Simulation for Food & Beverage Production
Where the Material Is a Flow, Not a Queue of Parts
Most simulation software was built for discrete manufacturing, where parts move through workstations. Food and beverage plants work differently. Product is bulk, liquid or paste for most of the process, and it has a shelf life. The line stops for sanitation whether or not it's running well, and demand arrives in seasons nobody controls.
Five things that make a food line hard to model
1. The material is a rate
Grain flowing into a cooker, syrup into a filler and olives through a grader aren't queues of entities. The usual approach is to lump bulk into pseudo-entities. The usual result is a model that either runs too slowly to be useful or is too coarse to trust. Rate-based simulation treats flow as flow, so a full year of a line runs in under a second here.
2. Changeovers are the schedule
Lines change over for sizes, flavors, labels and allergen sequences. In an olive plant, a two-hour changeover between olive sizes makes the pitters a bottleneck. A shifty bottleneck that moves whenever packaging size changes will defeat an Advanced Planning System optimizing against a fixed constraint. Both are real cases on this site.
3. Sanitation isn't downtime you can eliminate
Clean-in-place windows and changeover cleans are the cost of operating legally and safely. They belong in the model as scheduled structure, not as losses to attack. Mixing the two up is how improvement programs end up chasing something they can't have.
4. Shelf life turns storage into a time limit
In discrete manufacturing a buffer decouples two stages for as long as you have space. With partially-cooked or unpasteurized product it decouples them for as long as the material stays good, which may be minutes. In the cereal case on this site, stabilizing the intermediate turned a buffer measured in minutes into one measured in weeks.
5. Supply arrives when it arrives
Harvest windows, crop variability and seasonal demand set the pace. A plant sized for the average is idle half the year and constrained the other half. The real question isn't "what is our capacity" but "what is our capacity during the peak, given how the line actually behaves."
In every one of these, the constraint isn't where the loss report says it is. Downtime that looks identical on a Pareto has very different consequences depending on where it sits, what's buffering it, and what the line was running at the time.
Who has used it
Kellogg’s · General Mills · ConAgra · Tyson Foods · Hormel · Rich Products · Nestlé · Cargill · Hershey · Frito-Lay · McKee Foods · Bell-Carter Foods
“I’ve never seen anything like this.”
VP of Food Production
Food and beverage work on this site
$1.5M in hidden throughput
Two failure modes have near-identical downtime. Removing the smaller one returns 62% more.
Read the case study → Salad Dressing · PublishedMore output without another line
A plant model built from its own downtime records matches a year of actual OEE within 1% and finds many small fixes worth many points of OEE.
Fischel & Lange · open access → Olive Processing15% more throughput, no new equipment
At a specialty olive packer, sales are rising and the whole crop arrives in six weeks. Scheduling rules tested in a full-plant model raise throughput 15%.
Olive packer · published paper → Cereal19.8% more effective production
Stabilizing partially-cooked grit decouples the cookers from the toasting ovens. The model is live in a browser.
Run the simulator → CoffeeThe Shifty Bottleneck
A new stage in a Make-Store-Pack line meant the constraint moved with packaging size.
Read the example → BrewingThe expansion is limited upstream
Kegging or bottling would over-run the fermenting and serving tanks either way.
Published paper → Food ManufacturingFrom a snapshot to a video
A major food manufacturer’s move to simulation starts with a plant capital decision: how many packaging lines to buy for a product line.
Food manufacturer · published article →A free line assessment, this fall
Bring one question about your line. We model it on your own data and send back a two-page readout in about five business days.
Apply for a free line assessment →Or explore the Sandbox first, with no sign-up.