OTIF, FPY, and WIP: The Manufacturing Metrics That Actually Matter

And like OEE, all three are only as trustworthy as the connected data underneath them.

OTIF — On Time In Full

What it is: the percentage of orders delivered both on time and in full (complete). Both conditions have to be met — a shipment that's on time but short, or complete but late, doesn't count.

How to read it: OTIF = orders delivered on time and complete ÷ total orders. It's deliberately strict, because that's how the customer experiences you.

Why it matters: OTIF is the metric your customers actually feel — it ties everything upstream (production, scheduling, quality) to delivery reliability. Miss it consistently and you lose trust, contracts, and sometimes penalty-clause money, no matter how good your internal numbers look.

The catch: you can't measure OTIF honestly from one system. It spans the ERP (what was ordered and promised), production (what was made), and logistics (what shipped, when). If those don't reconcile, OTIF is a guess.

FPY — First Pass Yield

What it is: the percentage of units that pass through a process correctly the first time — no scrap, no rework.

How to read it: FPY = good units (first pass) ÷ total units started. For a multi-step process, the honest figure is rolled throughput yield — multiply the yield of each step — which is usually lower than any single step suggests, and far more revealing.

Why it matters: FPY is the clearest signal of process quality. Rework and scrap are pure cost — material, labor, and capacity spent twice — and a low FPY quietly drags down everything else, including OEE's quality component. It's also the early warning that a process is drifting.

The catch: FPY depends on connected, clean QMS data. When quality lives in disconnected spreadsheets, defects get logged late or inconsistently, and the number you see is rosier than reality.

WIP — Work In Progress

What it is: the inventory that's been started but not yet finished — sitting between process steps on the floor.

How to read it: WIP isn't a percentage; it's a level you manage. It relates to throughput and cycle time — broadly, the more WIP you carry at a given throughput, the longer things take to move through. Watch the trend and where it piles up.

Why it matters: WIP is cash and flow. Too much ties up working capital, lengthens lead times, and hides bottlenecks behind buffers. Too little starves the line and risks stoppages. The right level keeps cash free and bottlenecks visible.

The catch: accurate WIP needs connected MES and ERP data. Without it, WIP is estimated — and estimates are exactly where cash quietly hides.

How they fit together (and with OEE)

No single metric runs a plant. Read as a set, they cover the four things that matter:

• OEE — equipment productivity
• FPY — quality
• OTIF — delivery
• WIP — working capital and flow

The danger is optimizing one in isolation. Push OEE by running equipment flat out, and you can balloon WIP and still miss OTIF — busy machines making the wrong things, early. A balanced view keeps you from winning one number while losing the business. That balance is only possible when all four come from the same trusted source.

The common thread: trust comes from connected data

Every one of these metrics fails the same way OEE does — quietly, when it's measured by hand from disconnected systems. Numbers assembled in spreadsheets are slow, inconsistent, and usually flattering, and they spark the meetings where everyone argues about whose figure is right. Poor data quality alone costs organizations an average of $12.9 million a year (Gartner), and most of the data that would make these metrics accurate sits unused — IDC has estimated over 80% of manufacturing data is "dark" (IDC, 2022).

The fix is the same too: measure them automatically, in real time, on one connected data foundation. That's the job of analytics, built on solid data engineering. (The same lesson, in depth, for the headline metric: How to calculate and improve OEE.)