Production Capacity Planning Simulation
Will the line make the volume, and what’s the cheapest way to get there?

Rated capacity isn’t effective capacity

Rated capacity is what a machine can do while it’s running, fed and unblocked. Effective capacity is what the line ships over a week, a season or a year. Between the two sit losses that a capacity spreadsheet usually folds into one planning factor.

• Interrupts. A hundred one-minute stops and one hundred-minute stop cost the same minutes, but very different throughput once there’s storage between machines.
• Blocking and starving. A stopped machine idles its neighbors, and how far the stop travels depends on the buffers between them. See starved vs blocked.
• Changeovers and mix. Size, flavor, label and allergen changes are part of the schedule. A schedule that’s efficient for one product mix can be badly wrong for another.
• Rate against reliability. Running faster makes more product until failures that rise with rate eat the gain, so output has an optimum speed.
• Seasonality. A plant sized for average demand is idle part of the year and constrained the rest. The useful question is capacity during the peak, given how the line actually behaves.

Those losses don’t add up neatly. Five 85% machines in series can make a 44% line (is 85% OEE achievable?), and arithmetic on averages makes spreadsheet capacity plans come out predictably high. Throughput vs capacity works through the distinction.

The constraint is rarely where the plan puts it

A capacity plan usually names a bottleneck and sizes everything around it. The project record on this site keeps showing that the named bottleneck isn’t the one deciding throughput.

• At a specialty olive packer, a full-plant model confirms the pitting bottleneck and finds other choke points downstream in packaging. Scheduling rules tested in the model raise throughput 15% with no new equipment, and the team tests proposed capital against it before committing it.
• A craft brewery planning to add kegging and bottling finds that neither new line is the limit. Either would over-run the fermenting and serving tanks unless capacity is added there first.
• In a coffee Make-Store-Pack operation, changing packaging size moved the bottleneck, which defeats any plan built around a fixed constraint.

The real constraint was already installed and already looked adequate for the mix the plant ran before. Finding it means running the whole line with the change in place. Finding the hidden bottleneck covers the mechanics.

Capex, overtime or reliability: three ways to buy capacity

When a line falls short of the volume, there are three broad ways to close the gap, usually argued in different meetings with different numbers.

A simulation puts all three on the same footing. A specialty chemicals plant asks how many new bag lines to buy against how much overtime to accept. The model answers over a ten-year horizon, splitting each bag line’s weekly hours into production, repair, changeover, idle and overtime. It also shows that short-term storage between bulk production and packaging would decouple two operations the layout ties together, which surfaces low-capital growth options nobody had costed.

Decoupling is the lever most often missed. A cereal plant stabilizes partially cooked grit so it can be stored and later reconstituted, instead of throttling its cookers to what the toasting ovens can take. The model sizes that at 19.8% more effective production in key demand periods, with no new cooking or oven capacity. It’s a peak-period figure, since in a slack period there’s nothing to recover.

The same comparison works at machine level, weighing a second machine against a reliability improvement on the first. It’s one of the five decisions every production system faces.

New plants and consolidations

Capacity mistakes are most expensive before anything is built. A soap manufacturer consolidating several plants and more than 300 products into one facility uses a model to predict the buffer size between processing and packaging. The model also finds that one planned set of machine assignments leaves material in the system too long, a quality problem that would need a $4 million retrofit. Alternative routings are tested in the model instead, and the company now requires a simulation study before any major capital expenditure.

Both have a place. Use the spreadsheet for the first pass and simulation when coupling decides the answer. A major food manufacturer uses simulation to settle how many packaging lines to buy for a product line, where too few throttles a product and too many strands capital.

How a capacity study runs in ReliaSim®

1 · Build

Draw the line as it runs, or as proposed, with storage where it physically sits. Give each failure mode its distributions, either fitted by ReliaStats (downtime data analysis) or entered in the Interrupt Designer for a line that doesn’t exist yet. Turn every interrupt off and the model reports the most this topology can produce.

2 · Validate

Compare the model with the historian failure mode by failure mode before you trust it. The method has been validated within 1% of measured OEE on a real plant (published OEE validation).

3 · Predict

Change one thing at a time. Raise demand, add a machine, resize surge storage, change the changeover sequence, or remove a failure mode. Read capacity as a distribution across replications. Runs are cheap enough to compare candidate plans exhaustively instead of three picked in advance. The ReliaSim method covers each step.

Most of the project work above predates ReliaSim as a product. It was done with the discrete-rate method ReliaSim now packages, and each case study says which tool ran the model.

What simulation doesn’t replace

• A production schedule. A model can test a scheduling rule against variation before deployment, but writing tomorrow’s schedule is a planning system’s job.
• Rough-cut planning across the portfolio. Use simulation on the lines where coupling decides the answer.
• Data. The Interrupt Designer makes failure assumptions explicit, but it doesn’t make them right.
• Someone to build it. That’s manufacturing simulation consulting from ChiAha.

Frequently asked questions