The apparel supply chain uniquely faces the challenge of combining characteristics of make-to-stock manufacturing with consumer-driven fashion volatility. While an apparel maker may have a base demand that is amenable to cost-saving lean manufacturing, its fashion-driven business requires enhanced agility to achieve needed service levels in the face of short life cycles, high volatility, low predictability and high impulse purchasing.
Proportional profiles (also called distributions or “size curves”) that parse an item’s demand forecast by attributes such as color, size and more, are crucial to creating accurate demand plans at the SKU and sub-SKU level for new products, over the complete life cycle, and within different seasons in the apparel supply chain. Improving demand planning at the SKU and sub-SKU level can mean the difference between success and failure of a new product introduction or an entire season.
Creating accurate distributions has traditionally been a cumbersome manual task, fraught with uncertainty and prone to human error. However, recent advances in demand planning technology allow planners to go far beyond manual generation of size curves based largely on intuition and incomplete data.
Planners can now 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 making up the apparel supply chain.
This white paper discusses how proportional profiles (also called distributions or “size curves”) are used to parse an item’s demand forecast down to the attribute level (by color, size, fabric, and more) to help develop a good attribute-level supply chain forecast; and how 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.