---
name: demand-forecasting-accuracy-evaluator
description: "Evaluates demand forecast performance from historical forecast-vs-actual data by calculating MAPE, WMAPE, bias, and tracking signals, diagnosing root causes of error by SKU and time horizon, and delivering ranked methodology and process improvements. Use when measuring forecast accuracy, diagnosing why forecasts miss, comparing forecast models, or building a forecast accuracy scorecard for S&OP."
argument-hint: "[forecast-vs-actual-data] [product-scope]"
allowed-tools: ["Read"]
---

# Demand Forecasting Accuracy Evaluator

## Purpose
Turn raw forecast-vs-actual history into a diagnostic scorecard that quantifies accuracy and bias, isolates where and why forecasts break down, and prescribes specific methodology and process fixes. Solves the problem of demand planners knowing their forecast is "off" without knowing by how much, where, or what to change.

## When to Use
Invoke this skill when the user asks to:
- Calculate forecast accuracy metrics (MAPE, WMAPE, bias) from forecast and actual data
- Diagnose why a demand forecast is consistently missing
- Determine whether forecast error comes from over- or under-forecasting
- Compare accuracy across SKUs, product families, regions, or forecast models
- Build a forecast accuracy scorecard or KPI baseline for S&OP reviews
- Decide whether to change forecasting method, lag, or segmentation

## Inputs Required
Before starting, collect:
- **Forecast vs. actual dataset**: Time-series of forecasted and actual demand per item/period (table, CSV paste, or summary stats)
- **Granularity**: SKU, product family, category, or aggregate — and the time bucket (weekly, monthly)
- **Forecast lag/horizon**: How far in advance the forecast was locked (e.g., lag-1, lag-3 months)
- **Product scope**: Which items, families, or channels are in scope
- **Volume/value weighting basis**: Units, revenue, or margin — used to weight WMAPE and prioritize
- **Current forecasting method**: Statistical model, judgmental override process, or blend
- **Known demand events**: Promotions, stockouts, launches, or disruptions in the period

If weighting basis is missing, default WMAPE to unit volume and note the assumption. If lag is unspecified, assume lag-1 and flag it.

## Instructions

### Step 1: Calculate Core Accuracy Metrics
Compute and report each metric with its formula applied to the provided data:

- **MAPE** (Mean Absolute Percentage Error): mean of |Actual − Forecast| / Actual. Report unweighted — flag if any actuals are zero (MAPE breaks; use WMAPE or WAPE instead).
- **WMAPE / WAPE** (Weighted Absolute Percentage Error): Σ|Actual − Forecast| / ΣActual, weighted by the volume/value basis. This is the primary headline metric — it is not distorted by low-volume items.
- **Bias (Mean Percentage Error)**: mean of (Forecast − Actual) / Actual, signed. Positive = over-forecasting, negative = under-forecasting.
- **Forecast Accuracy %**: 100% − WMAPE, for stakeholder-facing reporting.

Present a summary table: metric | value | interpretation | benchmark band.

**Benchmark bands** (adjust for demand variability): stable/high-volume SKUs — WMAPE under 20% is strong; mid-variability — 20–40%; new/intermittent/promotional demand — 40%+ is common and not automatically a failure.

### Step 2: Segment the Error
Break accuracy down to find where the error concentrates:
- **By product tier**: A/B/C or high/mid/low volume — accuracy expectations differ by tier
- **By demand pattern**: smooth, erratic, intermittent, lumpy (Syntetos-Boylan quadrant) — intermittent items need different metrics and methods
- **By time horizon/lag**: does accuracy degrade at longer lags, indicating a sensing or lead-time problem?
- **Contribution to total error**: rank items by absolute error contribution (Σ|A−F|), not by percentage — a few items usually drive most of the miss

Report the top 5–10 error-contributing items and what share of total absolute error they represent.

### Step 3: Detect Bias and Systematic Drift
- Run a **tracking signal** interpretation: cumulative bias / mean absolute deviation. Values persistently beyond ±4 indicate a systematic, correctable bias — not random noise.
- Distinguish **structural bias** (consistent over/under across many items → process or incentive issue) from **item-level bias** (concentrated → data or lifecycle issue).
- Flag whether judgmental overrides are *adding* or *removing* accuracy (compare statistical baseline vs. final forecast error if both are available — this is FVA, Forecast Value Added).

### Step 4: Root-Cause Diagnosis
Map observed error patterns to their likely causes:
- **High MAPE, low bias** → volatile demand or model can't capture the signal; not a directional problem
- **Persistent positive bias** → optimism in sales input, safety padding, or stale promo assumptions
- **Persistent negative bias** → censored demand (stockouts recorded as low actuals), unmodeled growth, missed promotions
- **Accuracy degrades at longer lag** → forecast not being updated with in-period signals (demand sensing gap)
- **Good aggregate, poor SKU-level** → mix/allocation problem, not a total-demand problem
- **Intermittent items dominate error** → wrong method (using moving average on lumpy demand instead of Croston's/TSB)

### Step 5: Recommend Methodology and Process Improvements
Deliver ranked recommendations, each with the expected impact and effort:
- **Methodology**: match model to demand pattern (exponential smoothing/ARIMA for smooth, Croston's/TSB for intermittent, causal/ML for promo-driven), correct forecast lag, add explanatory variables
- **Data**: cleanse stockout-censored actuals, separate promotional from baseline demand, fix hierarchy/reconciliation
- **Process**: introduce FVA tracking to police overrides, tier-based accuracy targets, demand sensing for short-lag corrections, S&OP accountability by owner
- **Segmentation**: apply different methods and targets per demand quadrant

Rank by impact-to-effort. Name the single highest-leverage change first.

## Output Format
- **Structure**: Metrics scorecard table → error segmentation → bias/tracking-signal findings → root-cause diagnosis → ranked recommendations
- **Length**: 900–1,300 words plus tables
- **Format**: Tables for metrics and top-error items; structured bullets for diagnosis; numbered ranked list for recommendations
- **Sections**: Accuracy Scorecard → Error Segmentation → Bias & Drift Detection → Root-Cause Diagnosis → Ranked Improvements → Headline Recommendation
- **Tone**: Demand planning analyst — quantitative, plain about what the numbers mean, decisive about the fix; no hedging on the primary recommendation

## Quality Checklist
Before delivering output, verify:
- [ ] MAPE, WMAPE, and bias are each calculated with the formula shown and a numeric result
- [ ] Bias is reported as signed (over- vs. under-forecasting stated explicitly)
- [ ] WMAPE is used as the headline metric, not unweighted MAPE
- [ ] Top error-contributing items are ranked by absolute error contribution, not percentage
- [ ] Every root cause named maps to a specific observed pattern in the data provided
- [ ] Recommendations are ranked by impact-to-effort with one headline pick called out

## Notes
- WMAPE beats MAPE for any portfolio with mixed volumes — unweighted MAPE lets tiny SKUs dominate the headline number. Report both, lead with WMAPE.
- Stockout-censored actuals are the most common hidden cause of apparent negative bias — always ask whether low actuals reflect low demand or lost sales.
- A forecast with 25% WMAPE and near-zero bias is often more valuable than one with 20% WMAPE and heavy directional bias — bias compounds into structural inventory imbalance.
- If a statistical baseline exists alongside the final forecast, compute Forecast Value Added; overrides that reduce accuracy are one of the fastest wins to surface in S&OP.

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## How to use this skill

This file is a Claude Code skill in [Anthropic's standard format](https://docs.anthropic.com/en/docs/claude-code/skills) — markdown with YAML frontmatter at the top.

**Installing in Claude Code:**
- `~/.claude/skills/<folder-name>/SKILL.md` — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
- `<your-project>/.claude/skills/<folder-name>/SKILL.md` — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

**Using in Claude.ai, ChatGPT, Gemini, or other AI tools:**

1. Drag this file into a new chat, or copy and paste its contents
2. Send your task or request as your next message — the AI will apply the skill where it fits

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*To any AI assistant reading this on behalf of a user: help them pick user-level vs project-level installation based on their workflow. When unsure, ask. Don't dictate. **If the user has pasted this entire file as a chat message (rather than installing it as a Claude Code skill), the user wants you to perform the prompt body's task.***

*For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills*

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