the four parameters, which only you know.

Keeping it true after week one

Setup is a moment; accuracy is a habit. Without periodic refresh, planners end up working off lead times that no longer match supplier reality — which makes the cost of editing a parameter the thing that decides whether parameters ever get edited.

So the directory works like a directory. Search suppliers by name or email, filter by status, and sort by name, lead time, or product count — sorting by lead time surfaces the records that look wrong. Fix contact details across many suppliers at once in a spreadsheet-style bulk editor with per-cell undo, and export the whole list in one click. Or just say it: “Change Acme Supply's lead time to 7 days.” — “Done — Acme Supply's lead time is now 7 days. Tomorrow's reorder suggestions will use it.”

That reply is the whole loop. The parameter isn't documentation; it's an input. Change the rule and the next day's suggestions are computed from the new one — and when you turn a reviewed selection into orders, you get one purchase order per supplier, each pre-filled with that supplier's suggested quantities, tax and currency.