Retail Apparel Forecasting & Planning: How to Use Shape Curves

Shape curves are a commonly used tool in retail merchandise planning and demand forecasting, particularly in the apparel industry. They help retailers and manufacturers predict and plan for future demand for specific products, allowing them to make informed decisions about inventory levels, pricing, and other factors that can impact the success of their business.

Style in Retail Planning

One of the key factors that can influence demand for apparel is style. People’s fashion preferences are constantly evolving, and retailers need to be able to anticipate and respond to these changes in order to stay relevant and profitable. By analyzing shape curves for different styles of clothing, retailers can get a sense of how popular a particular style is likely to be in the future, and adjust their merchandise planning accordingly.

Color and Size in Retail Planning

Color is another important factor to consider in apparel demand forecasting. Different colors can have different levels of popularity depending on the season, trends, and other factors. By analyzing shape curves for different colors, retailers can get a sense of which colors are likely to be in demand and plan their inventory accordingly. A similar approach is needed in order to accommodate different sizes of clothing. Certain regions of the country for example may have a predominate distribution of certain sizes versus other regions. The concepts of Style, Color and Size are critical factors in determining a retail apparel merchandise plan.

How Shape Curves Fit Into Merchandise Planning

There are a few different ways that retailers can use shape curves to inform their merchandise planning and demand forecasting. One common approach is to analyze historical data to create shape curves that reflect past demand patterns. This can help retailers get a sense of how demand for a particular product has changed over time, and what factors may have influenced those changes.

Another approach is to use predictive analytics like omnithink.ai to forecast future demand based on a variety of factors, such as economic conditions, consumer sentiment, and market trends. This can help retailers anticipate changes in demand and adjust their merchandise planning accordingly.

One way that retailers can use shape curves to inform their demand forecasting is by analyzing the “life cycle” of a product. As a product becomes more popular, demand for it typically increases. However, eventually demand will start to decline as the product becomes less popular and is replaced by newer products. By analyzing shape curves for different products, retailers can get a sense of when demand is likely to peak and start to decline, and plan their inventory levels accordingly.

Another way that shape curves can be useful in retail merchandise planning and demand forecasting is by helping retailers identify “breakout” products. These are products that experience sudden and unexpected spikes in demand, often as a result of a viral trend or other unexpected event. By analyzing shape curves, retailers can identify products that may be at risk of experiencing a breakout, and adjust their inventory levels accordingly.

Overall, shape curves are a valuable tool for retailers and manufacturers looking to plan and forecast demand for their products. By analyzing shape curves for different styles, colors, and other factors, retailers can get a better understanding of how demand is likely to evolve over time, and make informed decisions about inventory levels, pricing, and other key factors that can impact their business.