ReliaSim / Capacity planning simulation

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

Most capacity plans start from rated speed multiplied by scheduled hours. Lines never deliver that number, and the gap is not a fixed discount. It depends on how machines stop, what storage sits between them, how often the mix changes, and which stage is binding when demand peaks. Capacity planning simulation models those interactions, so a capital request, an overtime plan and a reliability program can be priced against the same line before any of them is committed.

Rated capacity is not effective capacity

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

• Interrupts. A hundred one-minute stops and one hundred-minute stop cost the same minutes, and very different throughput once there is 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 is 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 do not add up neatly. Five machines at 85% OEE in series with no buffering give a line near 44%; with unlimited buffering, 85%. Real lines land between, depending on buffer size against stoppage length. Averages also err in one direction: for machines in series with buffers, expected throughput is strictly less than the figure you get by multiplying average rates and availabilities, so 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 is not the one deciding throughput:

• At Bell-Carter Foods, pitting machines with two-hour changeovers looked decisive. The capacity model found the binding constraints downstream in packaging, and Theory-of-Constraints daily scheduling recovered 15% more throughput with no new equipment. Proposed capital was then tested against the model before it was committed.
• A craft brewery planning to add kegging and bottling found that neither new line was the limit. Either would over-run the fermenting and serving tanks unless capacity was added there first.
• In a coffee Make-Store-Pack operation, changing packaging size moved the bottleneck, defeating 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. Rohm & Haas asked how many new bag lines to buy against how much overtime to accept. The model answered over a ten-year horizon, splitting each bag line’s weekly hours into production, repair, changeover, idle and overtime. It also showed that short-term storage between bulk production and packaging would decouple two operations the layout had tied together, which surfaced low-capital growth options nobody had costed.

Decoupling is the lever most often missed. A cereal plant stabilized partially cooked grit so it could be stored and later reconstituted, instead of throttling its cookers to what packaging could take. The model sized that at 19.8% more effective production in key demand periods, with no new cooking or packaging capacity. That is a peak-period figure: in a slack period there is nothing to recover.

The same comparison works at machine level, a second machine against a reliability improvement on the first. It is 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 used a model to predict the buffer size between processing and packaging. The model also found that one envisioned set of machine assignments left material in the system too long: a quality problem that would have needed a $4 million retrofit. Alternative routings were tested in the model instead, and the company now requires a simulation study before any major capital expenditure.

Both have a place: the spreadsheet for the first pass, simulation when coupling decides the answer. General Mills used 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: constraints with their rates, converters that change the unit of flow, and buffers where storage physically sits. Give each failure mode a time-to-failure and time-to-repair distribution, fitted from line event data by ReliaStats (downtime data analysis) or entered in the Interrupt Designer for a line that does not 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 trusting it. Built from per-failure-mode interrupt data, fitted properly and checked against line history, a ReliaSim model can match measured OEE within 1%. The published proof point: the Fischel & Lange WSC 2020 food plant model was rebuilt in ReliaSim and independently validated by Tom Lange within 1% of both the plant’s measured OEE and the original model, running the same one-year simulation 1,200× faster on the same laptop. See the published OEE validation.

3 · Predict

Change one thing at a time: raise demand, add a machine, resize surge storage, change the changeover sequence, remove a failure mode. Read capacity as a distribution across replications. Runs are cheap: a thousand one-year runs of the five-machine demo bottling line take about two and a half minutes on a laptop, so candidate plans get compared 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 does not replace

• A production schedule. A model can test a scheduling rule against variation before deployment; 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; it does not make them right.
• Someone to build it. That is manufacturing simulation consulting from ChiAha.

Frequently asked questions