The apparel industry uniquely combines the characteristics of make-to-stock manufacturing with consumer-driven fashion volatility which can lead to a forecasting nightmare. Speaking with several supply chain executives in the apparel industry, attribute-based SKU-level forecasting is not just difficult; it is often impossible. To reign in the chaos many have turned to massive spreadsheets or homegrown solutions to account for color, size, style, region, gender, season, etc.

Others have turned to off-the-shelf software only to realize the majority of solutions fail to go beyond two attributes (useless for the apparel industry). A lot of effort has been placed on solving this challenge because manufacturers know they must get more granular in their planning if they want to keep costs low and maintain high service levels.

Style and color attributes alone are notoriously difficult to forecast. Now add size distribution to the mix. While sizes will remain fairly consistent over time; can they serve to disaggregate high-level demand plans? Consider that even a forecast error of no more than 1% – 2% for each size in a curve from XS to XXL can cause an overall error of 10%. This is then multiplied across all styles and colors of the item.

The secret for honing size curves for better accuracy and lower obsolescence lies in gathering historical sales data on all items and product groups over time, then automating the task of selecting real-world profiles that most closely match the characteristics and seasonality of new products.

However, an even greater advantage lies beyond customizing the size curve. Look at how you can generate more accurate, attribute-based forecasts by style, color, fabric, pattern, label, and many more and you quickly realize proportional profile planning has the ability to revolutionize your forecasting. Here are a few helpful hints for apparel manufacturers (more can be found in the white paper, Trouble with the (Size) Curve):