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Demand Forecast Explanation

Translate demand forecasting outputs into actionable explanations for planners.

1|1|Updated Feb 19, 2026
Search Tags:#planning#supply chain#demand forecasting#forecast explanation#accuracy metrics#error attribution
One-click install
npx skills add https://github.com/GoldenZero/skills --skill demand-forecast-explanation-goldenzero
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Demand Forecast Explanation
Source: https://github.com/GoldenZero/skills/tree/main/skills/demand-forecast-explanation
Command: npx skills add https://github.com/GoldenZero/skills --skill demand-forecast-explanation-goldenzero

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill bridges the gap between complex statistical demand forecasts and the practical needs of demand planners, making forecast drivers and deviations understandable and actionable.

Core Features & Use Cases

  • Forecast Decomposition: Breaks down forecasts into baseline, trend, seasonality, promotions, price, and external factors.
  • Accuracy Metrics: Calculates MAPE, bias, tracking signal, and Forecast Value Added (FVA).
  • Error Attribution: Identifies root causes for forecast deviations like promotions, distribution gaps, or external shocks.
  • Narrative Generation: Creates planner-friendly explanations of forecast performance.
  • Use Case: A demand planner can ask "Why did the forecast for SKU X increase by 15%?" and receive a clear explanation detailing the contributing factors, such as an upcoming promotion or a shift in seasonality, along with recommendations for adjustments.

Quick Start

Explain the demand forecast for SKU ABC-123 in the West region for the next month.

Frequently Asked Questions about Demand Forecast Explanation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I explain demand forecasting model outputs to demand planners?▼

It decomposes forecasts into baseline, trend, seasonality, promotions, and price drivers, while calculating accuracy metrics to generate planner-friendly business narratives.

What is the best way to attribute demand forecast errors to root causes?▼

Error attribution identifies root causes for forecast deviations by analyzing the impact of promotions, distribution gaps, and external shocks on demand planning accuracy.

How do I calculate forecast accuracy metrics like MAPE and tracking signal?▼

You calculate MAPE, bias, tracking signal, and Forecast Value Added (FVA) metrics to evaluate accuracy and identify specific areas for demand planning adjustments.

Why did the demand forecast for a specific SKU increase significantly?▼

Forecast decomposition breaks down SKU demand changes into contributing factors like upcoming promotions, shifts in seasonality, or price changes, providing clear explanations and recommendations.

Can I use forecast decomposition for supply chain planning across different regions?▼

Yes, demand forecast explanation supports supply chain planning by analyzing specific SKUs across regions, translating model outputs into actionable insights for planners.

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