How GarmentFlow tracks production readiness automatically, holds production until materials arrive, and where AI fits into the workflow.
GarmentFlow watches whether each order is on track to ship on time, so your team
doesn’t have to hold it all in their heads. This page explains the
—
what “ready” means, the checks it runs, and where AI fits into the product.
Watch the morning readiness read — what's on track, what's overdue, and where the money is — in one glance.
An
is ready for its next production step when every condition that step depends on has
been met. Before cutting can begin, for example, the
has to be approved, the
complete, the materials received, and the
issued. Traditionally a merchandiser tracks all of that on a spreadsheet or by
memory, and a missed condition only surfaces when the factory calls.The readiness engine tracks these conditions automatically. It reads the same records
your team already maintains — approvals, fitting sign-offs, material receipts, issued
documents — and tells you, per order, what is done and what is still outstanding.
Nothing is entered twice and there’s no separate checklist to keep current: readiness
is computed from the work itself. That’s why it’s a cross-cutting capability rather
than a single screen — it draws on the Styles, Orders, and Production modules at once.
The most concrete readiness check is the material-arrival gate. Garment factories
don’t start cutting until the fabric and trims are physically in hand, and the gate
enforces exactly that rule in the system.When the gate is on and a
’s
materials haven’t all been received, GarmentFlow holds the start of that order’s
production and shows the shortfall — which materials are short, and by how much. The
check reads the same material
that feed
,
so it reflects what has genuinely arrived, not what was ordered.It’s a guard with an escape valve. When you need to start anyway — a receipt not yet
keyed, or a small shortfall you’re willing to accept — you can override the hold by
recording a reason. The override is kept on the record. This matters for two reasons:
it stops production beginning on incomplete material by accident, and it leaves an
audit trail of every deliberate exception, so a decision to proceed early is always
traceable to a person and a reason. The gate is switched on by your administrator; see
the Production module guide for how it appears on the factory
order.
Readiness tells you what is incomplete; the backward schedule tells you whether it’s
late. GarmentFlow schedules every milestone backward from the customer’s ship
date, using your own lead-time offsets, so each step — order confirmed, materials
received, documents issued, inspection — carries a target date that lands the shipment
on time.Read together, the two give you an early-warning view. The readiness engine compares
actual progress against those targets and flags anything slipping: a condition that’s
merely outstanding is one thing; one that’s outstanding and past its target date is a
ship date in danger. See
backward scheduling on the
Orders page for how the targets are derived.
GarmentFlow uses AI to speed up document drafting — not to make decisions. From a
and its order data, AI can generate a first draft of the documents in your commercial
workflow — including
,
,
and
—
so you start from a worked draft instead of a blank page.The important word is draft. AI produces a starting point; you review it, edit it,
and approve it before anything is sent or saved. It never confirms an order, issues a
document, or commits a number on its own — those stay deliberate human actions. The
result is a faster document workflow with the same control you have today. That’s the
boundary GarmentFlow draws: AI accelerates the work, people make the calls.
