We build models of production lines, plants and supply chains from your own data. Each model has to reproduce what the plant actually did before we use it to answer anything. Pick the question closest to yours below.
Each page below covers one kind of engagement in detail: what we model, the data it takes and what you get back.
Capacity, bottlenecks, buffer sizing, line design and OEE for single lines and whole plants.
Manufacturing simulation →Reliability, availability and maintainability studies for high-speed lines, built failure mode by failure mode from your downtime data.
RAM analysis →Test a target plant's throughput and capacity claims with a production model before the deal closes.
Private equity →Check what an AI tool recommends against a validated model of the line before anyone changes the plant.
AI validation →Inventory placement, run lengths, postponement and distribution networks, modeled over time instead of as a single average.
Supply chain case studies →A vendor-fair guide to matching the method to your system and checking a tool's accuracy claims before you buy.
Buyer's guide →The mix depends on the question. Most projects use several of these together, and the tools behind them are the same ones we sell.
Capacity modeling, bottleneck identification and what-if analysis on the line as it really runs, with stops, starving and blocking included.
Finding the constraint that actually governs output. On a line with frequent stops it often moves as failures come and go.
Comparing the model against your historian data, failure mode by failure mode, before anyone trusts what it predicts.
Line flow modeled as rates that change when machines stop and start, so a year on a high-speed line simulates in seconds.
Testing a configuration before you buy it, and finding out which uncertain inputs actually change the answer.
AI assistants that call the simulation engine and quote its results, so plant teams can ask what-if questions in plain language.
Engagements run on the same engines as our products, so the model doesn't disappear when the project ends. The manufacturing simulation page walks through how an engagement runs, step by step.
We scope it to your decision, build it from your data, validate it against history and hand over the answer with the evidence behind it.
Your team learns to build and maintain models on the same tools, and we review the first ones with them.
License the software and keep the model current as the plant changes. We stay available as extra modeling capacity.
A model earns the right to predict by matching history first. In a peer-reviewed study at the 2020 Winter Simulation Conference, a reliability model of a food plant matched a full year of measured OEE to within one percentage point, failure mode by failure mode.
That model was later rebuilt in ReliaSim and independently validated to the same standard. Read the published validation case study.
Describe the line, plant or network and what you need to decide. We'll tell you whether simulation is the right tool and what a validated answer would take.