ReliaSim / Comparisons / ReliaSim vs ExtendSim

ReliaSim vs ExtendSim
The same validated model, run in both: within 1%, and 1,200× faster

Most software comparisons are feature lists. This one is a single experiment. A published, peer-reviewed reliability model of a real food plant was built in ExtendSim and validated against the plant’s measured OEE. The same model was rebuilt in ReliaSim, and Tom Lange independently validated that too. Both came within 1%. On the same laptop, over the same simulated year, ReliaSim ran roughly 1,200× faster.

The same model, two engines

In their 2020 Winter Simulation Conference paper, Fischel and Lange modeled a real multi-line food plant, not a teaching example. The model is a per-failure-mode discrete rate and reliability model. It carries up to twenty failure modes on each of more than twenty unit operations.

Its structure matters for what follows. A reliability block diagram sits inside each rate-affecting unit operation, carrying that unit’s failure modes and letting its failure rate scale with production rate. The line itself comes from discrete rate coupling through the valves, tanks and conveyors between the units. The authors built it in ExtendSim, and it is the featured case study on ExtendSim’s own reliability block diagram page.

The validation bar they set was agreement within 1% of overall system OEE between a one-year simulation and the plant’s actual data. The model met it. The paper is also clear about why the per-mode detail is necessary:

The independent validation

That published model was rebuilt in ReliaSim and independently validated by Tom Lange, a co-author of the paper. The ReliaSim model came within 1% of the plant’s measured OEE, the same bar the original met, and within 1% of the original ExtendSim model.

Those are two different checks, and both matter. Agreement with the plant says the rebuild reproduces reality as well as the published model did. Agreement with the original model says the two engines, given the same structure and the same failure data, produce the same answers. The comparison below is therefore not “which tool is more accurate.” On this model they agree. It is what the same accurate answer costs in time.

The paper is two pages and open access, so you can check the original model yourself. Our write-up of the study is the published OEE validation case study.

The clock

Both models were run on the same laptop over the same one-year simulated duration. ReliaSim finished roughly 1,200× faster in real clock time. Because the hardware and the simulated horizon were held the same, the ratio compares the engines, not the machines they ran on.

The paper reports the validation, not run times; the timing comes from Tom Lange’s validation of the rebuild. For a sense of scale on a smaller line, a thousand one-year runs of ReliaSim’s five-machine demo bottling line take about two and a half minutes on a laptop, measured at 0.154 seconds per simulated year.

What the speed buys

A one-year run that takes a long time is a run you do a few of. You pick the candidates in advance, and the meeting becomes an argument about which ones to pick: an argument about the sampling, dressed up as an argument about the plant. When the same run costs seconds, the handful of hand-picked scenarios stops being a constraint and becomes a choice.

• Sweeps instead of guesses. The buffer sweep behind how big should a buffer be? is 7,770 runs: 1,864 simulated years in 31.8 seconds. You see where the curve bends instead of testing three sizes someone nominated.
• Every failure mode, removed one at a time. Ranking losses by what the line gets back takes one full re-run per failure mode, which on a model the size of Fischel and Lange’s means hundreds of one-year runs. On the bottling line in our case study, two failure modes with near-identical direct losses returned 0.73× and 1.21× their loss when removed. Only the experiment shows which is which. See which loss to fix first.
• Distributions, not single runs. Replications are cheap enough that every answer comes with its spread, which a single simulated year can hide.

The discipline does not change with the clock. You still build the model, you still need per-failure-mode data, and you still validate before any answer is worth having. What changes is the distance between asking a question and having it answered.

Where ExtendSim fits

ExtendSim is a general-purpose simulation toolkit. Its product line covers discrete event simulation, discrete rate simulation and reliability block diagramming, and modelers can build their own custom blocks. If you need several modeling methods in one environment, item-level logic alongside flow, or a component library you extend yourself, it is the broader tool, and ReliaSim does not try to match it. ReliaSim is deliberately narrower: high-speed and bulk-flow production lines, a short vocabulary of constraints, converters, buffers and interrupts, and a build, validate, predict method around it.

The two tools also share a history. Discrete rate simulation originated with Andrew Siprelle in 1990, as “bulk flow” simulation; its first paper, with David Parsons at WSC 1995, was built using Extend. ExtendSim’s Rate Library later built a second generation on that work, including Damiron and Nastasi’s 2008 linear-programming approach and Krahl’s 2009 paper. The full account is on the discrete rate lineage page. The speed difference is decades of the same idea, not a verdict on anyone’s engineering.

Frequently asked questions