Case Studies
Real Lines, Real Numbers, Published Where We Can
Thirty-five years of production systems modelled with the rate-based approach that became ReliaSim. Where the work was published, the paper is linked so the claim can be checked rather than taken on trust. Where the client is not named in the source, we do not name them here either.
How a bottling line found $1.5M in hidden throughput
Two failure modes, identical downtime on the loss report. Fixing the bigger one returned less than it cost; fixing the smaller one returned 62% more. Every figure is engine output you can reproduce in the sandbox.
Read the case study → Food ProcessingThe Constraint Everyone Could See
Two-hour changeovers on the pitting machines dominated the loss report. The real limits were downstream in packaging — and scheduling recovered 15% more throughput with no new equipment.
Bell-Carter Foods · published paper → Consumer ProductsThe $4 Million Retrofit That Never Had to Be Built
Consolidating several plants into one, across 300+ products. One machine configuration would have caused a quality problem needing a $4M retrofit. It was found in the model, not in production.
Read the case study → Specialty ChemicalsThe Bagging Lines Were Tied to the Reactors
Packaging dedicated to individual bulk lines. Modelling intermediate storage decoupled them — and surfaced low-capital growth options the client had not known it had.
Rohm & Haas · named client quote → CerealBuying Time Instead of Buying a Line
Stabilising partially-cooked corn grit decoupled a cereal plant’s cookers from its packaging. 19.8% more effective production in peak periods, no new capacity — and the model is live in a browser.
Run the simulator yourself → Food ManufacturingFrom a Snapshot to a Video
General Mills replaced aggregate spreadsheet analysis with a dynamic model. Six months of data in minutes, results in 15 — and the biggest gain arrived during model building, before the first run.
General Mills · published article → CoffeeThe Bottleneck That Would Not Stay Still
A new stage in a Make-Store-Pack line meant the constraint moved whenever packaging size changed. An APS cannot schedule against a bottleneck that will not hold still.
Read the case study → Consumer ProductsToo Little Storage Stops the Expensive End
In-process storage that moved with scheduling rules and reblend volume. Undersize it and expensive upstream processing shuts down. The model was re-implemented at 12+ similar factories.
Read the case study → BrewingThe Expansion Was Not Limited By the Line They Were Buying
Adding kegging and bottling to a craft brewery. Either line would over-run the fermenting and serving tanks — the constraint sat upstream of the capital being spent.
Published paper →On attribution
Most of this work predates ReliaSim as a product. It was done by the team using the discrete-rate method that ReliaSim now packages — the same treatment of flow, variability and downstream coupling. Each page says which tool actually ran the model, and where an account rests on our own project record rather than a published paper, that page says so too. We are not claiming the software in the sandbox produced these numbers. We are claiming the method behind them has been in production use for more than three decades — and that it now ships in a package fast enough to run a thousand one-year simulations in about thirty seconds.
Looking for the explanations rather than the examples? Those are in the guides.