# ReliaSim

> ReliaSim is simulation software from ChiAha for production and supply chain, designed with the executive in mind and built on a fast discrete rate engine that works like a game engine: easy to use, and focused on quick wins and what each decision is worth. It covers a single machine, a whole line or a supply chain network, and shows what would happen to throughput, service, stock and cost if a change were made, before making it. It finds the real bottleneck, sizes buffers, tests inventory and network decisions, and exposes where supply and demand don't match. Executives and operations leaders use it to decide where capital and effort go; engineers and planners build the models.

- Runs on the desktop: Mac (Apple Silicon) and Windows 11.
- Engine: discrete rate simulation — models material flow at rates rather than individual items.
- The published Fischel & Lange (Winter Simulation Conference) food plant model was rebuilt in ReliaSim and independently validated by Tom Lange within 1% of both the plant's measured OEE and the original ExtendSim model, running the same one-year simulation 1,200× faster on the same laptop.
- The 1% figure comes from that one published plant validation.
- Public MCP server (streamable HTTP): https://reliasim.com/mcp
- Full text of every page listed here, in one file: https://reliasim.com/llms-full.txt
- discreterate.com, scmodeling.com and reliastats.com now redirect here. Discrete rate simulation lives at https://reliasim.com/discrete-rate.html, supply chain simulation and network design at https://reliasim.com/supply-chain-simulation-software.html and https://reliasim.com/guides/supply-chain-simulation.html, and downtime data and reliability statistics (ReliaStats) at https://reliasim.com/reliastats.html and https://reliasim.com/guides/downtime-data-analysis.html.

## Product

- [ReliaSim home](https://reliasim.com/): It's the stops that drive your output. Simulation for high-speed production lines, built from each line's own stop history, to see what a change would do before spending on it.
- [Why simulation belongs in the capital plan](https://reliasim.com/guides/why-executives-simulate.html): For executives. Loss trees rank stops wrong; a validated playing field shows what a change returns before capital is committed.
- [How a ReliaSim project runs](https://reliasim.com/how-a-project-runs.html): The steps from first question to decision, with the data needed, the insights and the deliverable at each step.
- [Free line assessment](https://reliasim.com/assessment): Apply with one question about your line; it's modeled on your data and returned as a two-page readout.
- [Sandbox](https://reliasim.com/sandbox): Browser demos in four tiers, no install: Flow (discrete rate teaching models, /sandbox?tier=flow), Stats (ReliaStats, /sandbox?tier=stats), Lines (ReliaSim bottling-line models, /sandbox?tier=lines) and Networks (supply-chain demos: network design, stock over time and tariff or fuel cost shocks, /sandbox?tier=networks). Every number comes from a saved engine run; ask questions in plain language.
- [MCP server](https://reliasim.com/mcp): Connect an AI assistant to ReliaSim's public MCP endpoint for curated reference results from saved engine runs.
- [Use ReliaSim from Claude or ChatGPT](https://reliasim.com/ai): How to connect an AI assistant to ReliaSim over MCP so the numbers in its answers come from engine results; setup for Claude, Claude Code and ChatGPT custom connectors.
- [Manufacturing simulation software](https://reliasim.com/manufacturing-simulation-software.html): Desktop simulation for high-speed lines — bottlenecks, buffer sizing and what would happen to OEE if the line changed, from existing line data.
- [OEE simulation](https://reliasim.com/oee-simulation.html): See what would happen to line OEE before changing the line, from per-machine interrupt data.
- [Capacity planning simulation](https://reliasim.com/capacity-planning-simulation.html): Rated capacity vs effective throughput under downtime, buffers, changeovers and mix, before capex.
- [OEE monitoring vs OEE simulation](https://reliasim.com/oee-monitoring-vs-oee-simulation.html): Monitoring measures past OEE and supplies the data; simulation shows what would happen if you made a change.
- [Methodology](https://reliasim.com/method.html): Build, validate, then test what would happen if — how a production graph becomes a validated model of line OEE.
- [Capabilities](https://reliasim.com/capabilities.html): The rate-based engine, the node types a model is built from, and what that buys in speed and fidelity.
- [Five decisions every production system faces](https://reliasim.com/decisions.html): Add a machine, resize a buffer, change the schedule, chase a failure mode, or accept the line — and how a validated model answers each.
- [Videos](https://reliasim.com/videos.html): Short captioned films: Introducing ReliaSim, Simulate Before You Spend, New to Discrete Rate.
- [Security & deployment](https://reliasim.com/security.html): Desktop-native, no outbound data flow, air-gap compatible; answers for IT and security reviewers.
- [Training](https://reliasim.com/training.html): Guided sessions on discrete rate modeling, model building, interrupts, parameterization, validation and experimentation.
- [ReliaStats](https://reliasim.com/reliastats.html): ReliaSim's companion tool; fits per-failure-mode TTF/TTR distributions from line event data across 8 distribution types and exports ReliaSim-ready parameters. Free Designer (app at https://reliasim.com/reliastats/designer), paid desktop fitting (buy and download at https://chiaha.onfastspring.com/wishbone).
- [Pricing](https://reliasim.com/pricing.html): Licensing options for ReliaSim and ReliaStats (ReliaStats plans at https://reliasim.com/pricing.html#reliastats).

## Guides

- [Guides index](https://reliasim.com/guides.html): Every guide: line throughput, OEE, bottlenecks, buffers, downtime data, reliability, simulation methods and supply chain network design.
- [Find the production bottleneck: starved vs blocked](https://reliasim.com/guides/find-production-bottleneck.html): Why the slowest machine usually isn't the constraint, and how starved and blocked time point to the one that is.
- [Line OEE vs machine OEE](https://reliasim.com/guides/line-oee-vs-machine-oee.html): OEE calculation (A × P × Q) and why line OEE is not the average of machine OEEs, with a calculator for machines in series.
- [How big should a buffer be?](https://reliasim.com/guides/how-big-should-a-buffer-be.html): Buffer sizing across Lean, Theory of Constraints and Factory Physics views.
- [Throughput vs capacity](https://reliasim.com/guides/throughput-vs-capacity.html): Rated vs effective capacity and where capacity goes before it becomes output.
- [Micro stops vs breakdowns](https://reliasim.com/guides/micro-stops-vs-breakdowns.html): Why frequent short stops can cost more throughput than rare breakdowns.
- [Heritage: 35+ years of rate-based simulation](https://reliasim.com/heritage.html): The team behind ReliaSim wrote the Extend libraries that became ExtendSim's Discrete Rate and Database libraries; its case studies were built in ExtendSim and rebuilt in ReliaSim.
- [Lean, TPM or TOC: which would fix this line first?](https://reliasim.com/guides/lean-tpm-toc-which-fix-first.html): Lean, TPM and the Theory of Constraints each pick a different first fix for the same bottling line; a validated model scores all three.
- [Which of the six big losses to fix first](https://reliasim.com/guides/which-of-the-six-big-losses-first.html): Ranking losses by recoverable throughput rather than minutes lost.
- [Is 85% OEE achievable?](https://reliasim.com/guides/is-85-percent-oee-achievable.html): Why a single-machine benchmark does not transfer to a line in series, and how to compute a line's ceiling.
- [The Improvement Roadmap](https://reliasim.com/guides/improvement-roadmap.html): Which fixes, in what order, to reach a target efficiency, from engine runs on a model of the line. On the demo line seven fixes reach 85.2%; add one 100-case buffer before the Palletizer as the second step and five steps pass 85%, seven reach 88.9%; none of 2,000 random plans reach 85%. Part of "the science of continuous improvement" with Gain/Loss and the Buffer Sizing Experiment (https://reliasim.com/capabilities.html#continuous-improvement).
- [Hidden factory: asset efficiency vs OEE](https://reliasim.com/guides/how-big-is-your-hidden-factory.html): Measuring capacity already owned but outside scheduled time, and how the time denominator decides whether Asset Efficiency, OEE and TEEP agree.
- [OEE improvement tradeoffs](https://reliasim.com/guides/oee-improvement-tradeoffs.html): Speed vs stops, downtime vs buffers, machine vs line OEE — pricing each fix.
- [Theory of constraints simulation](https://reliasim.com/guides/theory-of-constraints-simulation.html): Finding, buffering and tracking a constraint that moves with downtime, mix and schedule.
- [Capacity without capital](https://reliasim.com/guides/capacity-without-capital.html): No-capital levers ranked on engine runs: the failure mode that gives most back, buffer placement, and schedules that cause bin lock.
- [A flight simulator for your plant](https://reliasim.com/guides/flight-simulator-for-your-plant.html): Training plant teams on a validated playing field built from their own line, including a custom version of the Beat the Bottleneck game.
- [Spreadsheet, dashboard, AI or simulation?](https://reliasim.com/guides/spreadsheet-ai-or-simulation.html): What each approach can and can't tell you about a production line, including disruption what-ifs a dashboard can't answer.
- [Reliability block diagram vs line simulation](https://reliasim.com/guides/reliability-block-diagram-vs-simulation.html): Why a series RBD misstates line throughput once storage decouples machines.
- [RAM analysis](https://reliasim.com/guides/ram-analysis.html): Reliability, availability and maintainability defined, what a RAM study answers, and where line simulation fits.
- [Value stream map simulation](https://reliasim.com/guides/value-stream-map-simulation.html): Turning a static VSM of averages into a rate-based model.
- [Why predictive maintenance can't answer what-if](https://reliasim.com/guides/predictive-maintenance-cannot-answer-what-if.html): Condition monitoring says when a machine will fail; it can't say what a change returns, because it models the plant you already have.
- [Why an AI can't answer what-if](https://reliasim.com/guides/why-ai-cannot-answer-what-if.html): A language model answers a question about your line fluently and with a number, with no model of the line behind it.
- [Test a line change across every product](https://reliasim.com/guides/test-every-configuration-every-product.html): How to test a packing, staffing or equipment change against every product a line makes. Example: a food plant that ran about 30,000 simulations, every line in every configuration for every product, from the command line.

### Reliability statistics

- [Downtime data analysis](https://reliasim.com/guides/downtime-data-analysis.html): From a stop log to failure-mode TTF/TTR distributions a simulation can run.
- [Weibull analysis of downtime data](https://reliasim.com/guides/weibull-analysis-downtime-data.html): Shape and scale parameters, fitting each failure mode separately, goodness of fit.
- [MTBF vs MTTR: outputs, not inputs](https://reliasim.com/guides/mtbf-mttr-distributions.html): Why a model takes TTF/TTR distributions and MTBF, MTTR and OEE come out of the run.
- [Downtime Pareto](https://reliasim.com/guides/downtime-pareto.html): Ranking stops by frequency, duration or lost throughput, and why the three disagree.
- [Availability vs reliability](https://reliasim.com/guides/availability-vs-reliability.html): Share of time up vs chance of running without a stop; inherent vs operational availability.

### Supply chain simulation and network design

- [Supply chain simulation software](https://reliasim.com/supply-chain-simulation-software.html): ReliaSim for supply chain networks: eleven browser network demos with no sign-up, and supply chain studies on your own network run as projects.
- [What is supply chain simulation?](https://reliasim.com/guides/supply-chain-simulation.html): When to use it, what it answers that optimization can't (and the reverse), greenfield vs brownfield design, and a what-would-happen-if question for each network demo.
- [How supply chain simulation works](https://reliasim.com/guides/how-supply-chain-simulation-works.html): The discrete-event engine, inventory position and ordering policy, what a run shows, and the limits of the demos.
- [Supply chain optimization vs simulation](https://reliasim.com/guides/supply-chain-network-optimization.html): When to optimize and when to simulate, and how network optimization works: mixed-integer programming for network design: decision variables, landed-cost objective, constraints and the classic problem types.
- [Greenfield network design](https://reliasim.com/guides/greenfield-network-design.html): Siting DCs from scratch, location-allocation for several DCs, service-distance constraints, and how greenfield differs from brownfield and footprint rationalization.
- [Center of gravity analysis](https://reliasim.com/guides/center-of-gravity-analysis.html): The demand-weighted balance point in plain terms, a worked example, what the method leaves out, and testing the answer by simulation.
- [How many distribution centers do you need?](https://reliasim.com/guides/how-many-distribution-centers.html): Outbound distance against fixed cost and inventory, reading the elbow on a DC-count curve, the square root law and its assumptions.
- [How to know your supply chain model is right](https://reliasim.com/guides/validate-a-supply-chain-model.html): Reproduce history before trusting scenarios: what to compare (flows by lane, stock by site, lead times), holdout periods and common data errors.
- [Free supply chain simulator](https://reliasim.com/sandbox/networks): Index of the nine browser network demos, one page each: [Sketch a network](https://reliasim.com/sandbox/networks/sketch-a-network), [Greenfield · US](https://reliasim.com/sandbox/networks/greenfield-us), [Brownfield · EU](https://reliasim.com/sandbox/networks/brownfield-eu), [Footprint rationalization](https://reliasim.com/sandbox/networks/footprint-rationalization), [Simple supply chain](https://reliasim.com/sandbox/networks/simple-supply-chain), [Cookie production](https://reliasim.com/sandbox/networks/cookie-production), [Safety stock](https://reliasim.com/sandbox/networks/safety-stock), [Tariff: offshore or reshore](https://reliasim.com/sandbox/networks/tariff-offshore-reshore), [Coffee co-pack](https://reliasim.com/sandbox/networks/coffee-co-pack).
- [Tariff network redesign](https://reliasim.com/guides/tariff-network-redesign.html): Treat the tariff as a cost on the lane it applies to, run the whole range of rates, re-optimize sourcing and footprint, then simulate the redesign. Includes the tariff demo walkthrough (sourcing moves from China to Mexico between 7.5% and 25% on that network).
- [The bullwhip effect](https://reliasim.com/guides/bullwhip-effect.html): What the bullwhip effect is, its four causes, the bullwhip ratio, order fill rate versus on-time fill rate, and why common fixes backfire, on a simulated supplier, plant and big-box store.
- [Safety stock simulation](https://reliasim.com/guides/safety-stock-simulation.html): The safety stock formula and its assumptions, pooling at a central DC versus holding stock near customers, and testing a policy over time.
- [Distribution Network Design](https://reliasim.com/guides/distribution-network-design.html): The four decisions (count, location, allocation, inventory placement), redesigning an existing network, optimization proposes and simulation tests.
- [Multi-Echelon Inventory](https://reliasim.com/guides/multi-echelon-inventory.html): What echelons are, why site-by-site targets over-stock, how stock at one tier buffers the next, and testing a policy through the worst season.
- [Supply chain glossary](https://reliasim.com/guides/glossary.html): Short definitions of network design, inventory and simulation terms, each linked to its guide.

### Simulation methods

- [What is discrete rate simulation?](https://reliasim.com/discrete-rate.html): Flow modeled as rates, the three primitives (constraints, buffers, interrupts), where the method fits and where it came from.
- [Discrete rate vs discrete event simulation](https://reliasim.com/guides/discrete-rate-vs-discrete-event-simulation.html): What counts as an event in each, where discrete event struggles on high-speed lines, and which tools use which method.
- [History of Discrete Rate Simulation](https://reliasim.com/guides/discrete-rate-history.html): Created by Andrew Siprelle in 1990 as bulk flow simulation; the 1995 paper, the late-2000s rename, the published record.
- [The Lineage of Discrete Rate Simulation](https://reliasim.com/guides/discrete-rate-lineage.html): Pritsker's SLAM (1979), Siprelle's bulk flow blocks, the ExtendSim Rate Library and today's engine, with primary sources and the full bibliography.
- [Migrating ExtendSim and Plant Simulation Models](https://reliasim.com/guides/simulation-model-migration.html): Moving a model from ExtendSim or Plant Simulation to ReliaSim: the data into ReliaDB tables (CSV), the layout rebuilt as constraints, converters and buffers, parity proved against the original model and plant history. ChiAha does the migration as a service, starting with a free migration assessment.
- [Simulation methodologies](https://reliasim.com/guides/simulation-methodologies.html): Discrete event, discrete rate, continuous, agent-based, system dynamics and Monte Carlo compared.
- [Monte Carlo vs discrete rate simulation](https://reliasim.com/guides/monte-carlo-vs-discrete-rate-simulation.html): When a static spreadsheet model is enough and when you need a clock.
- [Digital twin vs simulation](https://reliasim.com/guides/digital-twin-vs-simulation.html): Digital model, shadow and twin; why validation comes before a live connection.

## Comparisons

- [Comparisons hub](https://reliasim.com/compare/): How ReliaSim compares with other simulation and reliability tools, by method and purpose.
- [ReliaSim vs ExtendSim](https://reliasim.com/compare/reliasim-vs-extendsim.html): The published Fischel & Lange model rebuilt in ReliaSim — validated within 1% of measured OEE and the original, 1,200× faster.
- [ReliaSim vs anyLogistix](https://reliasim.com/compare/reliasim-vs-anylogistix.html): When a dedicated supply chain optimization platform fits and when validation-first simulation from the production line to the network fits; claims about anyLogistix cite its own pages.
- [ReliaSim vs BlockSim](https://reliasim.com/compare/reliasim-vs-blocksim.html): RBD reliability analysis vs discrete rate simulation of production lines.

## Case studies

- [Case studies index](https://reliasim.com/case-studies.html): Production systems modeled with the rate-based approach behind ReliaSim, with papers linked where published.
- [More salad dressing without another line](https://reliasim.com/case-studies/published-oee-validation.html): Fischel & Lange (Winter Simulation Conference). A salad dressing plant faces rising demand; a per-failure-mode plant model matches actual OEE within 1% and finds many small fixes instead of a new line. Rebuilt in ReliaSim and independently validated by Tom Lange, ~1200x faster.
- [Bottling line worked example](https://reliasim.com/case-study.html): Where the loss tree names the wrong fix and what the validated simulation finds instead.
- [Modeling Every Failure Mode at Once](https://reliasim.com/case-studies/every-failure-mode-at-once.html): A global consumer-products maker models the hundreds of failure modes on each line together, from its own stop data. By the company's figures, productivity rises up to 44% and savings pass $2 billion. The approach ReliaSim is built on.
- [Consolidating a Soap Maker’s Plants Into One](https://reliasim.com/case-studies/soap-plant-consolidation.html): A household-products manufacturer consolidating several soap plants into one, with over 300 products, simulates the whole plant first. The model shows the planned machine assignments would cause a quality problem; other routings avoid it and a $4 million retrofit.
- [A Brewpub Plans Kegging and Bottling](https://reliasim.com/case-studies/microbrewery-expansion.html): A brewpub planning to add kegging and bottling equipment models its brewery first. Either line would overrun the fermenting and serving tanks unless more tank capacity goes in too, and management is more confident about the return after comparing several scenarios.
- [Sizing Grit Storage Between a Cereal Plant’s Cookers and Ovens](https://reliasim.com/case-studies/cereal-decoupling.html): A food-process technology developer's patented technology lets a cereal plant stabilize and store partially cooked corn grit instead of holding its cookers to what the toasting ovens can take. A discrete-rate model sizes the system: one off-ramp and one on-ramp give 19.8% more effective production in key demand periods, with no new cooker or oven.
- [Choosing Where to Pack and Where to Hold Cycle Stock](https://reliasim.com/case-studies/pack-at-plant-or-dc.html): A consumer goods manufacturer weighs moving packing from its plants to its distribution centers. A network model shows downstream packing cuts finished-goods inventory and ends redeployment, but new bulk inventory offsets the gains for many product categories. Cycle stock is best held at the plants.
- [Sizing Overseas Capacity Before the Peak Is Known](https://reliasim.com/case-studies/lifecycle-capacity-commitment.html): An electronics manufacturer has to commit capital to overseas component capacity before it knows how big the product will be. A supply chain simulation crosses three capacity levels with three sales outcomes, shows the cost of guessing wrong either way before the money is spent, and tests flexible ordering and air-freight expediting.
- [Growing an olive plant's output](https://reliasim.com/case-studies/olive-plant-capacity-scheduling.html): A specialty olive packer faces rising sales and a six-week harvest; a full-plant simulation shows what the plant can handle and where capital should go. Scheduling rules tested in the model raise throughput 15% without new equipment. Published paper.
- [From a Snapshot to a Video](https://reliasim.com/case-studies/food-manufacturer-spreadsheets-to-simulation.html): A major food manufacturer moves production decisions from spreadsheets to dynamic simulation: six months of data run in minutes, results in as little as 15, and an estimated $5 million saved across 40 models company-wide, most of it from stopping projects that would have backfired.
- [Weighing New Bag Lines Against Overtime](https://reliasim.com/case-studies/chemical-bag-lines-vs-overtime.html): At a global specialty chemicals company's plant, each bag line is tied to a bulk production line, and overtime is the central cost. A model run over a ten-year horizon weighs new bag line configurations against overtime, tests short-term storage between bulk production and packaging, and turns up low-capital growth options the company hadn't seen.
- [The Cereal Plant That Runs Its Model Before Every Shift](https://reliasim.com/case-studies/cereal-plant-shift-model.html): A major cereal maker's planners run a model of their make-store-pack plant before every shift to see what could go wrong, and change the plan before the trouble reaches the floor. The model treats cereal as flow, which makes it fast enough to run every shift.
- [Forecasting New-Vehicle Flow Across a Continental Network](https://reliasim.com/case-studies/vehicle-network-live-data-forecast.html): A railroad-owned vehicle-logistics company runs a major automaker’s new-vehicle distribution across a continent. Its network model, built with our team, starts each forecast from where every vehicle actually is and settles within a day or two. By the company’s figures, one set of its recommendations saved $21.7M in inventory carrying costs and cut transit time 19%.

## Industries

- [Industries overview](https://reliasim.com/industries.html): Where rate-based simulation applies and what binds throughput in each industry.
- [Food & beverage](https://reliasim.com/industries/food-and-beverage.html): Changeovers, sanitation windows, shelf life and seasonal supply.
- [Packaging & CPG](https://reliasim.com/industries/packaging-and-cpg.html): Surge sizing, changeovers, micro-stops and reblend.
- [Chemicals, oil & gas](https://reliasim.com/industries/chemicals-oil-and-gas.html): Intermediate storage, campaign scheduling and tank farms.
- [Pharma & life sciences](https://reliasim.com/industries/pharma-life-sciences.html): Quality release, validated cleaning and batch integrity as constraints.
- [Electronics & automotive](https://reliasim.com/industries/electronics-automotive.html): Takted lines with minimal inventory where every stoppage propagates.
- [Aerospace & defense](https://reliasim.com/industries/aerospace-defense.html): Throughput decided by fixtures, bays and qualified people.

## Optional

- [Academic program](https://reliasim.com/academic.html): ReliaSim licenses for classroom teaching and non-commercial academic research.
- [Conferences](https://reliasim.com/conferences.html): Sandbox, case study and demo links from ChiAha conference stands.
- [ReliaStats Designer](https://reliasim.com/reliastats/designer): The free ReliaStats Designer app (runs in the browser). The Interrupt Explorer (sign-in) is at https://reliasim.com/reliastats/explorer.
- [ReliaStats MCP](https://reliasim.com/reliastats/mcp/v1): Public MCP server with 11 reliability-theory and closed-form math tools (JSON-RPC over POST). reliastats.com now redirects to reliasim.com; reliastats.com/mcp/v1 still answers for existing clients.
- [Privacy policy](https://reliasim.com/privacy)
- [Software license](https://reliasim.com/license)
- [Beat the Bullwhip](https://reliasim.com/beat-the-bullwhip): a free game on a supplier, plant and big-box store: a 9-day rush at the store, one setting to change, ReliaSim runs 200 days and scores the on-time fill rate.
- [Beat the Bottleneck](https://reliasim.com/beat-the-bottleneck): a free game on the bottling-line model: fix one problem or add buffers, ReliaSim runs the line for 90 days, ten times, then compare Lean, TPM and TOC and see the Improvement Roadmap.