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Every system has one constraint that limits how much it can produce. Improving anything that is not the constraint rarely changes the result. Find the weakest link first, then decide what to do about it.
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Any process with multiple steps has exactly one step that caps total output, no matter how good the other steps become. Find that step. Protect it, feed it first, and invest in it before you invest anywhere else.
Important: the whole line can only ship 18 units a week, no matter how fast Design, QA or Deploy become. Dev is the constraint.
The model is most useful whenever work flows through a sequence of dependent steps, and effort keeps going into steps that were never the real limit.
These are Eliyahu Goldratt’s Five Focusing Steps. Skipping straight to “elevate” before the earlier steps usually wastes money.
Find the single step, resource, policy or rule that limits total throughput. It is rarely the step people assume.
Get the most out of the constraint as it exists today, before spending anything. Stop it from ever sitting idle.
Set the pace of every other step to match the constraint. Building ahead of it only creates backlog, not output.
If exploiting still isn’t enough, invest resources, headcount or budget specifically at the constraint.
Open a use case to see what the model changes.
Map every station’s throughput for a fixed period. The station with the lowest number is the constraint, regardless of how busy the other stations look.
Adding more developers will not help. QA’s throughput is the ceiling. Either reduce what reaches QA, speed up QA itself, or add QA capacity before adding anything upstream.
Find the task on the critical path with no slack. Protect it with buffers and give it first claim on attention, approvals and resources.
Look for the single recurring step, such as a slow morning routine or a decision you re-litigate daily, that caps everything scheduled after it.
More marketing spend just grows the backlog. The constraint is follow-up capacity. Fix that before generating more leads.
Count available beds, imaging slots or discharge steps, not staff hours. Flow is usually limited by one scarce physical resource, not general effort.
This is an illustrative example designed to teach the method.
Expand for the origin of the theory and how far it has traveled since.
Eliyahu M. Goldratt, an Israeli physicist turned management consultant, introduced it in his 1984 business novel The Goal. The book dramatized a struggling factory manager discovering that balancing every station’s efficiency was the wrong goal; finding and managing the one true bottleneck was the right one.
A physical constraint is a real capacity limit, such as one machine, one skilled role or one piece of equipment. A policy constraint is a rule, habit or approval step that limits output even though no physical resource is actually maxed out, such as a batch-size rule or a sign-off that only one manager can give. Policy constraints are often cheaper to fix, but easier to miss because nothing looks “busy.”
Drum-Buffer-Rope is Goldratt’s scheduling method built directly on this theory: the constraint sets the “drum beat,” a buffer protects it from running dry, and a “rope” paces upstream release to match. Critical Chain Project Management applies the same logic to project schedules, placing buffers around the chain of tasks with no slack rather than padding every task individually.
Trying to make every station equally fast feels fair, but it does not raise total output. Only the constraint’s capacity sets the ceiling.
Speeding up a station that already outpaces the constraint just builds a bigger pile of unfinished work in front of the real bottleneck.
Teams look for machines or people and overlook the approval rule, batch size or meeting cadence that is the actual limit.
Elevating a constraint moves the limit somewhere else. Treating step 05 as optional leads to quietly re-bottlenecking the system.
Rewarding every station for looking busy encourages overproduction everywhere except where it matters.
A constraint that is busy but working on low-value items is still underperforming. What it works on matters as much as how occupied it stays.
capacity is genuinely even across every step, so there is no single station dragging down the rest.
the work is early-stage research with no repeatable process yet; “throughput” is not a meaningful unit until a process exists.
multiple feedback loops interact and the bottleneck shifts faster than a single team can act on it.
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At any given moment, yes, in the sense that one step sets the ceiling. But constraints move. After you elevate today’s constraint, measure again. A different step, or a policy rather than a resource, usually becomes the new limit.
Watch for queues instead. Work tends to pile up directly in front of the constraint and sit idle just after it. A visible backlog is often a faster signal than a precise throughput count.
They are related but distinct. Critical path identifies the longest dependency chain in a one-time project. The Theory of Constraints is built for repeating processes and asks which single resource caps ongoing throughput, then manages buffers around it.