Print-on-Demand · Spoilage module Find where spoilage starts, and stop the next one.
Every spoiled garment, traced to the blank, press, shift and client behind it. Then a warning on the jobs that look like the last problem.
A spoiled garment costs more than the blank.
Add the ink, the press time, the reprint and the knock-on delay to the next job, and one misprint costs several times what the blank did.
Most shops know their spoilage rate only as a monthly number. By then the press, the shift or the supplier lot behind it has moved on, and the same thing happens again.
- Which garments, presses and shifts spoil the most?
- What changed this month against each item’s own history?
- Which upcoming jobs look like a past problem, and what should we check first?
From the finding to the fix, on your own data.
- What it findsEvery spoiled unit and why: blank, print method, press, shift, client and defect type. Then what changed against each item’s own history, with events such as a new shift or a new supplier marked on the line.
- What it predictsThe jobs in the queue that match a past problem, with a risk score that sharpens as your history grows.
- What you doAssign the check to a plant manager, shift lead or operator. They close it with a note, and you see whether the rate came down.
What you get
- A spoilage dashboard and a weekly spoilage report
- “What changed”, with the likely cause marked
- Alerts, and a job queue with the check to do before each job
- Assigned tasks, and whether each fix worked
- Decision mapping: which decisions each role owns
Used by: Owner / GM, Plant manager, Press operator.
See it before we talk. The Spoilage module runs in our demo on sample data from a print shop.
Open the demo →Stakes Manufacturing: a print-on-demand apparel manufacturer, modernized without stopping production, with an AI layer that shows where spoilage happens. Read the case study →
- Read-only, on the system you already run. No migration, nothing new for your team to learn.
- Your data stays yours. In your environment, never shared, never used to train anyone else’s models.
- You pay only once it works. A flat annual subscription per module, with no one-time project fee.
What shops ask about spoilage.
What is a normal DTG spoilage rate?
Most shops treat 1 to 3% as normal. The useful question is where yours comes from, and whether it is rising for one press, shift, garment or supplier. The module answers that.
We don’t record a reason for every misprint. Can it still work?
Yes. It starts with what you record. We tell you which one or two fields would make the findings sharper, and what they are worth.
Does it change how operators work?
Only by adding a short check before the jobs that look risky. The operator sees it in the job queue and closes it with a note.
What data does it need?
Read-only access to: qC or reprint records: what was spoiled, when and why; the job, garment, press and shift for each record; the client on each order; the supplier or lot of the blanks, if you record it. Where a field is missing, we tell you what to capture and what it is worth, or extend your system to record it.
Let’s look at spoilage on your own data.
Bring us your spoilage problem. We’ll show you the first findings on your own records, read-only.
Prefer to look first? See it with sample data, no call needed →
- 1Pick the problemThe one costing you most.
- 2A demo on your dataWe build it on your own records, read-only.
- 3Pay once it worksThe subscription starts only then.