Enabling Demand Forecasting for New Products

Solution Demand Forecasting Tool 
Solution Demand Forecasting Tool 
Industry CPG
Region US
Technology Microsoft Azure
Context
A leading fashion apparel brand faced persistent challenges in forecasting demand for limited-edition SKUs, which comprise 40% of its annual portfolio and have short sales lifecycles. Traditional methods—rooted in heuristics and basic analyses—yielded moderate early-stage accuracy but rapidly declined post-launch, affecting revenue and inventory alignment. Key issues included slow response to market signals and poor lifecycle visibility, leading to suboptimal promotional planning and inventory inefficiencies. MathCo proposed a dynamic, SKU-level demand forecasting model tailored for short-life products. This solution enables real-time insights, improves forecast accuracy, and supports agile supply chain decisions, reducing stock-outs and lost sales while enhancing topline performance.
Problem statement

Our client, a leading fashion apparel brand, specializes in launching limited edition products. A significant portion of their annual portfolio, about 40% comprises entirely new SKUs with short sales lifecycles ranging from 2 to 5 months. These products were often released in collaboration with brand ambassadors or partnerships and were primarily sold through e-commerce channels.   

To estimate demand for these new and short-life products, the client relied on a set of preliminary analyses based on historical proxies, intuition-led heuristics, and basic market basket analysis. Though this achieved a moderate prediction accuracy of 60–70% during the early phase of product launch, the shortcomings were reflected in the accuracy dropping sharply—falling below 40%—leading to missed revenue opportunities and challenges in aligning inventory with actual demand. Despite their deep industry experience and strong brand equity, accurately forecasting demand for new products remained a key strategic gap impacting topline growth.  

Impact

  • Achieved a 25–30% improvement in new launch forecast accuracy by leveraging category-specific dynamics  
  • Prevented over $5M in missed sales opportunities through better demand visibility
  • Enabled savings up to 55% of the previous investment in an off-the-shelf demand forecasting tool 
  • Lowered inventory holding costs by 11%, optimizing working capital  
  • Delivered and deployed the end-to-end solution within 12 weeks, with forecasts refreshed weekly based on real-time market signals  

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