Introduction 

A leading retail manufacturer with over $1 billion in annual revenue faced several issues: inconsistent decision-making affecting profit margins, labor-intensive and time consuming trend analysis, and difficulty predicting successful fabric designs.

• Develop a machine learning (ML) engine to predict fabric design success for upcoming seasons, enhancing data-driven decision-making and optimizing fabric choices.

• Combining data driven predictions with the creativity inherent in fashion

• Adapting to the fast evolving nature of fashion trends

• Customized Fabric Success KPI: Combined two business relevant KPIs, ROS (Return on Sales) and GP per NSV (Gross Profit per Net Sales Value)

• Predictive Algorithm: Provided insights into fabric design success before production

• Real-Time Dashboard: Displayed predicted success rates, streamlining decision-making

• Image and Text Retrieval: Allowed users to search historical designs by fabric attributes and visuals, comparing new designs with similar past fabrics to assess performance

Technological Framework:

Why this setup?

A combined fabric success KPI (ROS + GP per NSV) with image and text retrieval enables buyers to objectively benchmark new designs against historical commercial performance replacing labour intensive manual trend analysis.

Why this setup?

Integrating historical ROS and GP per NSV data directly into the prediction model ensures that success metrics reflect real commercial outcomes making the ML system directly relevant to profitability decisions.

Takeaway

Zapcom built an ML powered fabric selection engine for a $1B+ retail manufacturer enabling data-driven design decisions through a combined ROS/GP KPI, predictive algorithm, and real-time dashboard that replaced labour intensive manual trend analysis.