The ReliaSim Method — From Production Graph to Confident Decision

Step 1 · Build

Sketch what is. Parameterize what matters.

Draw your production graph node by node. Set rates, mark buffer positions, and compose each machine's behavior from historian data or reliability shapes.

Define every unit operation and the flow between them
Set nominal production rates, conversions, and buffer positions
ReliaStats fits distributions from historian data — TTF and TTR
Each machine gets its own interrupt signature and Perfect Production falls out

So that your model reflects the real system from day one.

Step 1 · Build → Sketch what is.

Draw your production line node by node. Set rates, add buffers, mark decoupling points.

Duration: 90.00 Days Efficiency: 100.0% PerfectProduction: 130,896 pallets

Bulk Storage

Filler A

Capper A

Labeler A

Filler B

Capper B

Labeler B

Case Packer

Palletizer

Warehouse

See it in action — model build video ▼

Drawing the production graph — block by block

Step 1 · Build → Parameterize what matters.

Feed historian data to to find the right failure distributions.

TTF / TTR distribution designer

Source-to-fit — distribution matching

For new systems, the Interrupt Designer lets you choose distribution shapes manually.

Infant Mortality

Decreasing failure rate

Wear-out

Age-related failures

Scheduled

Fixed-duration maintenance

Step 2 · Validate → Validate against history.

Compare to your historian. Each point is an interrupt. On the diagonal = model matches reality.

Model vs. historian — simulated availability vs. actual

Interrupt Validation scatter — source vs. sim availability with 95% PI and 99% CI bands

Each point is an interrupt. On the diagonal = model matches reality.

"The only way to predict this accurately is through simulation."

— Tom Lange, 36 years Procter & Gamble

Step 3 · Predict → Gain ≠ Loss.

Each machine is an actor with its own rhythm. When the ensemble performs together, blocking and starving emerge from the interaction.

Loss → Gain detail

Labeler Misalignment

Loss 6.78% → Gain 5.10%

0.75×

recover less than you lost

Filler Micro Stop

Loss 6.67% → Gain 7.97%

1.2×

56% more recovery

Same loss. Completely different recovery.

From production graph to confident decision

How do you increase production?

Sketch what is. Parameterize what matters.

Step 1 · Build → Sketch what is.

Step 1 · Build → Parameterize what matters.

Step 2 · Validate → Validate against history.

Step 3 · Predict → Gain ≠ Loss.

Now see it on your line.