Business problem
Customer Service & Fulfillment
Whether the order arrived complete, on the date you committed to.
OTIF has no single definition, and the spread between the defensible ones is wide enough to change what a supply chain review concludes. Order grain or line grain, measured against the original commitment or the current one, on the ship date or the delivery date — each pairing produces a different, honest number. The definitions here publish every switch and the denominator each figure was computed over; perfect order gets the same treatment, published as the AND across its four components beside the product of their rates, which is the answer that looks right and is not. Lead time is here too, read as a distribution rather than an average, because a promise date set from a mean is wrong about half the time by construction.
Metric definitions
Computable definitions from the KPI Dictionary — every decision switch made explicit, with the formula and the source tables in each ERP.
Dashboard patterns
A wireframe paired with the star schema underneath it: fact grain, measures with their additivity rules, and the source tables per reference.
Reference builds
Working implementations of the definitions and patterns above, running over frozen, simulated data. Not a resource and not a case study — no client data, and no client named.
Simulated data, frozen snapshot — not a live client system.
In the references
Where this data lives in each source system. A way into the ERP references, not an index of them — every reference landing lists all of its modules.
The other business problems
- Inventory & Working Capital — What you're holding, why it's there, and what it costs to hold it.
- Demand Planning & Forecasting — How wrong the forecast is, in which direction, and where the error is worth fixing.
- Procurement & Supply — What's on order, when it lands, and how the supplier actually performs.
- Manufacturing & Production — Whether the plant built what the plan said it would build.
- Data Foundation & Governance — Landing ERP data in a lakehouse without losing deletes, history, or control of who reads it.