Critical Dimension · mmdeviation raised
A deviation found after the batch is finished is already scrap or rework. Munshi checks each reading against your range the moment it is entered, says what an out-of-range value means and what to do, and links the result back to the batch that produced it.
Your ERP logs the rejection: quantity, grade, disposition.
It won't tell you which parameter drifted, on which shift, on which batch.
Why the gap existsWhere the losses hide
What Munshi does
Every parameter carries an OK range and deviation thresholds. The instant a value is entered, Munshi assesses it - OK, Warning, Major Deviation - and shows the direction: too low, above limit.
When a reading is off, Munshi states what it means and the recommended action in plain language - so the floor is not waiting on the quality head to interpret it.
Every out-of-range reading becomes a logged deviation - parameter, value, threshold, severity, direction - with an alert and a Resolved state. Nothing quietly stays open.
Values derived from what was entered - and from production data - are calculated for you, the same way, every time. No two people doing the same maths differently.
Every QC result is linked to the production report, batch, machine and shift that produced it. A bad batch leads straight back to the conditions that made it - not to a guessing game.
First Pass Yield, rejected quantity and rework quantity tracked per batch - with rejection and rework separated, so the true cost of each is visible on its own.
Insights & Alerts
Live examples of the quality losses Munshi surfaces - teal is normal, maroon is flagged. Illustrative data.
Representative findings
Illustrative of what teams uncover once the data sits in one place.
A critical measurement drifted past its lower tolerance mid-batch. Munshi flagged it on entry - with the likely cause and the check to run - before it became a full batch of rework.
Flagged the moment it is recorded
With cause and action attached
Rework was quietly running higher than rejection, but the two were lumped together. Separating them showed the real cost was in correction, not scrap.
FPY, rejection and rework, separated
Per batch, machine and shift
First Pass Yield differed sharply between two shifts running the same product - a process difference nobody had isolated until the batches were compared.
Computed from QC data
Trended by shift and machine
Calculated automatically
You set the standards. Munshi monitors every batch against them.
Get started
Tell us one line or one machine where you suspect losses, and we will show you exactly what Munshi would surface there - mapped to your operation, not a generic demo.