Looking for worked examples? The case studies cover real lines with real numbers, including a bottling line that found $1.5M, an olive plant that found 15%, and a soap plant that avoided a $4M retrofit.
Why Simulation Belongs in the Capital Plan
Capital gets one shot. It’s the stops that drive your output, and the loss report ranks them wrong. How a playing field shows what a change returns before you spend.
Read the guide → For leadersHow a ReliaSim Project Runs
From the first question to the decision: what you bring, what happens, what you learn and what you get at each step.
See the steps → OEE · CalculatorLine OEE vs Machine OEE
How to calculate OEE, why a line is never the average of its machines, and a calculator for up to six machines in series.
Open the guide and calculator → ThroughputHow Big Should a Buffer Be?
Lean says inventory is waste. Theory of Constraints says protect the constraint. Here’s why the argument never settles, and how to settle it for your line.
Read the guide → ComparisonSpreadsheet, Dashboard, AI or Simulation?
A loss tree averages away interactions, a dashboard only reports what already happened, and a model trained on history can’t answer for a line that doesn’t exist yet. Here’s what each tool can tell you.
Read the guide → CapacityThe Hidden Factory: Asset Efficiency vs OEE
Capacity you already own that OEE can’t see, because it leaves unscheduled time out. Also why Asset Efficiency and OEE can agree within 1% on a 24/7 line and differ by 30 points on one shift.
Read the guide → PrioritizationWhich Loss to Fix First
Ranking by minutes lost ranks occurrence, not cost. A stoppage on a buffered machine costs nothing. Rank by what comes back instead.
Read the guide → PrioritizationLean, TPM or TOC: Which Fix First?
Three schools, three different first fixes for the same bottling line. A validated model scores all three, and none finds the best move.
Read the guide → PrioritizationThe Improvement Roadmap: Which Fixes, in What Order
Engine runs on a model find the order. With no capital, seven fixes take our demo line from 54% to 85%; spend capital on one buffer, 100 cases before the Palletizer, and five steps do it. Of 2,000 random plans, none gets there.
Read the guide → BenchmarksIs 85% OEE Achievable on Your Line?
It’s a single-machine benchmark. Five world-class machines in series with no buffering give a line OEE near 44%.
Read the guide → DiagnosticsFind the Bottleneck: Starved vs Blocked
The busiest-looking machine is rarely the constraint. Read starved and blocked time, and the buffers beside each stop, to find the step that actually limits output.
Read the guide → OEEOEE Improvement Tradeoffs
Speed against stops, downtime against buffers, machine OEE against line output. Loss/gain analysis and buffer tradeoff price each fix before you spend.
Read the guide → MethodTest a Line Change Across Every Product
A packing, staffing or equipment change has to work for every product a line makes. One food plant ran about 30,000 simulations, every line in every configuration for every product, from the command line.
Read the guide → ConstraintsTheory of Constraints Simulation
The five focusing steps assume you can find the constraint. On a real line it moves with downtime, mix and schedule. This guide shows how to find it, buffer it and see where it goes next.
Read the guide → ThroughputCapacity Without Capital
What to change before you buy: the failure mode that gives most back, where storage goes, and a schedule that stops fighting the plant. Engine-run figures from the demo lines.
Read the guide → TrainingA Flight Simulator for Your Plant
Let your team practice on a playing field built from your own line: what to fix first, where storage goes, the shift ahead. A game on your own stop data.
Read the guide → ReliabilityReliability Block Diagram vs Simulation
An RBD gives availability, not throughput. This covers why a series diagram misstates a line with storage between machines, and where RBDs still belong.
Read the guide → ReliabilityRAM Analysis
Reliability, availability and maintainability, defined. What a RAM study answers, how established RAM tools approach it, and where discrete rate simulation fits on high-speed lines.
Read the guide → CapacityThroughput vs Capacity
Rated speed never shows up on the report. This covers where capacity goes before it becomes output, and why only the constraint sets throughput.
Read the guide → MethodAn LLM Will Answer Your Question. That’s the Problem.
A chatbot will tell you whether a second palletizer gets you to volume. It answers fluently, with a number, and with no model of your line. That’s worse than being wrong, and this guide explains why.
Read the guide → LossesWhy Predictive Maintenance Can’t Answer What-If
Condition monitoring forecasts when a machine fails, from the plant you already have. It can’t say what a change returns.
Read the guide → LossesMicro-Stops vs Breakdowns
The recorded loss is the same, but the recovery isn’t. Frequent short stops can cost more throughput than rare long ones, and they hide in performance loss.
Read the guide → LeanValue Stream Map Simulation
A static map of averages has no clock, so it hides the moving constraint and what buffers are doing. Here’s how to turn a current-state VSM into a model you can validate.
Read the guide →Reliability statistics
Turning stop logs into the time-to-failure and time-to-repair distributions a line model runs on. Fitting them from line event data is the job of ReliaStats®.
Downtime Data Analysis for Production Lines
From a stop log to failure-mode distributions a simulation can run: event data, cause groups, TTF and TTR, fitting and validation.
Read the guide → DowntimeDowntime Pareto: Frequency, Duration and Lost Throughput
Rank stops by count, by minutes or by production recovered. Why the three charts disagree and how to get from a Pareto to root cause.
Read the guide → ReliabilityMTBF vs MTTR: Outputs, Not Inputs
MTBF and MTTR summarize what happened. A model needs TTF and TTR distributions per failure mode, and MTBF, MTTR and OEE come out of the run.
Read the guide → ReliabilityWeibull Analysis of Downtime Data
What the shape and scale parameters mean, why to fit each failure mode separately, goodness of fit, and how fits feed a line simulation.
Read the guide → ReliabilityAvailability vs Reliability
The share of time up versus the chance of running without a stop. Inherent vs operational availability, and why buffers change which one matters.
Read the guide →Supply chain
Network design above the plant: where facilities go, how product is sourced, and whether the design holds up once demand and lead times vary. The demos run in the Networks tier of the Sandbox, and each has its own page in the free supply chain simulator index.
What Is Supply Chain Simulation?
An optimizer picks the network. A simulation checks whether it survives variable demand, late suppliers and long lead times. Why a redesign needs both, plus greenfield vs brownfield design.
Read the guide → TariffsTariff Network Redesign
Treat the tariff as one more cost in the network model, re-optimize sourcing and footprint, then simulate the redesign on the same model before you move a plant.
Read the guide → SimulationHow Supply Chain Simulation Works
The event list, ordering policy and inventory position behind a discrete-event network model, what a run shows, and the limits of the demos.
Read the guide → OptimizationSupply Chain Optimization vs Simulation
Decision variables, a landed-cost objective and constraints: the mixed-integer model behind network design, its classic problem types and where it stops.
Read the guide → Network designGreenfield Network Design
Where would the DCs go if you started over? The siting math, location-allocation for several sites, and how greenfield differs from brownfield and footprint rationalization.
Read the guide → ValidationHow to Know Your Supply Chain Model Is Right
Reproduce a period you lived through before trusting any scenario: what to compare, why the parts matter more than the total, and holding out a period.
Read the guide → Siting methodCenter of Gravity Analysis
The balance point of your demand, in plain terms: how to run one, a worked example, what it leaves out, and how to test the answer.
Read the guide → Network designHow Many Distribution Centers Do You Need?
Distance falls with each added DC while fixed cost and inventory rise. Reading the elbow, the square root law, and testing the count.
Read the guide → InventoryThe Bullwhip Effect
Why a small rush at the shelf becomes a big wave at the plant: the four causes, the bullwhip ratio, fill rate vs on-time fill, and why common fixes backfire.
Read the guide → InventorySafety Stock Simulation
Where the safety stock formula stops: its assumptions, pooling at a central DC against holding stock near customers, and testing a policy over time.
Read the guide → Network DesignDistribution Network Design
How many DCs, where, which customers each serves and where stock sits. Redesign the network you have, then watch it run.
Read the guide → InventoryMulti-Echelon Inventory
Set stock across plant, DC and regional tiers together. Why site-by-site targets over-stock, and how simulation tests the result.
Read the guide → ReferenceSupply Chain Glossary
Short definitions of network design, inventory and simulation terms, each linked to the guide that goes into it.
Read the guide →Simulation methods
How the main simulation methods differ, and where discrete rate simulation, the method ReliaSim is built on, fits among them. The demos run in the Discrete Rate tier of the Sandbox.
What Is Discrete Rate Simulation?
Flow modeled as rates between machines and buffers, with work done only when a rate changes. The three primitives (constraints, buffers and interrupts), where the method fits and where it came from.
Read the overview → ComparisonDiscrete Rate vs Discrete Event Simulation
What counts as an event in each, where discrete event struggles on high-speed lines, how to choose, and which tools use which method.
Read the guide → HistoryHistory of Discrete Rate Simulation
Created by Andrew Siprelle in 1990 as bulk flow simulation: the 1995 paper, the rename in the late 2000s, and three decades of published work.
Read the guide → HistoryThe Lineage of Discrete Rate Simulation
Five decades from Pritsker’s SLAM to today: what each era contributed, what carried forward, and the full bibliography.
Read the guide → MigrationMigrating a Simulation Model
Move a model from ExtendSim or Plant Simulation: its data into ReliaDB, the layout rebuilt, and parity proved against the old model and the plant.
Read the guide → MethodsWhich Simulation Method Fits?
Discrete event, discrete rate, continuous, agent-based, system dynamics and Monte Carlo, compared by what each treats as state and how its clock moves.
Read the guide → ComparisonMonte Carlo vs Discrete Rate Simulation
A Monte Carlo spreadsheet has no clock. When that’s fine, and when buffers, blocking and the length of each stop mean you need a dynamic model.
Read the guide → DefinitionsDigital Twin vs Simulation
The difference is the data connection and whether the model is validated, not how realistic it looks. Digital model, shadow and twin, and when a live feed is worth it.
Read the guide →Looking for product reference instead, like menus, file formats and node parameters? You’ll find it in the ReliaSim documentation.
See the ideas on a real line model
Every guide above describes something a line model can show. The Sandbox’s four tiers cover flow models, fitting stop data, a real bottling line and supply-chain networks, each with saved engine results.
Open the Sandbox → Read the case study