Comparing Forecasting Approaches for Fashion Retail

After a decade in merchandising analytics, I've watched forecasting methodologies evolve from spreadsheet-based gut checks to sophisticated AI systems. The question I hear most often: "Is AI really better, or is it just hype?" The honest answer? It depends on your context, data maturity, and what you're trying to optimize.

Understanding the tradeoffs between traditional statistical forecasting and AI-Driven Demand Forecasting is critical for fashion retailers evaluating their next investment. Let's break down the approaches, examining real-world performance across the metrics that matter: forecast accuracy, implementation complexity, adaptability to trend shifts, and impact on key merchandising KPIs like sell-through rates and GMROI.

Traditional Statistical Forecasting: The Proven Baseline

What it is : Methods like moving averages, exponential smoothing, and ARIMA (AutoRegressive Integrated Moving Average) that use historical sales patterns to project…