The four numbers every plant should know cold.

Walk into any mid-market manufacturing plant in North America and you will find a report that includes some version of these four numbers. Walk into it on a Wednesday morning and ask the plant manager whether the report is honest, and a small but specific kind of pause will happen.

The pause is informative. The four numbers below — OEE, first-pass yield, on-time-in-full, and cost per good unit — are the workhorse metrics of every operations team in the country. The math behind each one is short and well understood. The inputs are where the dishonesty enters. Sometimes by accident. Usually by inheritance — a report set up six years ago, by someone who no longer works there, using assumptions nobody documented.

This essay walks through all four, the most common errors in each, and a single example of what the gap between strict-reported looks like in practice. The 17-page field guide goes deeper and adds a fill-in worksheet per metric.

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Metric 01

OEE — and the three lies inside it.

Overall Equipment Effectiveness is the most-cited and most-fudged number in shop-floor reporting. The math is honest. Three independent measurements rolled into one. Each input has its own definition, and each definition has been quietly stretched somewhere in your plant's history.

Lie 01 · The availability fudge.

The strict definition treats scheduled run time as the denominator. The common shortcut is to also strip out planned changeovers, scheduled breaks, and meeting time — making the equipment look more available than it actually is. Typical inflation: eight to fifteen points of OEE.

Lie 02 · The performance fudge.

Performance is supposed to compare your actual rate to the equipment's ideal cycle. The shortcut is to compare to the average rate the line happens to run — which builds historical underperformance directly into the target, making every period look "on plan." If the press's nameplate cycle is 48 seconds and you benchmark against a 60-second average, you've handed yourself a free 25% of "performance" before the shift starts.

Lie 03 · The quality fudge.

Strict quality counts only first-pass good units. The shortcut is to count anything that eventually ships — which folds the cost of rework invisibly into the OEE number, and disconnects the metric from the process actually getting better. A line with three rework loops can report 99% quality and still produce expensive units.

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Metric 02

First-pass yield — the leading indicator.

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 is the metric that catches process drift before it becomes scrap, and it is also the metric most often quietly inflated.

Fudge 01 · Counting reworked units as first-pass.

If a unit fails inspection, gets reworked, and then ships, the strict definition says it's not first-pass. The shortcut is to count anything that ultimately leaves the plant — which buries rework cost and disconnects the metric from process control.

Fudge 02 · Excluding "minor" rework from the count.

"It was just a quick polish." "It was a label fix, not real rework." Plants that don't define rework explicitly tend to let small fixes go uncounted — turning FPY into a measure of major rework events only, which is much less useful.

Fudge 03 · Measuring only at the last station.

If FPY is computed at final inspection, every catch upstream of final inspection is invisible. A line with five quality gates and four interventions per part can report 100% FPY at the last gate — and you'll never see the process drift until something escapes the final check.

Fudge 04 · Excluding units that never reach the line.

Material that fails incoming inspection. Setup parts that get scrapped during changeover. Calibration runs. These are sometimes excluded from "units started" — which understates the real cost of variation in the input.

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Metric 03

On-time-in-full — your customer's view.

OTIF is the customer's metric. They score you on whether each order arrived complete and on the date promised. Most plants report a version of OTIF that has been quietly relaxed in one of four ways, and the gap between your number and your customer's is the thing that explains the difficult call you got from a key account last quarter.

Fudge 01 · Splitting orders into multiple shipments.

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 arrived late and incomplete, every time.

Fudge 02 · Using ship date instead of delivery date.

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 by everything that matters. Carrier delays, customs, and weekend pickups are silent OTIF killers.

Fudge 03 · Counting partial shipments as full.

A 1,000-unit order ships 940 on time and 60 the following week. Strict OTIF counts the 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 — and is what most internal reports show.

Fudge 04 · Adjusting promised dates after the fact.

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 is the most damaging fudge: it destroys both the metric and the institutional memory of what was actually promised.

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Metric 04

Cost per good unit — the cross-check.

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.

Cost per good unit is also the only one of the four metrics that has to talk to finance, which is where the most common errors enter. When OEE goes up but CPGU also goes up, something is wrong with the OEE number — not with your accounting.

Fudge 01 · Cost per unit shipped, not cost per unit good.

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 your margin is leaking.

Fudge 02 · Excluding rework labor from "cost of production."

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.

Fudge 03 · Excluding scrap material from "cost of production."

Scrap value is sometimes netted against scrap revenue and reported as a small line item. The full material cost of scrapped units belongs in your production cost — what you paid for the material that didn't become a sellable unit.

Fudge 04 · Using standard cost instead of actual.

Standard cost is what the system thinks each unit costs based on routings and rates set last year. Actual cost includes variance, overtime, expedited material, and the changeover that took three hours longer than the standard. Cost per good unit measured at standard is the cost you wish you had, not the cost you actually have.

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Together

Read together — the four numbers as a system.

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.

You don't need new software to start this.

The four worksheets in the full field guide can be done in a single afternoon with whatever reports you already pull. The point is not 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. The software is the cure, but only if you've already diagnosed which lie 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.