When a line is the constraint, every hour on it is sold once. The question is which product gets it.
Most plants answer by due date, which the sequencing article covers. Some answer by margin per unit, which feels like the financially literate choice and uses the wrong denominator. A high-margin SKU that runs slowly and changes over badly can earn the plant less per hour than a cheaper SKU that runs fast and clean. Margin per unit compares products on a unit the constrained plant is not short of. Margin per line-hour compares them on the only thing it is short of: time on the bottleneck.
This article defines the metric, shows why the per-unit ranking misleads, says where the metric applies and where it does not, and describes the three decisions it should drive.
Contribution margin per line-hour is what an hour of a line’s time earns when it runs a given SKU, after the changeovers that SKU’s runs cost the schedule.
Contribution margin per line-hour = (contribution margin per unit × demonstrated rate per hour) × (run hours ÷ (run hours + changeover hours attributable to the run))
Three inputs, from three places.
Contribution margin per unit comes from finance: selling price minus variable cost, the number the SKU-level value article insists on in place of gross margin.
Demonstrated rate comes from the floor: the units per hour the line actually sustains on that SKU, measured, per line, as the demonstrated-rate article defines it. Not the standard. A metric built on the standard rate is a belief about margin per hour.
Changeover hours attributable to the run come from the planner: the time the sequence spends getting into and out of that SKU, from the measured matrix. An allergen format that costs 90 minutes to leave earns less per scheduled hour than its run rate suggests.
The metric is where the finance, floor and planning articles in this series meet, and none of the three functions can set it alone. Finance does not know the rate, the floor does not know the margin, and the planner has neither unless a system puts them together.
The series line, six SKUs, with the margins from the value article and the demonstrated rates from the planning article.
| SKU | Margin/unit | Demonstrated rate (u/min) | Margin per running hour | Changeover penalty | Margin per line-hour | Rank by unit | Rank by hour |
|---|---|---|---|---|---|---|---|
| D | $0.31 | 142 | $2,640 | 5% | $2,510 | 1 | 1 |
| F | $0.24 | 136 | $1,960 | 12% | $1,720 | 2 | 2 |
| A | $0.20 | 138 | $1,660 | 5% | $1,570 | 3 | 3 |
| B | $0.16 | 132 | $1,270 | 5% | $1,200 | 4 | 4 |
| C | $0.14 | 126 | $1,060 | 6% | $990 | 5 | 5 |
| E | $0.12 | 106 | $760 | 11% | $680 | 6 | 6 |
On this line the two rankings agree, which is worth saying plainly: the metric does not always flip the order. What it changes is the distances. D and F are close on margin per unit, 31 cents against 24; per hour, D earns nearly 50% more, because it runs faster and leaves cheaply. E at 12 cents looks like 60% of A at 20; per hour it is less than half, because it runs a quarter slower and carries the allergen exit. The range across the line is nearly four to one, $680 to $2,510 an hour. A planner choosing between two orders for the last constrained hour of the week is choosing between those numbers, and the due-date list does not show them.
Where the metric does flip the ranking is across lines. SKU E runs at 106 on this line and 130 on line 4, where the labeler has no guide problem. Its margin per hour on line 4 is $940 before changeovers, against $760 here. The metric is per SKU per line, and that is the number routing should use: the same product is worth different amounts an hour depending on where it runs.
The idea is not new. Throughput accounting, in Goldratt’s formulation, ranks products by throughput per unit of constraint time and has done so since the 1980s. What is different now is the inputs. Goldratt’s planners estimated the rate and the setup time; a plant with station-level data measures both, per SKU, per line, and refreshes them monthly as the loss work changes them. The metric was always right. It is only recently that the numbers under it have been real.
Margin per line-hour matters on the constraint and nowhere else.
On a line with spare hours, it is irrelevant. If the line can make everything that is demanded in the hours available, every order is worth its full contribution and the question of which one gets the hour does not arise. Make everything. Using the metric to idle a low-margin SKU on an unconstrained line throws away margin to improve a ratio.
On a constrained line, it should rank the queue. When the orders need 134 hours and the line has 120, something will not be made this week, and margin per hour says what it should be, other things being equal.
Service constraints override it. Other things are not equal. A contractual order, a promotional window, a customer the plant cannot afford to short: these run when they are due regardless of what they earn an hour. The decision rule is therefore in three steps: meet committed service; fill the remaining constraint hours in margin-per-hour order; and when the two collide, make the trade-off explicit, with the margin given up and the service protected both stated.
In a demand-constrained week it changes meaning. When demand is below capacity, margin per hour does not tell the plant which product to push; it tells it which line to idle, and which SKUs to run on the remaining lines. The metric is still useful; the decision it informs is different.
A plant that applies margin per hour everywhere will cut good business. A plant that applies it on the constraint within service limits will find it is the most useful number the schedule has.
Sequencing and routing. When two orders compete for the constrained hour, the higher margin per hour runs first unless service says otherwise. When a SKU can run on two lines, it runs where its margin per hour is higher, which usually means where its demonstrated rate is higher. The sequencing article chose the order to minimize changeover time; this metric chooses it to maximize what the remaining hours earn, and the two are combined in the same schedule.
Mix in S&OP. The commercial team usually sees margin per unit and volume. Shown margin per constraint-hour, it sees which products consume the plant’s scarce time cheaply and which consume it expensively. That is the beginning of a pricing conversation about the slow, low-margin formats, and the end of the assumption that volume on any SKU is good news when the line is full.
The CI backlog. A fix that raises a high-margin SKU’s demonstrated rate adds more margin per hour than the same fix on a low-margin SKU. The value article made this point for losses; here it applies to improvements. On the series line, a 10% rate gain on D is worth about $260 an hour; the same on E is worth about $75.
Every line has one or two formats that consume constraint hours at a fraction of the line’s average margin per hour. On the series line it is E, at $680 against a line average around $1,450. The data makes them visible, and visibility forces a decision that has usually been avoided.
The options are finite. Raise the price, which the commercial team will resist until it sees the number. Move the SKU to a less constrained line or a line where it runs faster, which is routing. Fix the rate, which is the CI backlog. Batch it to cut the changeovers it carries, which is sequencing. Or stop making it, which is a commercial decision the plant has never been able to argue with data.
This is the paragraph for the COO. The metric turns a floor’s vague sense that “format E is a pain” into a figure per hour that the commercial team has to answer, and the answer is usually one of the five options rather than the status quo.
The series line in a constrained week: 120 hours available, orders needing 134, committed service on three orders. Illustrative figures.
Due-date schedule. Orders placed in date order, the overflow pushed to Saturday overtime. Weekly contribution from the 120 scheduled hours: about $178,000. The orders that spilled to Saturday were whichever landed last: the second E run and part of the F run, the line’s second-best earner per hour.
Margin-per-hour schedule within the same service constraints. The three committed orders fixed in place. The remaining constraint hours filled in margin-per-hour order, with the sequencing rules applied so the changeover penalties in the table are real rather than assumed. One of SKU E’s two runs rerouted to line 4, where it earns $940 an hour before changeovers instead of $760, and frees nearly ten hours here. Weekly contribution from the same scheduled hours, line 3’s 120 plus spare hours line 4 already had: about $188,000, up 6%, with two orders moved a day and nothing committed moved at all. The overflow that remains is part of a C run, the lowest earner per hour among the orders free to move, and it is now a candidate for next week rather than for overtime.
Six percent more weekly contribution from the same hours, from the same orders, by choosing which hour runs what. The figures are illustrative; the shape, mid-single-digit gains from re-ranking within service limits and a larger gain from the routing of one slow SKU, is typical of the first week a plant schedules this way.
The metric needs three numbers from three functions to be current at the same time. In most plants they live in a costing file, an OEE report and a planner’s head. In Fabrico’s manufacturing performance platform (MES, OEE, CMMS & AI) they are computed together.
Computed continuously, per SKU per line. The financial impact module takes the contribution margin entered once per SKU, the demonstrated rate from the OEE module per line, and the changeover penalty from the measured matrix, and holds margin per line-hour for every SKU on every line it can run, refreshed as the rates and the matrix change.
Shown against service and inventory on every candidate schedule. The scheduler displays each plan’s weekly contribution next to its service position and its inventory days, so the trade-off is a choice between three numbers rather than a guess. Within the plant’s service rules, it can rank the constraint queue by margin per hour and route each SKU to the line where it earns most.
The S&OP view lists SKUs by constraint-hour efficiency. The commercial team sees margin per unit, volume and margin per constraint-hour side by side, which is the view that starts the pricing and mix conversation.
The uncomfortable SKUs are flagged with their options. The AI actionable insights identify the formats earning well below the line’s average per hour and quantify each option, reroute, fix the rate, batch, reprice, with the value attached, so the decision arrives as a comparison rather than a complaint.
Planned reconciles to actual. The weekly contribution the schedule promised and the contribution the line delivered are reported together, from the same data, so the metric is accountable rather than theoretical.
One caveat belongs in this section as much as in the method. The platform never overrides a committed order for margin. Service rules are inputs; the metric fills what they leave, and where they conflict the trade-off is shown to a person who decides.
What is contribution margin per machine hour? The contribution margin a line earns in one hour running a given product: margin per unit multiplied by the demonstrated hourly rate, reduced for the changeover time the product’s runs cost the schedule. It is the right basis for deciding which product gets a constrained hour, because time on the constraint is what the plant is short of.
What is throughput accounting? A management accounting approach, associated with Goldratt’s theory of constraints, that evaluates products by throughput (sales minus truly variable cost) per unit of time on the constraining resource, rather than by margin per unit or by allocated cost. Contribution margin per line-hour is its practical application to a packing line, with measured rather than estimated rates.
Should you prioritize high-margin products on a bottleneck? Prioritize high margin per bottleneck hour, which is not the same thing. A high-margin product that runs slowly or changes over expensively may earn less per constrained hour than a cheaper product that runs fast. And only after committed service is met; the metric fills the remaining hours, it does not override customer commitments.
How do you choose product mix when capacity constrained? Rank products by contribution margin per hour on the constrained line, meet committed service first, fill the remaining constraint hours in that order, and route each product to the line where it earns most per hour. Show the commercial team the ranking so pricing and mix decisions are made with the constraint’s economics visible.
Does margin per hour conflict with customer service? It can, and the conflict should be explicit rather than hidden. The decision rule is service first, margin per hour second, with the margin given up to protect service stated as a number. A plant that sees the number usually finds the right balance; a plant that cannot see it is making the trade-off blind in favor of whichever order arrived first.
If one line is the constraint and the schedule is a due-date list, the plant is deciding what its scarcest hours earn without knowing the answer. Four weeks of measured rates and changeovers, next to the margins the controller already has, will give the number per SKU per line, and the first week scheduled by it usually shows mid-single-digit gains in weekly contribution on the same hours.
The fixed-scope pilot does that on one line: six weeks, demonstrated rates per SKU and the measured changeover matrix captured with an industrial sensor and hub installed with your team, the plant’s own contribution margins entered once per SKU, and a readout with margin per line-hour for every format on the line, the uncomfortable SKUs and their options, and one constrained week re-ranked within service limits as a before-and-after. The fee is fixed and credited in full against a first-year subscription if you roll out. The pilot runs on Fabrico’s manufacturing performance platform (MES, OEE, CMMS & AI), which connects machine data, OEE and loss analysis, production scheduling, SKU-level output value and maintenance in one system, so the constrained hours go to the work that earns the most from them.
Request a demo or read how the scheduling module ranks the constraint queue within service rules.
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