WMAPE calculation, forecast bias detection, tracking signal computation, consensus vs statistical forecast value-add, promo lift attribution, and ABC×XYZ segmentation — the full demand planning analyst SQL curriculum across 30 missions.
30 missions · 13 free + 17 Pro · Starter → Master
Read the briefing
A Slack message from your manager
Explore the schema
5 tables in a star schema
Write your query
Full SQL editor with autocomplete
Get expert feedback
Graduated hints, not just pass/fail
Learn why WMAPE (Σ|A−F|/ΣA) is the industry default — it weights by volume and avoids divide-by-zero on intermittent SKUs.
Spot the systematic over-forecast on Category C that hides inside an acceptable WMAPE — the pattern demand planners escalate to leadership.
Compute rolling tracking signals and flag SKUs where |TS| > 4 — the statistical trigger for model recalibration.
Measure whether the human consensus override actually beats the statistical baseline — it only does 60% of the time in this dataset.
Each mission is a real request from someone at the company. Difficulty increases as you go.
A demand planning star schema: 30 SKUs (4 intermittent, 2 NPI with no history), 5 locations, 4 forecast versions (STATISTICAL/CONSENSUS/OVERRIDE/NAÏVE), 480 forecast rows, 120 actuals. Planted data-quality issues: STATISTICAL version over-forecasts Category C by ~18% (bias pattern); CONSENSUS beats STATISTICAL on only 60% of periods; 2 promos with negative lift (pull-forward effect); 4 SKUs with zero-actual periods that destroy MAPE.
●dimension tables ● fact tables
WMAPE, bias, tracking signals, consensus value-add — the queries that determine which models to trust.
Looking for something different?