Supply chain

SQL · policy in one node · maps

Eight weeks of demand joined to stock on hand — in SQL, because that is what SQL is for — and then the policy: lead-time demand, safety stock, order multiples, days of cover. That split is the point. The query is a query; the thing a planner argues about is one node they can read.

Every line below its reorder point becomes a draft purchase order you can open, and the plan is a page by the time the planner sits down. The scorecard hands the model numbers it did not compute and asks for a recommendation against a schema.

How the graphs run

1. A join and a group-by in the database

   The policy in a node beside it.

2. A draft order raised per line

   As a record in your own ontology.

3. OTIF and defect rate computed by the graph

   The recommended action asked of a model.

4. Temperature excursions

   Plotted at the point where each one peaked.

On the canvas

sql:sqliteQuery · frame:* · ai:extract · views:map · entities:create

It ships with the product

This project ships with DjiniousWorkflow. One command seeds it, another runs every graph in it, and the captures here came out of those runs.

Where a graph leans on a fixture rather than a live system, it reads the fixture where your deployment would read a database — swap the node at the top and the rest of the graph does not change. The engine work — the fan-out, the gather, the approval, the publish — is the product’s own.