Categories › Business & Finance › E-commerce & marketplaces
Supply-chain network design via simulation, optimization, and greenfield analysis.
Remote server: your requests and data go to scmodeling.com. Claude Code: claude mcp add --transport http scmodeling https://scmodeling.com/mcp · Claude app: Settings → Connectors → Add custom connector.
The tools this server listed when we connected to it, in its own words.
list_models — List the sample supply chain models run_simulation has a saved engine run for, with each model's id and a short description.run_simulation — Return the saved SC Builder engine run of a sample supply chain model: metrics, orders, shipments, routing and inventory series, verbatim. Tget_sc_theory — Reference text from the SC Builder engine: event-driven model, ordering policies (pull, push, planned), the forecast-demand and order-point explain_simulation — Primer on supply chain simulation: when to simulate, optimize or both, how a discrete-event network model works, which numbers the engine reexplain_optimization — Primer on supply chain network optimization: mixed-integer programming, decision variables and constraints, landed-cost objectives, the commexplain_greenfield — Primer on greenfield analysis: center-of-gravity (Weber) siting, Weiszfeld's algorithm, location-allocation for N facilities, service constrlist_opt_demos — List the saved optimization demos (Tariff sourcing, Coffee co-pack siting, Safety Stock Optimization) with an id and summary each. Use befordescribe_opt_demo — Detail on one optimization demo: what it shows, its source, and every saved scenario key. Use before get_opt_result.get_opt_result — Return the saved optimization result for one scenario of a demo, verbatim: sourcing, production and transport decisions, costs, and facilitylist_greenfield_demos — List the saved greenfield siting demos with an id and summary each. Use before describe_greenfield_demo or get_greenfield_result.describe_greenfield_demo — Detail on one greenfield demo: region, customer count, the saved DC counts and the score curve. Use before get_greenfield_result.get_greenfield_result — Return the saved greenfield result for one DC count, verbatim: the sited DCs (lat/lon, nearest city) and which customers each serves. Quote Not tested by KitScout yet. When our harness runs it, pros, cons and the tasks we tried appear here, with the date and evidence.