Demand Planning for Fashion and Apparel

Fashion demand planning, a specialist branch of demand planning and inventory optimization, is the work of buying and allocating products with short selling windows, long lead times and demand that depends on taste, weather and timing. A garment designed in one season is often sourced and committed months before the first customer sees it, and by the end of the season whatever remains is worth much less. The planner has to decide how much to commit early, how much to keep flexible, how to split the buy across sizes, colors and locations, and when to cut prices. This guide covers the structure of the fashion buying calendar, the commit-and-chase approach, size and color curves, sell-through tracking, markdown economics, returns in online fashion and the measures to watch. It includes worked examples with checked arithmetic. It is part of the series that includes luxury goods, leads to food and beverage and ecommerce, and connects to open-to-buy planning, markdown planning and cash flow management.

The shape of the problem

Fashion has three features that drive the planning approach. Lifecycles are short, from a few weeks for trend items to a season for most lines, so there is little time to learn and react. Lead times are long, often months from design approval to delivery, so the first commitments are made on judgment and limited evidence. And the assortment is wide, with each style available in multiple sizes and colors, so volumes per item are small and the forecast at that level is uncertain. The result is that forecast errors at item level are large, and the cost of being wrong is asymmetric: too little means missed sales on the best sellers, and too much means markdowns on the rest.

Commit and chase

A common response is to split the buy. A base commitment is made at the start for the portion of the season that the planner is most confident about. A flexible portion is held back, made available through short lead time sourcing, local production or capacity reservations with suppliers, and is released in season against what the early sales show. Consider a style with a total expected season demand of 2,000 units (illustrative). The planner commits 60 percent up front, or 1,200 units, and keeps 800 units of flexible capacity. After four weeks, sell-through is running well ahead of plan, and the planner releases 500 units of the flexible capacity. The final buy is 1,700 units. If the style had flopped, the planner would have released nothing, and the exposure would have been limited to 1,200 units. Check: 0.60 × 2,000 = 1,200, and 1,200 + 500 = 1,700. The cost of flexibility is usually a higher unit price or less choice of fabrics. The benefit is less excess on losers and fewer stockouts on winners. The right split depends on the cost of the premium for flexibility compared with the expected markdown on unsold units. See open-to-buy planning.

Size curves

Demand for sizes is not even. A size curve describes the share of the buy that should go to each size, based on historical sales for similar styles, adjusted for the target customer, the market and the channel. A style with a total buy of 1,000 units might follow a curve of 15 percent in the smallest size, 30 percent in each of two middle sizes, 15 percent in the next and 10 percent in the largest, which adds to 100 percent. The units are then 150, 300, 300, 150 and 100, a total of 1,000. If the size curve is wrong, the middle sizes sell out early while the extremes remain. A common cause of markdowns is broken size runs, where only the sizes that fewer customers need are left. Update size curves by channel and region, because the mix differs, and review them each season. Consider the relation between sizes in stores and online, where returns in multiple sizes can distort demand.

Color and fabrication

Color adds another dimension. A color that looks promising at design stage may be less popular in the market, and colors may sell differently by region and by season. The same approach applies: use history from analogs, make the first commitments in proportions that reflect confidence, and keep a flexible portion for colors that prove strong. Where production allows, delay dyeing or finishing so that the commitment to a specific color is made later. This is a form of postponement and can reduce risk.

Tracking sell-through

Sell-through is the share of units received that have been sold. Compare actual sell-through to a plan curve by week, and review the gaps. Suppose a style is planned to reach 25 percent sell-through by week four, and by week four it has reached 35 percent. It is selling at 1.4 times the planned pace (35 / 25 = 1.4). That is a signal to consider chasing additional units, provided there is time and capacity. A style at 15 percent after four weeks is at 0.6 of plan, and the planner should consider early action, such as redirecting stock to better channels, adjusting marketing, or starting a markdown plan. The earlier the signal is acted on, the more options are available. See markdown planning.

The economics of markdowns

Markdown is part of the plan in fashion, not an exception. Consider a style with 1,000 units, a cost of $20 and a full price of $60 (illustrative). The plan is for 650 units at full price, 200 units at 25 percent off ($45) and 150 units at 50 percent off ($30). Revenue is 650 × 60 + 200 × 45 + 150 × 30 = 39,000 + 9,000 + 4,500 = $52,500. The cost is 1,000 × 20 = $20,000, so gross margin is $32,500, or 61.9 percent of revenue. Check: 650 + 200 + 150 = 1,000 units, and 32,500 / 52,500 = 0.619. Now suppose the full-price sell-through is only 550 units, with 250 units at $45 and 200 at $30. Revenue is 33,000 + 11,250 + 6,000 = $50,250, and gross margin is 30,250, or 60.2 percent. The 100-unit shift from full price to markdown costs $2,250 of revenue and the same in margin. In a business with many styles, small shifts in full-price sell-through have a large effect on profit, which is why initial buys and early reads matter so much.

Returns in online fashion

Online fashion has high return rates, often driven by fit and size uncertainty, and customers sometimes order several sizes and return what does not fit. Returned goods need to be inspected, repackaged and put back into stock, which takes time and cost, and some cannot be resold at full price. Forecast gross demand, then apply expected return rates by category and size to derive net demand and the volume of returns coming back. Include the time lag: returns arrive days or weeks after the order and affect availability. Reduce returns with better size guidance, fit data and accurate images, and measure the return rate by style and size to improve the buy. See how to forecast ecommerce sales.

Replenishment of core and basics

Not all fashion is seasonal. Basics and core items, such as plain T-shirts or classic denim, sell year-round and can be managed with replenishment methods: forecast by item and location, set safety stock according to variability and lead times, and order regularly. Separate the core range from the fashion range in the plan. Applying fashion rules to basics creates unnecessary stockouts, and applying replenishment rules to fashion items creates excess. See safety stock formula.

Allocation across channels and stores

With small volumes per style, the allocation to stores and channels matters. Hold part of the stock centrally and allocate in waves based on early sell-through. Avoid sending a full size run to every store, as that leaves slow stores with unsold stock and fast stores short. Use the data from stores that read demand early, such as flagship stores or the online channel, to guide allocation elsewhere. Define rules to transfer stock when imbalances appear, and weigh the transfer cost against the margin at stake.

Season transitions and carryover

At the end of a season, decide which items are carried over, which are cleared and which are retired. Carry over only items with a classic or neutral character, and avoid carrying items that will look dated. Plan clearance early and use channels that protect the brand. Learn from the season by reviewing the buy against actual sales by style, size and color. The review is the main source of improvement for the next buy. See inventory write-downs.

What to measure

Useful measures include sell-through by week against plan, full-price sell-through, markdown rate, gross margin return on inventory, inventory age, size-run completeness, forecast accuracy by category, in-stock rate on core items and return rate. Review in a weekly trading meeting during the season and a monthly planning meeting. See in-stock rate.

Common mistakes

Committing the entire buy early removes the ability to respond. Using one size curve for all channels creates imbalances. Waiting too long to act on a slow seller reduces the options for recovery. Treating returns as a customer service issue and not a planning issue leaves a gap in net demand. Applying seasonal rules to basics or the reverse causes stockouts or excess. Failing to review the season leaves the same errors to be repeated.

FAQs

What is fashion demand planning?

Planning the quantity, timing and allocation of apparel and accessories with short selling windows and long lead times, across sizes, colors and locations.

What does commit and chase mean?

Committing part of the buy early and keeping the rest flexible to release in season based on actual sales.

Why are size curves important?

Demand differs by size. A wrong curve leaves broken size runs that sell poorly and drive markdowns.

How do I track fashion sell-through?

Compare cumulative units sold to units received by week against a plan curve, and act early on gaps.

How do returns affect planning?

They reduce net demand and return stock after a delay, so plan net demand and the flow of returned goods.