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Node reference

Every node the canvas can place, grouped the way the palette groups them. A page says when to use the node, what it needs wired in, and what each parameter means.

Data

Transform

  • Aggregate — Aggregate data with grouping and calculations
  • Concatenate — Stack rows from two datasets into one (UNION ALL)
  • Filter — Filter rows based on conditions
  • Join — Join two datasets on matching keys
  • Script — Clean or reshape raw data with Python

Map Data

  • Map Bill of Materials — Map columns to BoM schema (recipe: output product + components consumed)
  • Map Demand History — Map columns to demand-history schema (dated quantities — the forecast's training series)
  • Map Demand Policy — Map columns to demand-policy schema (requested quantity, unit revenue — the rules)
  • Map Demand State — Map columns to demand-state schema (fulfilled quantity, realized revenue — the outcome)
  • Map Handling Policy — Map columns to handling-policy schema (unit handling cost — the rule)
  • Map Handling State — Map columns to handling-state schema (realized handling cost — the outcome)
  • Map Inventory Policy — Map columns to inventory-policy schema (per-unit carrying cost — the rule)
  • Map Inventory State — Map columns to inventory-state schema (average inventory, realized carrying cost — the outcome)
  • Map Lanes — Map columns to lane schema (possible transport connections for optimization)
  • Map Location Policy — Map site policy for single-period network design
  • Map Location State — Map site state for single-period network design
  • Map Locations — Map columns to location schema (warehouses, customers, suppliers)
  • Map Production Policy — Map columns to production-policy schema (unit production cost + capacity — the rule)
  • Map Production State — Map columns to production-state schema (produced quantity, realized cost — the outcome)
  • Map Supply Policy — Map columns to supply-policy schema (capacity, unit cost — the rules)
  • Map Supply State — Map columns to supply-state schema (used quantity, realized cost — the outcome)
  • Map Transport — Map columns to transport schema (shipments, flows)

Scenario

Analyze

  • Center of Gravity — Find optimal warehouse/distribution center locations
  • Cost to Serve — Allocate supply, transport, and inventory costs to demand points
  • Scenario Script — Modify scenario data with Python and return a scenario
  • Scenario to Table — Compute rows from scenario data with Python for processing or export
  • XYZ Segmentation — Classify items by demand variability (XYZ) from demand history alone — no defaults, every threshold and bucket is stated

Optimize

  • Add Constraint — Add a business rule or constraint to the optimization model
  • Consolidate Shipments — Pack a scenario's shipment history onto the fewest transports that still fit every capacity dimension and the date window — proven optimal per lane, and back as a scenario to compare
  • Network Optimization — Optimize supply chain network flows, sourcing, and demand fulfillment
  • Set Objective — Change what the optimizer optimizes for