How To Build Size Runs For Efficient Inventory And Fulfillment

Size Run

A set of apparel units across a range of sizes for a specific style and color. Building effective size runs requires combining sales data, demographic insights, and warehouse capabilities to produce pack mixes that reduce stockouts and handling costs.

Designing size runs is both analytical and practical. Buyers must translate demand signals into size ratios while fulfillment teams ensure those ratios can be received, stored, and picked efficiently. The process reduces return rates due to size mismatch and keeps replenishment predictable.

Data You Need

Start with accurate inputs:

• Historical sell-through: Sales by size for the same or similar styles over multiple seasons.
• Return rates: Returns by size to detect systematic fit issues.
• Store demographics: Local customer size distributions that may require store-specific runs.
• Online demand: Channel-specific size preferences; e-commerce often skews different than brick-and-mortar.

Steps To Build Size Runs

Follow a repeatable workflow:

• Analyze sales curves: Plot units sold by size to identify peaks and troughs. Convert raw counts into a ratio for a target pack size (e.g., 12-piece pack).
• Define target pack size: Choose a pack quantity that meets vendor MOQs, reduces shipping complexity, and suits store needs.
• Create the run: Allocate integers to each size to match the ratio (rounding while keeping the overall shape of the curve).
• Validate with stakeholders: Confirm runs with merchandising, store operations, and the DC to ensure feasibility and merchandising alignment.
• Document and publish: Put the run in your PO templates and EDI/ASN specifications so vendors and the warehouse receive the same instructions.

Warehouse And Fulfillment Best Practices

After defining runs, configure operations to support them:

• WMS cartonization rules: Program pick-to-carton and pack rules based on runs to automate carton contents and reduce manual decisions.
• Labeling clarity: Ensure cartons and inner packs show the SKU, color, and exact size counts to speed receiving and store allocation.
• Pick face organization: Store units to minimize travel when common runs and pack sizes are picked together.
• Cross-dock for speed: For store pre-packs, cross-dock pre-built runs to shipping lanes to minimize touchpoints.

Common Pitfalls And How To Avoid Them

Beware of these mistakes:

• Blind rounding: Rounding ratios without checking impact can create persistent oversupply in some sizes. Reassess runs after a few replenishment cycles.
• One-size-fits-all: Using the same run for all stores ignores local demand differences. Segment runs where data supports it.
• Poor communication: Failure to document runs on POs leads to vendor and DC mismatches. Use standard packing instructions and EDI fields.
• Ignoring returns: High return rates by size indicate fit problems that should trigger run adjustments or product changes.

Implementation Example

A midsize activewear brand analyzes six months of sales and finds a size curve approximating 1 S : 4 M : 5 L : 2 XL. They choose a 12-piece pack to meet vendor MOQ. Rounding yields 1 S, 4 M, 5 L, 2 XL. The DC programs its WMS to generate pick tickets and carton labels reflecting this run. Stores receiving pre-packs can merchandise immediately; online orders are fulfilled by breaking the run into singles for individual orders.

Quick Checklist For Ongoing Management

• Review quarterly: Recalculate runs every quarter or after significant sales shifts.
• Monitor KPIs: Track size-specific sell-through, stockouts, and markdowns to validate runs.
• Segment intelligently: Create regional or channel-specific runs when justified by data.
• Automate where possible: Use WMS/TMS integrations and EDI to enforce runs and reduce manual errors.

In short, the Size Run should be a data-driven, documented pack blueprint that informs purchasing, packing, and allocation. When built and maintained properly it reduces handling, improves inventory balance by size, and keeps both stores and online channels better stocked for customer fit preferences.