Constraints and OEE
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Find the constraint in a process from each step's capacity and the demand, see what limits throughput, and calculate OEE for the constraint or any machine.
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Theory of constraints
Eliyahu Goldratt's theory of constraints (TOC) starts from the chain analogy: a chain is only as strong as its weakest link. Every system has at least one constraint that limits its output. An hour lost at the constraint is an hour lost for the whole system; an hour saved at a non-constraint is a mirage. Improving steps that are not the constraint produces local optima: better local numbers, more inventory and no more throughput.
Constraints can be physical (a machine, a skill, a space), market (demand is below capacity) or policy (batch sizes, rules, measures that drive the wrong behavior). Policy constraints are common and often the cheapest to remove.
The five focusing steps and TOC measures
- Identify the constraint, exploit it (get the most from it as it is), subordinate everything else to it, elevate it (add capacity), then repeat.
- Throughput (T): the rate the system generates money through sales. Inventory (I): money tied up in things intended for sale. Operating expense (OE): money spent turning inventory into throughput. The aim is to raise T while reducing I and OE.
- Drum-buffer-rope: the constraint sets the pace (drum), a time buffer protects it from upstream disruption (buffer), and material release is tied to its pace (rope).
Overall equipment effectiveness
OEE combines three losses: availability = run time / planned production time; performance = (ideal cycle time × total count) / run time; quality = good count / total count. OEE is their product. The six big losses map onto them: breakdowns and setups (availability), minor stops and reduced speed (performance), and defects and start-up losses (quality). An OEE of 85 percent is often cited as world class, a figure attributed to Seiichi Nakajima's work on total productive maintenance; it is a reference, not a standard.
On the CMQ/OE exam
Expect to find the bottleneck from a set of capacities, to state system output, to name the focusing step that applies, and to compute OEE from shift data. A common trap is improving the busiest-looking step rather than the one with the least capacity.