Retail Forecasting — The Value of the Crystal Ball

Executive Snapshot

• Figure: Executive Snapshot: Retail Forecasting — The Value of the Crystal Ball

Situation Overview

• The Core of Demand Forecasting Is Predicting the Future

• Figure: Demand Forecasting Drives Retail Functions

• Modeling Demand Is Fitting Data to a Mathematical Equation

• Table: Common Demand Forecasting Models

• Modeling Granularity and Depth Varies Across Scenarios

• Time Horizons Also Vary and Influence Forecast Output

• Empirical Models — Time Series and Regression

• Figure: Forecast Example: Exponential Smoothing, January 2021–December 2023

• Survey-Based Models Look Ahead

• Demand Sensing

• Demand Shaping

• Value of the Crystal Ball

• Matching Supply and Demand

• Excess Inventory

• Figure: Inventory Impact

• Scarce Inventory

• Holistic Multifunctional Advantages

• Figure: ROI of Merchandising

• Challenges

• Promotional Forecasting

• New Product Introductions

• End of Life/Delisting

• Data

• Efficiency

• Accuracy

• Figure: MAPE, FVA Error Formulas

• Table: Benchmarks in Accuracy (%)

• Predictability

• Explainability

• Outcomes

• Future Vision for Retail Forecasting

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• Related Research

• Synopsis