The apparel industry uniquely combines characteristics of make-to-stock manufacturing with consumer-driven fashion volatility.

Creating accurate demand plans down to the SKU and sub-SKU level can mean the difference between success and failure of a new product introduction or an entire season. Proportional profiles (also called distributions or “size curves”) parse an item’s demand forecast down to the attribute level (by color, size, fabric, and more). Good attribute-level supply chain forecasting is crucial to creating accurate demand planning.

Creating distributions has traditionally been a cumbersome manual task, based largely on intuition and incomplete data. The process is fraught with uncertainty and prone to human error. Recent advances in demand planning technology allow planners to go far beyond manual generation of size curves to automate the creation and management of proportional profiles spanning attributes such as gender, size, color, width, trim, fabric, channel, region, label/brand, and much more, for entire collections of apparel items.

This paper outlines the main components of proportional profile planning:
- Automates the generation of accurate profiles using existing sales history data.
- Handles any number of product attributes and tiers.
- Forecasts by attribute earlier in the planning cycle to streamline new product introductions.
- Adapts proportional profiles across seasons and life cycles to optimize overall profitability.
- Streamlines demand-to-supply translations to maximize market-driven response.