How ReliaSim® turns a production graph into an OEE prediction you validate against your own measured year, without needing simulation expertise.
Every system has multiple ways to improve.
Or you can model the system, parameterize it from real data, and predict which lever pays off before you pull it. The three steps below show how.
Sketch the line as it actually runs, as a graph of nodes in whatever topology it has, then give each machine its own interrupt signature. Perfect Production comes out of the model.
Draw your production line node by node. Set rates, add buffers, mark decoupling points.
Modeling projects usually go wrong at this step, without anyone noticing. A graph drawn from a spec sheet describes the line the vendor sold you. A graph drawn from the plant describes the one you actually run. The difference doesn't show up here. It shows up three steps later as a model that’s confidently wrong. At the end of this step you want a topology someone on the floor would recognize, with the right machines in the right order and the buffers where they physically are.
Equal speeds don’t mean no bottleneck. On the bottling demo every machine runs 100 bottles a minute, so nothing looks like the limit. Perfect Production says what the line could make; the gap between that and what it ships is stops, not speed, and the drawing is where that gap starts to show.
Bulk Storage
Filler A
Capper ALabeler AFiller BCapper BLabeler BCase PackerPalletizerWarehouseFit the line event history in 
to find each machine’s failure distributions.
Nominal rates are the easy part, and they don't decide output. How each machine actually fails decides it, meaning how often, for how long, and with how much spread. ReliaStats fits those distributions from your own event history instead of asking you to pick one, so every unit operation carries its own interrupt signature instead of a shared average. Try it in the Sandbox, where the ReliaStats® tools sit next to the bottling-line models.
Averages erase the answer. Two failure modes can cost the same hours and behave nothing alike: frequent short stops versus rare long ones. Fischel and Lange found that with one averaged failure mode per operation, the gains and losses come out “essentially identical to each other”. The detail is what makes the ranking possible.
For a line that doesn’t exist yet, the Interrupt Designer lets you choose the shapes deliberately, which makes the assumption explicit.
Compare to your historian, interrupt by interrupt.
Validation separates a simulation from an opinion, and it’s the step most often skipped. It isn’t a sanity check on the total. Every interrupt is compared individually against what the historian recorded, and each should land on the diagonal inside its confidence band. A model that matches annual OEE while getting the individual failure modes wrong will still answer your questions, but incorrectly, and you won’t find out until the capital is committed.
The line isn’t the product of its machines. Multiply the five machines’ availabilities on the bottling demo and you get 46.7%. The line actually runs at 54.3%, because some stops overlap and some are covered. That is also why the model has to match every interrupt, not just the total: a published food-plant model matched measured OEE within 1%.
Each point is an interrupt. A point on the diagonal means the model matches reality.
"The only way to predict this accurately is through simulation."— Tom Lange, 36 years in manufacturing R&D
Each machine is an actor with its own rhythm. When the ensemble performs together, blocking and starving emerge from the interaction.
Gain ≠ Loss is the part that surprises people, and it’s why a loss report isn’t a plan. Remove a failure mode and you don’t simply get its downtime back. Some of that loss was absorbed, because the line caught up in a buffer or the machine was starved during the stop anyway. Some of it was cascading into the machines downstream, so removing it returns considerably more than it appeared to cost. Which of the two you’re looking at depends on where the failure sits in the system, and no ranking of losses can tell you. The simulation removes each one individually and re-runs the whole system to find out.
The biggest loss isn’t the biggest gain. Labeler misalignment costs 6.79% and gives back 5.0 points when fixed. Filler micro stops cost almost the same, 6.72%, and give back 8.1: 62% more. A loss report ranks them as a tie. Lean, TPM and TOC each pick a different first fix on this line, and none finds the best one.
Same loss, very different recovery.
Everything above rests on a rate-based engine. Because ReliaSim models material as flow instead of tracking every item, a thousand year-long runs of the five-machine bottling demo finish in the time it takes to read this page. Why the engine works that way →
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