Decision Engine

ReliaSim® answers all five decisions every production system faces

These are the real capital and operational questions that decide whether your next investment pays off.

Predictable wear-out is only 2–4% of downtime on a high-speed line. The other 95% of events aren't predictable before they happen, like bathroom stops and traffic jams on a road trip. You can't schedule around them, so you have to design around them, and these five decisions are how you do it.

1%
Published OEE validation
15 min
Model construction
0.154 s
Per simulated year
3×
Typical ROI multiplier
300+
Organizations since the early 1990s
Face validity vs. statistical validity

3D animated models look like your line, but they can take 6–8 hours to run a single scenario. Looking like the line is face validity. A playing field for strategic capital decisions needs statistical validity, which means a model validated against your actual OEE and fast enough to run a thousand one-year what-ifs in about two and a half minutes (measured on the bottling demo).

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. So there's a best speed for each line.
📦
How much buffer?
Protect the bottleneck so upstream and downstream processes never shut it down. Too little starves it and too much ties up capital, and simulation finds the right size.
🔁
Do we need a second machine?
Compare equipment redundancy with reliability improvement on equal statistical footing before you spend capital.
🔄
Manual or automatic resupply?
Shutting down a perfectly running line every 15 minutes for a manual splice? Downtime analysis says fixing it is worth half a percent, but the simulation usually says 1.5%, which is enough to justify the automatic splicer.
"The only way to predict this accurately is through simulation."
— Tom Lange, 36 years in manufacturing R&D
All you need to start is line event data, meaning a CSV of machine stop-and-start times recorded to the second rather than summaries or rolled-up totals. We handle data cleaning, parameterization and the model build.

See the evidence behind one of these decisions

The case study follows a single throughput decision from Loss Tree to validated model and shows where the two disagree.

Open the Sandbox → Read the case study