An optimizer tells you what your network should be. Supply chain simulation tells you whether that design survives variable demand, late suppliers and long lead times. SCModeling does both on one model, on your machine: design the network, optimize it, simulate it, and bring a decision you can defend.
Network optimization answers one class of question: given a fixed set of choices, what is the best combination? Which facilities open, which supplier serves which customer, how much flows on each lane. It returns the best decision under the model's assumptions.
Those assumptions are where the gaps sit. Optimization typically works from averaged annual or monthly demand and assumes capacity is fully usable. It has no time-series visibility: a "75% service rate" might mean steady at 75%, or 100% for 270 days and 0% for 90. A discrete-event simulation can tell the difference, because it runs the network forward through time.
Day-to-day demand variance, stochastic lead times, supplier reliability. The simulation runs the design against the variability the optimizer averaged away.
Fill rate and lead time come out of the run shipment by shipment, so a design that stocks out for a month shows up before you commit.
Inventory draws down and replenishes at every site over the run, so you see whether a DC drains faster than its supplier can refill it.
Simulation evaluates the design you give it; it does not search the alternatives for the best one. Ask a simulator "how many DCs do I need?" and it can only tell you how the configuration you typed in performs. That's why a well-run network-design project uses both: optimize to find the candidate design, then simulate to verify it survives realistic variance.
| If you're asking… | Use |
|---|---|
| "What's the optimal X?" (one answer, given constraints) | Optimization |
| "How many facilities do I need?" | Optimization |
| "Where should DCs go in a clean-slate analysis?" | Greenfield design |
| "What would happen if I tried X?" (states over time, with variance) | Simulation |
| "Will this policy deliver 95% service under real demand variance?" | Simulation |
| "How does safety stock behave over a month of stochastic demand?" | Simulation |
Optimization gives you the answer. Simulation tells you whether the answer survives reality.
Most tools stop at optimize. SCModeling's parts share one canonical model, edited in place: no import/export round-trips, no second copy to drift.
Network design questions come in two shapes, and they are different problems. The Network Sandbox has a working demo of each.
Greenfield answers "if we started over, where would we even start looking?" It places facilities where demand clusters, using distance as a proxy for cost.
Greenfield Design — US: the GreenfieldAnalysis engine sites 2 to 8 DCs from scratch for 189 US demand points. Step the count and the sandbox marks the elbow where diminishing returns start.
Open Greenfield Design — US →Brownfield design is subset selection: a fixed set of existing or candidate sites, some pinned open, and a decision about which of the rest to keep. No new locations are sited.
Brownfield Design — EU: 142 demand points across France, Germany and Luxembourg and 10 candidate DCs. Click a DC on the map to flip it from pinned to optional, set a target count, and the sandbox keeps every pinned DC while choosing which optionals stay open. The graph shows the gap between your pinned curve and the all-optional curve: what your pins cost.
Open Brownfield Design — EU →An honest caveat on both: they optimize demand-weighted distance, not landed cost, and ignore inbound supply, operating costs and capacity. They are a starting argument, not a final answer. Once fixed costs, capacity and freight matter, the question becomes facility-selection optimization, and that optimized design is what you then simulate.
The sandbox runs in your browser on sample models. Each card is tagged by what it exercises: STRUCT (network shape), DESIGN (siting), OPT (optimizer output) or SIM (the simulation engine).
Supplier → Plant → Customer, single SKU assembled from raw material. The engine smoke model.
Nashville plant → 12 regional DCs → 36 retail customer zones, randomized daily demand, 30-day run.
2 suppliers → plant → Kansas City hub → four corner DCs. The hub fans out to each spoke.
189 demand points; the engine sites 2–8 DCs from scratch.
142 demand points, 10 candidate DCs, pinned vs. optional subset selection.
Offshore via Port LA vs. a Mexico reshore. Toggle the tariff and watch sourcing flip.
Sign a US co-pack contract or not, and at what fixed cost, across the diesel range.
Where in a 3-tier network to hold safety stock, and how much, at a 95% Type-1 service level.
Suppliers → 2 plants → 2 DCs → 3 regions.
One plant, candidate DCs, customers spread out.
Blank canvas: add nodes, draw lanes, build your own shape.
A tariff is a cost coefficient on a sourcing or lane arc; the optimizer weighs duties as one input among many. What's hard is proving the redesign survives reality. Re-optimize under the new rates, then simulate the redesign on the same model before any capital moves.
Read the tariff redesign brief →SCModeling runs on the modeler's Windows or macOS machine. Models are files on disk that you control. There is no SCModeling cloud for the product to call and no telemetry of model contents.
Deployment & IT details →SCModeling is built for strategic network questions: what the footprint should be, and whether it holds up. For other questions, look elsewhere.
Supply chain simulation runs a network forward through time: orders arrive, inventory draws down and replenishes, shipments move, lead times vary. SCModeling uses discrete-event simulation, so service, inventory and lead time evolve day by day instead of collapsing into one averaged number.
Optimization searches the decisions (which facilities open, which supplier serves which customer, what flows on each lane) and returns the best combination under its assumptions. Simulation runs one design with variability over time. Optimization gives you the answer; simulation tells you whether it survives reality.
For a network redesign, yes. Optimization works from averaged demand and fully usable capacity, so it can't tell a steady 75% service rate from 100% for 270 days and 0% for 90. Optimize to find the candidate design, then simulate it. SCModeling does both on one model.
Greenfield sites facilities from scratch, starting from where demand is. Brownfield starts from the sites you have and decides which subset to keep open. The sandbox's Brownfield Design — EU demo lets you pin DCs that must stay open, mark others optional and set a target count; no new locations are sited.
No. SCModeling is a self-contained desktop application for Windows and macOS. Models are files on the modeler's machine, there is no SCModeling cloud for the product to call, and no telemetry of model contents. The public sandbox runs sample data only.
Yes. The Network Sandbox runs in the browser on sample models. The Simple Supply Chain, Cookie Production and Hub and Spoke demos show real engine output: fill rate, average lead time, inventory series and a replay of simulated shipments.
Yes. A tariff is a cost coefficient on a sourcing or lane arc. Re-optimize sourcing and footprint under the new duty rates, then simulate the redesign on the same model. The sandbox's Tariff: Offshore vs. Reshore demo compares offshore via Port LA with a Mexico reshore.
An annual desktop license priced per modeler, with multi-product and team discounts. Quotes are tailored to the number of modelers, product mix, and pilot versus production deployment.
Open Cookie Production: one plant, 12 regional DCs, 36 customer zones and randomized daily demand on the real simulation engine. Sample data only. Ready to talk about your own network? Request a walkthrough.