Business problem
Demand Planning & Forecasting
How wrong the forecast is, in which direction, and where the error is worth fixing.
Forecast error is easy to compute and easy to compute wrongly. The number moves with the grain it is measured at, the lag it is measured from, and whether errors are weighted by volume before they are averaged — so two teams can report the same forecast as accurate and as poor without either being mistaken. The definitions here make each of those switches explicit and name the house convention; the monthly-cycle and plan-vs-actual patterns carry the same discipline into the review meeting, where the harder question is whose plan the actual is being scored against. The reference builds run the full chain over simulated data — from the forecast override that makes accuracy worse, through the cycle review that locks a plan, to the planner-grade instrument that splits a miss into volume, mix, price and timing.
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.
- Customer Service & Fulfillment — Whether the order arrived complete, on the date you committed to.
- 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.