Production Decision Intelligence

Find the improvements
your Loss Tree
will never surface.

Your Loss Tree ranks by downtime. But the fix with less downtime often recovers 3× more throughput. ReliaSim models the whole system — validated within 1% OEE — so you invest where it actually matters. No simulation expertise required.

See the Six Steps → Book a 15-Min Demo
Independently validated within 1% OEE accuracy — Tom Lange, 36 years Procter & Gamble
The paradox of averages

Equal downtime.
Unequal impact.
Your Loss Tree can't tell the difference.

Consider two failure modes with identical total downtime per week. Your historian treats them the same. Your Loss Tree puts them in the same priority tier. But eliminating them produces completely different system-level results — because cascade effects never appear under the original failure's name.

Scenario 1 — Timing Belt
One long stop per week
Frequency1× / week
Repair time~2 hours
120 min downtime / week
Scenario 2 — Bottle Jams
One hundred twenty short stops per week
Frequency~120× / week
Repair time~1 minute
120 min downtime / week
Your Loss Tree says: equal downtime = equal priority.
The validated simulation says otherwise. Frequent short stops cascade through your entire system — blocking and starving machines that never appear under the original failure's name. The smaller loss recovers five times as much throughput.
See How It Works →
The Six-Step Sequence

From production graph
to confident decision

No simulation degree required. If you can describe your line, you can model it.

Graph
Step 1
Step 2
Parameterize
Step 2A
Simulate
Step 3
Validate
Step 4
Experiment
Step 5
Decide
Step 6
Step 1 · Graph

Capture what is. Prepare for what if.

Set your scope. Establish rates and conversions. Mark where decoupling exists today — and place zero-capacity buffers where it could exist tomorrow. Your Perfect Production pops right out as the OEE denominator.

So that you know your scope, your decoupling, and your theoretical max.

Step 1 — Click-connect what is. Prepare for what if.

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

Duration: 90.00 Days Efficiency: 100.0% PerfectProduction: 129,600 pallets
BufferSource
ConstraintFiller
ConstraintCapper
ConstraintLabeler
ConstraintCase Packer
ConstraintPalletizer
BufferSink
See it in action — model build video ▼
Drawing the production graph — block by block

Step 2 — Use your data to drive your system's behavior.

Feed historian data to ReliaStats to find the right failure distributions. For new systems, the Interrupt Designer lets you choose shapes manually.

Source-to-fit — distribution matching
Q-Q plot
Interrupt Designer — select distribution shapes
Interrupt Designer — TTF and TTR distribution shape selection

Step 3 — Simulate your baseline.

Evaluate your system with eyes wide open to its range of outcomes.

Throughput distribution
Throughput histogram

Step 4 — Validate against history. Within 1%.

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

Model vs. historian — simulated availability vs. actual
Bivariate validation

Step 5 — Experiment & Compare.

Eliminate failure modes one at a time. Measure the whole system. Gain ≠ Loss — always.

Efficiency Gain/Loss — evaluate each interrupt, one at a time
Gain/Loss chart

Step 6 — Make confident decisions.

Commit capital where the validated model says it belongs — not where your Loss Tree guesses.

Decision Engine

Five decisions every production system faces.
ReliaSim answers all of them.

These aren't hypothetical — they're the capital and operational questions that determine whether your next investment pays off or falls flat.

~1%
OEE accuracy
15 min
Model construction
1,200×
Faster than traditional
3×
Typical ROI multiplier
300+
Organizations since 1995
Capital Decisions — high cost, irreversible
Operational Decisions — frequent, hidden impact
🔧
Which fix yields the biggest gain?
Rank by system-level recovery, not downtime hours. The biggest loss on your Pareto is rarely the biggest improvement.
⚡
Run faster or slower?
Running faster makes more product but causes more failures — and you don't repair any faster. There's an optimal speed.
📦
How much buffer?
Protect the bottleneck with accumulation — but how big should it be? Simulation answers exactly.
🔁
Do we need a second machine?
Compare equipment redundancy vs. reliability improvement on equal statistical footing — before spending capital.
🔄
Manual or automatic resupply?
Shutting down a perfectly running line every 15 minutes for a manual splice? Simulation often shows the true cost is 3× what downtime analysis claims.
"The only way to predict this accurately is through simulation."
— Tom Lange, 36 years Procter & Gamble · Watch the full podcast
AI-Powered via MCP

Ask your validated model anything

ReliaSim exposes your production model through a Model Context Protocol (MCP) server. Claude and other AI assistants can load, inspect, simulate, and interrogate your model in plain language — no copy-paste, no screenshots.

🔍

Inspect model topology and constraints

Ask about node types, buffer positions, interrupt configurations, and constraint availability — all in plain language from your AI assistant.

▶️

Run simulations on demand

Run simulations and get back per-constraint availability, OEE, and multi-run statistics instantly.

📈

Buffer analysis and production through time

Get buffer utilization, inflow/outflow, and percentage time empty or full. See constraint timelines and throughput distributions across replications.

📄

Generate diagrams and full reports

Visualize your production topology and export detailed reports — all from a single conversation. Share with your team without leaving the chat.

22 MCP tools — load model, run simulation, get constraint analysis, interrupt breakdown, buffer analysis, multi-run summary, Mermaid diagram, and more. Works with Claude, any MCP-compatible AI assistant, and Claude Code.
Bottling Line 90-day run — Efficiency 53.6%
53.6% OEE 69,498 pallets / 90 days
ReliaSim AI — Bottling Line Validated ✓
You
The model is validated at 53.6% OEE. Which machine should I fix first to move that number?
RS
RS
The Capper is your critical path. It logged 15,481 downtime events over 90 days — more than triple any other machine — with only 75.44% internal availability. Its downstream position means every outage starves the Labeler, Case Packer, and Palletizer simultaneously. Start there.
You
Could adding a buffer before the Capper help without touching the machine itself?
RS
RS
Yes — a pre-Capper buffer absorbs Filler surges and prevents starvation during short Capper outages. Estimated gain: +2–3% OEE at low capital cost. But the Capper's 12 active interrupt types still fire at the same rate. The buffer buys resilience; fixing the Capper's interrupts buys efficiency. Run both scenarios and compare.
Ask about throughput, buffers, interrupts, or what-if scenarios…

Responses powered by your specific validated production graph — not a generic AI.

Food & Beverage Pharma & Life Sciences Aerospace & Defense Energy & Chemical Packaging & CPG Paper & Pulp Semiconductor Academic Research
Tom Lange
"Those 120 one-minute interruptions are creating cascading problems throughout your system that don't show up under the original problem's name. When you eliminate them, you often recover 180–220 minutes of uptime — significantly more than the original downtime suggests. The only way to predict this accurately is through simulation."
Tom Lange
Technology Optimization & Management LLC
36 years, Procter & Gamble · Retired Director, Modeling & Simulation, Corporate R&D
Co-author, "High Accuracy Discrete Rate and Reliability Modeling" (Winter Simulation Conference 2020)
✓ Validated within 1% OEE accuracy
▶ Watch the podcast
"It runs on my desktop. No integration, no IT project, no waiting. I just install it and start modeling."
— Engineering Leader, Essity
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See how a bottling line used the six steps to find $1.5M in hidden throughput — and which fix their Loss Tree would have missed.