We've built models for more than 300 organizations since the early 1990s, from single packaging lines to national distribution networks. Here's what some of them said, and the studies we're able to publish.
“We leveraged the model to recommend solutions that saved $21.7M in inventory carrying costs and reduced transit time by 19%.”
“Instead of taking a snapshot, this is more like a video. Your ability to actually see the activity in action while the supply chain is running lets you understand impacts much better.”
“The team went above and beyond the project scope, proactively brainstorming ways to make our project better.”
Food, consumer goods, pharmaceuticals, chemicals, aerospace and logistics. Most of this work stays confidential, so the published case studies below are a small share of it.
Each study states the decision, how it was modeled and what changed. They're hosted on reliasim.com, alongside the software used for most of them.
A peer-reviewed WSC 2020 model of a food plant matched a full year of measured OEE within one percentage point, failure mode by failure mode.
Read the study →The pitting machines had two-hour changeovers and looked like the constraint. A capacity model found the real limits downstream in packaging.
Read the study →Dynamic simulation replaced aggregate spreadsheet analysis of throughput. It ran six months of data in minutes, and the biggest gain came while building the model.
Read the study →Modeling storage between the reactors and the bagging lines decoupled them and turned up low-capital growth options.
Read the study →A soap manufacturer found that one machine configuration would have caused a quality problem needing a $4 million retrofit. The model caught it before the plant was built.
Read the study →A discrete rate model sized a storage step between a cereal plant's cookers and toasting ovens at 19.8% more effective production in peak periods, with no new capacity.
Read the study →A craft brewery adding kegging and bottling learned that either line would overrun its fermenting and serving tanks unless capacity was added there first.
Read the study →At a coffee plant the bottleneck moved with pack format. At a CPG plant, reblend moved the in-process storage requirement, and that model was reused at 12+ factories.
Read the study →A food plant modeled every line in its current and future packing configurations and ran every product through them, about 30,000 simulation runs.
Read the study →New-vehicle distribution across rail and truck, with every run starting from where each vehicle actually was. One set of recommendations saved $21.7M in carrying costs.
Read the study →A network model showed which product categories gained from packing at the distribution centers, which lost, and that cycle stock belonged at the plants.
Read the study →An electronics manufacturer tested three capacity levels against three sales outcomes over a two-year product life, with flexible ordering and expedited freight.
Read the study →Customers and partners have also written peer-reviewed papers about projects done with our tools. They're listed on the research page.
Tell us what you need to decide. We'll tell you whether a model can answer it and what data it would take.