Size Run Sheet Template for Apparel Production
A size run sheet defines the quantity of each size to produce within a total order, and getting this distribution right is one of the most impactful decisions in apparel inventory management. Produce too many of a slow-selling size and you face markdowns and dead stock. Produce too few of a popular size and you lose sales and disappoint customers. Our size run sheet template provides a structured framework for calculating size ratios based on historical sales data, channel requirements, and target customer demographics. It includes worksheets for single-style size curves, collection-level size aggregation, and comparison against previous season sell-through performance. Whether you are a direct-to-consumer brand optimizing your e-commerce inventory or a wholesale brand building pre-packs for retail accounts, this template helps you allocate production quantities with data-driven precision.
Understanding Size Curves and Their Impact
A size curve is the distribution of demand across a size range, expressed as a percentage of the total quantity. For example, a standard women's size curve for tops might allocate five percent to extra small, fifteen percent to small, thirty percent to medium, thirty percent to large, fifteen percent to extra large, and five percent to double extra large. This bell-curve distribution reflects the reality that middle sizes sell in higher volume.
However, size curves vary significantly by product category, target demographic, and sales channel. Activewear tends to skew toward smaller sizes. Plus-size lines obviously weight toward larger sizes. Men's basics often have a flatter distribution than women's fashion. Your size curve must reflect your specific customer base, not a generic industry average.
Getting the size curve wrong has compounding financial consequences. Oversized inventory requires markdowns that erode margins. Undersized inventory results in lost sales and reduced customer lifetime value. Our template helps you build and refine size curves using your actual sales data.
Template Layout and Key Fields
The size run sheet template is organized to take you from data input through quantity calculation in a logical sequence. Each section builds on the previous one to arrive at a final production order by size.
- Style information header with style number, description, and total order quantity
- Size range definition with all available sizes listed across the top row
- Historical sell-through data input with units sold per size from previous seasons
- Calculated size curve percentages derived from historical data
- Adjusted curve fields for manual modifications based on trend or channel knowledge
- Final quantity per size with rounding rules to match pack or carton configurations
- Summary table aggregating size quantities across all styles for production planning
Building Your Size Curve from Sales Data
The most reliable method for determining your size curve is analyzing historical sell-through data at the SKU level. Pull sales reports for comparable styles from the previous two to three seasons and calculate the percentage of total units sold in each size. Average the percentages across seasons to smooth out anomalies from individual styles.
If you are a new brand without historical data, start with industry benchmarks for your category and adjust based on your target demographic. After your first season of sales, analyze the actual sell-through by size and refine your curve for subsequent orders. Even one season of data is dramatically more accurate than guesswork.
Pay attention to sizes that consistently sell out versus sizes that consistently end up on markdown. A high sell-through rate in a particular size may indicate you are under-allocating, while chronic markdowns in another size suggest over-allocation. Our template includes a sell-through analysis section to surface these insights.
Adjusting for Channel and Category Differences
Different sales channels often have different size distributions. Your e-commerce channel may skew differently from your wholesale accounts, and individual retailers may have specific size curve preferences based on their customer demographics. The template includes fields for channel-specific adjustments.
Product category also influences the curve. Fitted garments like blazers and dresses tend to have more variation in size demand than relaxed or oversized styles. When launching a new category that you have not sold before, err on the side of a flatter distribution to minimize risk until you have real data.
Rounding and Pack Configurations
Calculated quantities rarely result in clean numbers. You will need to round to meet minimum production quantities per size, carton packing configurations, and pre-pack requirements if you sell to retailers. Our template includes rounding rules that ensure final quantities make logistical sense.
For wholesale accounts that order in pre-packs, the size run sheet must align with the agreed pack ratio. A common pre-pack for a five-size run might be one-two-three-two-one, meaning one extra small, two small, three medium, two large, and one extra large per pack. Multiply the pack ratio by the number of packs ordered to get your production quantities.
Document any rounding adjustments and the rationale behind them. If you round up on a popular size and round down on a slow-moving one, note this so your team understands the decision and can evaluate its accuracy after the selling season.
Reviewing and Updating Size Curves Seasonally
Size curves should not be set once and forgotten. Consumer body proportions, fashion trends, and brand growth all influence size demand over time. Review and update your size curves at least once per year, ideally after analyzing the full sell-through data from each selling season.
As your brand expands into new markets or demographics, your size curve may shift. International markets often have different size distributions than domestic ones. A brand expanding from the United States into Asian markets, for example, may need to increase allocation toward smaller sizes. Track size performance by market and maintain separate curves where the data supports it.
Frequently Asked Questions
What is a typical size curve for women's apparel?
A common starting point for women's apparel across sizes XS through XL is approximately eight percent, eighteen percent, twenty-eight percent, twenty-eight percent, and eighteen percent respectively. However, this varies significantly by category, brand positioning, and target demographic. Activewear and contemporary brands may skew smaller, while inclusive or plus-size brands allocate more to larger sizes. Always base your curve on your own sell-through data as soon as it is available.
How do I handle size curves for a new product category with no sales history?
Start with a relatively flat distribution to minimize downside risk, then adjust after the first season of sales data. You can also analyze the size curves of similar brands in the same category, survey your customer base about sizing preferences, or use the size curve from your most comparable existing product as a starting point. The key is to collect and analyze data as quickly as possible to replace assumptions with evidence.
Should I maintain different size curves for different sales channels?
Yes, if your data shows meaningful differences in size demand between channels. E-commerce customers may have different demographic profiles than customers at specific retail accounts, leading to different size distributions. Maintaining channel-specific curves improves inventory accuracy and reduces markdowns. Our template includes fields for tracking channel-level size performance to support this segmentation.
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