Size Curve Analysis

An examination of the distribution of sizes within a product category or subcategory, used to optimise inventory levels and meet customer demand.

What is Size Curve Analysis?

.Size Curve Analysis is a retail strategy used to optimise inventory and assortment planning. It involves evaluating sales data to understand the demand patterns for different product sizes. Retailers can use this analysis to make informed decisions about how much stock to carry for each size, reducing excess inventory or stockouts and improving overall customer satisfaction and profitability.

How Size Curve Analysis works

  • Data Collection: Retailers gather historical sales data for a specific product, focusing on sales quantities and sizes sold over a defined period.

  • Segmentation: The data is segmented into different size categories, creating a visual representation of the sales distribution for each size.

  • Analysis: Retailers analyse the sales data to identify trends and patterns. They can determine which sizes sell best and worst and assess the seasonality or demand fluctuations for each size.

  • Inventory Planning: Using the insights from the analysis, retailers make informed decisions about how much inventory to allocate to each size. This allows them to reduce overstocking of less popular sizes and avoid stockouts of high-demand sizes.

  • Assortment Planning: Retailers can adjust their product assortments based on the size curve analysis. For example, they may choose to stock more items in the sizes that have higher demand.

  • Replenishment Strategies: Retailers implement replenishment strategies, such as automatic restocking for popular sizes, to maintain optimal inventory levels.

  • Monitor and Adjust: Size Curve Analysis is an ongoing process. Retailers continuously monitor sales data and adjust their strategies based on changing customer preferences and market trends.
By using Size Curve Analysis, retailers aim to optimise their inventory and product offerings, leading to increased sales, reduced carrying costs, and improved customer satisfaction.

Pros of Size Curve Analysis

  1. Optimised Inventory Management: Size Curve Analysis enables retailers to stock the right quantities of each size based on historical demand. This minimises overstocking of slow-selling sizes and reduces stockouts for popular sizes, leading to improved inventory turnover and reduced carrying costs.
  2. Enhanced Customer Satisfaction: By ensuring that popular sizes are consistently available and reducing the frustration of customers encountering stockouts in their preferred size, retailers can enhance the overall shopping experience and customer satisfaction.
  3. Increased Sales and Profitability: When retailers use Size Curve Analysis to align their inventory with customer demand, they can increase sales by having the right products and sizes in stock. This, in turn, boosts profitability and minimises the need for clearance sales or markdowns on excess inventory.

Cons of Size Curve Analysis

  1. Data Dependency: Effective Size Curve Analysis relies on accurate historical sales data. Retailers may face challenges when dealing with incomplete or inaccurate data, which can lead to suboptimal size recommendations.
  2. Initial Implementation Challenges: Implementing Size Curve Analysis may require changes in inventory management practices and systems. This can be a complex and time-consuming process, especially for retailers with existing inventory management systems.
  3. Seasonal Variability: Size demand can vary seasonally, making it challenging to establish fixed size curves that work year-round. Retailers may need to continually adjust size curves to accommodate these fluctuations, which can be resource-intensive.


Below you will find answers to common questions
How can Size Curve Analysis help improve my inventory management?
Size Curve Analysis helps retailers make data-driven decisions about stocking sizes, reducing stockouts for popular sizes and minimising excess inventory for less popular sizes. By aligning inventory with customer demand, you can optimise stock levels, reduce carrying costs, and improve inventory turnover.
What data do I need for effective Size Curve Analysis?
Effective Size Curve Analysis relies on historical sales data, ideally broken down by size. Additionally, data on customer preferences and returns can be valuable. With this information, you can identify trends in size demand, adjust your size curves accordingly, and make informed stocking decisions. Retailers often use point-of-sale systems and inventory management software to collect and analyse this data.