Simulation for Electronics & Automotive
High Volume, Tight Coupling, No Room to Absorb a Stop
Automotive components and electronics assembly are the high-volume discrete case: fast cycles, many stations in series, and takt times that leave almost nothing spare. When a line runs to a takt and inventory between stations is deliberately minimal, a stoppage anywhere propagates immediately in both directions — which is exactly the behaviour that averages cannot describe.
Who has used it
Delphi Automotive Systems · Ericsson Wireless Communications · Lucent · Bosch · JTEKT · Hewlett-Packard · BICC General Cable
Lean removed the inventory, not the variability
Pulling inventory out of a line is good practice and it does not make the line more reliable — it removes the thing that was hiding the unreliability. The variability is still there; what has changed is that it now propagates instead of being absorbed.
That is the trade every lean programme makes, usually without quantifying it. The question is not whether to hold buffer but which form to hold it in: inventory, spare capacity, or time. How big should a buffer be? works through why the buffer never actually disappears.
What tends to be binding
Series length punishes good machines
85% OEE is a single-machine benchmark. Five machines at 85% in series with no buffering give a line near 44%, and an assembly line is far longer than five stations. Nothing has gone wrong and the line is at less than half. The arithmetic is here.
Micro-stops, not breakdowns
On a tightly coupled line a stoppage measured in seconds never lets the small buffers refill, so its cost is carried continuously rather than occasionally. It looks negligible on a loss report and is frequently the largest recoverable item on the line.
Mix and sequence
Model mix changes station-level work content, so the constraint moves with the build sequence. A line balanced for one mix is unbalanced for another, and the balance is only true on average — which is not a state the line is ever actually in.
Supplier and inbound variability
A station starved by a late delivery is indistinguishable, on a machine report, from a station that broke. The cause is upstream of the plant entirely, and only a model that represents both can attribute it correctly.
All of these produce the same misreading: the loss report ranks by minutes lost, the line funds the top of that list, and the throughput does not arrive. What you want ranked is recoverable throughput — and on a tightly coupled line the two rankings diverge more, not less.
The same mechanisms, documented
$1.5M in hidden throughput
Two failure modes with near-identical downtime on the loss report. Fixing the bigger one returned less than it cost; the smaller one returned 62% more, because it was cascading into the stations downstream.
Read the case study → PropagationStarved vs blocked
Both look like a stopped machine and they have opposite causes. On a takted line the machine you are staring at is usually not the one with the problem.
Read the guide →Where to start
The browser sandbox runs a five-machine line with no download and no sign-up — the same series-of-stations problem, with the state of every machine exposed while it runs.
Related: Packaging & CPG · Aerospace & defence · All case studies