Demand Forecast Explanation

Overview

This skill translates complex demand forecasting model outputs into clear, actionable explanations for demand planners and supply chain stakeholders. It decomposes forecasts into constituent drivers—baseline demand, trend, seasonality, promotions, price elasticity, and external factors—and attributes forecast error to specific causes. The goal is to bridge the gap between data science outputs and planner decision-making, enabling informed consensus adjustments.

When to Use

• A demand planner asks why a forecast increased or decreased for a specific SKU/location
• Stakeholders need to understand what is driving demand for an upcoming planning cycle
• Forecast accuracy has degraded and leadership requires root-cause attribution
• During S&OP meetings when forecast assumptions need to be defended or challenged
• When comparing statistical forecast vs. consensus forecast to justify overrides
• Post-period forecast error review to improve future accuracy

Required Inputs