A short field guide for plant managers and ops directors. The math behind OEE, first-pass yield, on-time-in-full, and cost per good unit — the common ways each gets fudged in reporting, and a worksheet that auto-computes the honest version against what your plant reports today.
Free. No email gate, no nurture sequence, no follow-up. If after reading you'd like to talk it through with a manufacturing engineer, we can schedule a 30-minute call — but only if you ask.
Every manufacturer we've worked with reports a version of these four metrics. Many of them, on close inspection, have been rounding the corners on at least one — usually by inheritance: the way a report was set up six years ago, by someone who no longer works there, using assumptions nobody documented.
This guide doesn't introduce new metrics. It re-introduces four numbers you already use, with the specific errors and exclusions that often creep into them. Each section gives you the formula, the common ways the number gets fudged, an example of what that looks like in practice, and an interactive worksheet you can run with your own numbers — right here on this page.
If your reported numbers come out close to the strict numbers, your reporting is honest and you can stop reading. If there's a gap — and there usually is — the gap is the most useful number in this guide.
Pick one line, one product, or one customer — last shift, last week, or last month. Type your numbers into the white cells. The shaded cells auto-compute the strict version. Compare to what your reports show today.
Nothing is sent anywhere. The worksheets run entirely in your browser; close the tab and the numbers are gone.
Overall Equipment Effectiveness is the most-cited and most-fudged number in shop-floor reporting. The math is simple. The inputs are where the trouble lives.
OEE rolls three independent measurements into one number. Each input has its own definition, and each definition tends to get quietly stretched somewhere in your plant's history of reporting. Three places to look:
The strict definition uses scheduled run time as the denominator. A common shortcut also strips out planned changeovers, breaks, and meeting time — making the equipment look more available than it is. Typical inflation: 8–15 points of OEE.
Performance is supposed to compare actual rate to the equipment's ideal cycle. A common shortcut compares to the average rate the line happens to run — building historical underperformance directly into the target.
Strict quality counts only first-pass good units. The shortcut counts anything that eventually ships — folding rework cost invisibly into OEE and disconnecting the metric from process control.
Plant reports 82% OEE. On audit: availability denominator excludes an hour of daily changeover (trap 01), performance is benchmarked to a 60-second cycle when the press's nameplate ideal is 48 seconds (trap 02), and rework loops are counted as good (trap 03).
Strict OEE: 64%. The 18-point gap isn't a measurement error — it's a strategic decision somebody made at some point, and almost certainly never re-examined.
Pick one line, one shift. Fill in the strict-definition inputs in the white cells — the shaded cells compute themselves. Compare to what your reports show today.
First-pass yield captures the units that almost shipped before someone caught them. It's the single best leading indicator of whether your process is actually under control.
By the time a unit hits the scrap line, the cost is mostly paid: labor, material, energy, and the slot on the line. First-pass yield tells you what fraction of units cleared the first quality gate without intervention. It catches process drift before it becomes scrap — and it is also the metric most often quietly inflated.
If a unit fails inspection, gets reworked, and then ships, strict FPY says it's not first-pass. The shortcut counts anything that ultimately leaves the plant — burying rework cost.
"It was just a quick polish." Plants that don't define rework explicitly tend to let small fixes go uncounted — turning FPY into a measure of major rework only, which is much less useful.
If FPY is computed at final inspection, every catch upstream is invisible. A line with five quality gates and four interventions per part can show 100% FPY at the last gate.
Material that fails incoming inspection. Setup parts scrapped during changeover. Calibration runs. These are sometimes excluded from "units started" — understating real cost of input variation.
Plant reports 98.4% FPY at final inspection. On audit: rework loops at stations 3 and 5 aren't counted (fudge 01), polish-line repairs are excluded as "minor" (fudge 02), and FPY is measured only at the final gate (fudge 03).
Strict FPY computed at the first quality gate, counting every intervention: 86.1%. The 12-point gap is the size of the process-control problem you don't currently see.
Pick a single product or line. Choose your first quality gate — not the last. Count every intervention, including minor ones.
On-time-in-full is what your customer is actually scoring you on. The difference between "on-time" and "on-time-in-full" is where partial shipments quietly erode customer relationships you thought were healthy.
OTIF is the customer's metric. They score you on whether each order arrived complete and on the date promised. Many plants report a version that has been quietly relaxed in one of four ways — and the gap between your number and theirs explains a lot of difficult calls from key accounts.
An order for 1,000 units split into three shipments — 600 on time, 300 a day late, 100 a week late — can be reported as three separate "fulfillments," two of which are on-time. From the customer's side, the order was late and incomplete.
OTIF is what your customer experiences. If your contract is on delivery date but your metric is on ship date, you can ship "on time" and still be late on what matters. Carrier delays, customs, and weekend pickups are silent OTIF killers.
A 1,000-unit order ships 940 on time and 60 the following week. Strict OTIF counts that order as not on-time-in-full. The shortcut counts it as a 94% fill rate — which technically isn't wrong, but isn't OTIF either.
When the original promise was Tuesday and the order shipped Friday, some systems update the "promised date" to Friday so the order shows on-time. This destroys both the metric and the institutional memory of what was actually promised.
Plant reports 96% OTIF. Customer's vendor scorecard for the same period: 78%. The gap is split shipments (fudge 01), promised-date updates (fudge 04), and ship-date measurement on a contract that's on delivery date (fudge 02).
The 18-point gap is the size of the trust gap. Customers usually don't share scorecards unless something has already gone wrong — by the time you see the score, you may already be losing share.
Pick one major customer, last quarter. Strict OTIF treats each customer-order as one unit: in-full and on the originally promised delivery date.
Cost per good unit sorts the efficiency improvements that paid off from the ones that quietly didn't. When OEE goes up but CPGU also goes up, something's wrong with the OEE number — not with accounting.
Most plants track cost per unit. The strict version is cost per good unit — first-pass good only — and it gets compromised in two places: by counting too many units in the denominator, and by leaving too many costs out of the numerator. CPGU is also the only one of the four metrics that has to talk to finance, which is where the most common errors enter.
If reworked units count in the denominator, the cost of rework is invisible in the per-unit number. Cost per shipped unit can look flat while cost per good unit is climbing — and margin is leaking.
Rework hours sometimes get coded to a separate cost center — quality, maintenance, "indirect" — so they don't show up in standard cost. The hours were real and the wages were paid. They belong in the numerator.
Scrap value is sometimes netted against scrap revenue and reported as a small line item. The full material cost of scrapped units belongs in production cost — what you paid for material that didn't become a sellable unit.
Standard cost is what the system thinks each unit costs based on routings and rates set last year. Actual includes variance, overtime, expedited material, and the changeover that ran three hours long. Standard is the cost you wish you had.
Plant reports $4.18 cost per unit, flat year over year. On audit: rework labor coded to maintenance (fudge 02), scrap material netted against scrap proceeds (fudge 03), denominator is units shipped not first-pass good (fudge 01), and the whole calculation runs at standard cost (fudge 04).
Strict CPGU last year: $4.18. Strict CPGU this year: $4.61. Margin lost: ~10%.
Pick one product family, last month. Use actual costs, not standard. Include rework labor and full scrap material.
Each metric, in isolation, can be moved by gaming a single input. Read together, they catch each other. The point of running all four is the cross-check — the place where two numbers contradict each other is the most useful information in your reporting.
The four worksheets above can be done in a single afternoon with whatever reports you already pull. The point isn't to install a new dashboard — it's to do the strict calculation by hand, find the gap, and figure out where the gap came from.
What software is for — and what FactoryView is for, specifically — is making the strict calculation automatic, in real time, with the actual data flowing in from your equipment and your ERP. The afternoon-with-a-worksheet exercise is the diagnostic. Software is the cure, but only if you've already diagnosed which trap was hiding in your reporting.
Do the worksheets first. Find the gaps. Then we can talk about whether automating these numbers is worth doing in your plant — and where the highest-leverage place to start would be.
Run all four worksheets on one product or line, last month. Compare strict to reported. Take the biggest gap to your next staff meeting and ask one question: how did this gap get into our reporting?
The answer is almost never a person doing something wrong. It's almost always a system that was set up correctly four years ago for a business that has since changed. Finding that system, and updating it, is the work.
If after running the worksheets you'd like to talk through the gaps you found with a manufacturing engineer, we're happy to schedule a 30-minute call. We won't follow up otherwise — no nurture sequence, no sales rep "checking in."
FactoryView is shop-floor software for manufacturers. We sit between off-the-shelf MES and pure custom development. Pre-built where it should be — dashboards, scoreboards, downtime, PLC connectivity. Customized where it has to be.