Demand Forecast Review Skill

Every unit of forecast becomes purchase orders, capacity commitments, and inventory. This skill interrogates a forecast the way a supply planner must: separate the defensible baseline from hopeful uplift, confront the forecast with its own accuracy history, hunt for hockey sticks, and register every assumption so that when the number misses, you know which belief broke.

What This Skill Produces

• A baseline vs. uplift decomposition (statistical base + named uplift layers)
• Forecast accuracy history: MAPE and bias, with what they imply for buffering
• Hockey-stick and pattern-anomaly flags
• An assumption register with owner, evidence strength, and expiry
• Consensus vs. statistical divergence flags with a burden-of-proof call
• An overall credibility verdict: plan to it / plan to it with buffers / send back

Required Inputs

Ask for these if not provided:

• The forecast — by product/family and period, over the horizon under review
• History — actuals for the trailing 12+ months; prior forecasts vs. actuals if available (for MAPE/bias)
• Uplift drivers — promotions, launches, new customers, pipeline deals baked into the number
• Who built it — statistical, sales-driven, consensus; and what changed since last cycle
• Decision at stake — what the forecast will commit (buy, build, capacity) and its lead time

With no accuracy history, review structure and assumptions and state plainly: [accuracy unknown — treat forecast as unvalidated]. Never present conclusions as if history existed.

Interrogation Framework

1. Decompose baseline vs. uplift. Baseline = what history alone supports (trend + seasonality). Everything above it is an uplift layer that must be named: which promotion, which customer, which launch. Compute uplift share of total — above ~30% uplift, the forecast is a sales plan wearing a forecast's clothes, and each layer needs its own evidence.

2. Confront accuracy history.

Metric