Micro-Stops vs Breakdowns
Why the Short Ones Cost More

A breakdown gets a work order, a root-cause meeting and a line on the downtime Pareto. A one-minute jam gets cleared by the operator and forgotten. Put the two side by side with the same recorded loss, and the frequent short stops can take more throughput off the line than the rare long one. Here’s why, and how to tell which you have.

Micro-stops, minor stops and speed loss

In the Six Big Losses, equipment failure is an Availability loss and minor stops and reduced speed are Performance losses. The split matters more than it looks. A breakdown is logged as a stop with a start, an end and usually a cause. A micro-stop, a jam cleared in under a minute or a sensor fault reset at the panel, often falls below whatever threshold the plant’s system uses to record a stop. It shows up only as a machine that made fewer units than its run time allowed.

So micro-stops hide in performance loss. They get recorded as speed loss, which reads like a machine running a little slow, not one stopping a hundred times a shift. Nobody writes a work order for speed loss, which is the first reason they get ranked too low.

The second reason is structural, and it applies even when every stop is logged perfectly.

The ranking a loss report gives you

In our bottling line case study, a five-machine line (Filler, Capper, Labeler, Case Packer, Palletizer) has two failure modes with nearly equal direct losses. Labeler Misalignment is infrequent, longer stops at 6.79% direct efficiency loss. Filler Micro Stop is frequent, short stops at 6.72%. On a Pareto they rank level, and most improvement teams would pick the Labeler, because it’s the bigger single event and easier to see.

What removing each one gives back

A loss report can tell you what each failure mode cost. It can’t tell you what comes back when you remove it. For that you remove one failure mode in a validated model, re-run the whole line, and measure the recovered output. ReliaSim®’s methodology page shows the pair:

The losses are almost identical and the recoveries aren’t. Removing the Labeler’s long stops returns less than they appeared to cost, because the line was already absorbing part of them. Removing the Filler’s micro-stops returns more than they appeared to cost. The case study describes 120 one-minute micro stops whose effects landed as idle time on the Labeler, Case Packer and Palletizer, never under the Filler’s own name.

Why frequent short stops can cost more

A buffer absorbs a stop only if it has stock when the stop begins. That condition explains most of the gap, and it’s exactly what frequency attacks.

1. They never let the buffer refill

A buffer refills only as fast as the machine feeding it can outrun the machine it feeds. On a high-speed line where every machine is rated near the same speed, that margin is small, so refilling is slow. Short stops that arrive faster than the buffer refills drain it step by step.

Illustrative arithmetic, not plant data

An upstream machine can run at 66 units per minute. The machine after the buffer draws 60, so the buffer refills at 6 a minute while both run. A one-minute upstream stop drains 60 units, which takes 10 minutes to put back. If a one-minute stop comes every five minutes, the buffer regains 24 units between stops and loses 60 in each. It falls by 36 units per cycle, so a 240-unit buffer is nearly empty after half an hour. From then on it holds about 24 seconds of stock when a stop begins.

2. They take the buffer away from every other stop

Gain can exceed loss for this reason. Once short stops have drained a buffer, it can’t protect against anything else either, such as a stop on the machine feeding it, a longer jam or a changeover overrun. Those losses pass straight through and get charged to other failure modes. Remove the micro-stops, and the buffer is full again for all of them. That recovered protection is the part of the gain the loss report never showed.

3. They cascade on close-coupled sections

Where there’s little or no storage between machines, every micro-stop starves the machine after it and blocks the one before. The cost lands as idle time on neighbors that did nothing wrong (Starved vs Blocked). The same pattern shows up on packaging and electronics and automotive lines.

Why long stops still matter

None of this means micro-stops always outrank breakdowns. A long stop outlasts any practical buffer. On the Buffer-Options demo line, removing the dominant long stoppage on the constraint beats adding storage at every buffer size (How big should a buffer be?).

What decides it on a given line is how stop lengths compare with buffer size, how quickly buffers refill, and where each machine sits relative to the constraint. So a line model takes stop distributions per failure mode rather than averages (MTBF and MTTR are outputs, not inputs).

Speed loss isn’t the same as a stop

Reduced speed also lands in Performance, and it has its own interaction with buffers. A machine running slower than the one before it fills the buffer between them and then blocks the faster machine. A machine running slower than the one after it drains the buffer and then sets the pace of everything downstream. Either way, a slow machine defeats the buffer beside it, just as frequent stops do.

Where the slow machine sits decides what it costs. Speed loss on the constraint costs output directly. Speed loss on a machine with plenty of spare rate may cost nothing at all. The OEE report charges both the same Performance points.

How to rank short and long stops on your line

1. Log short stops as stops. Record them by cause, not as a lower rate. A micro-stop you can’t see as an event can’t be modeled or ranked.
2. Fit distributions per cause. Use time-to-failure and time-to-repair for each failure mode, not a pooled average per machine. Downtime data analysis in ReliaStats does this from line event data.
3. Validate before you rank. Check the model against the line’s own history first (salad dressing plant case study).
4. Rank by gain, not loss. Remove each failure mode in turn and read what comes back. Which loss to fix first covers the method.

Following the Pareto points the budget at alignment tooling for the Labeler. Following the model points it at the Filler’s jams, which recover 62% more throughput.

Frequently asked questions

Try it on a real bottling line: the Sandbox question What should we fix first?, or one move in Beat the Bottleneck.