DEMAND FORECASTING FOR A TEXTILE MANUFACTURER
Demand forecasting is fundamental to compete in the textile and fashion industries. Unreliable forecasts affect directly companies profitability due to potential stock-out or overstock occurrences. Sales forecasting is a very complex task. The success of fashion products heavily relies on the personal taste of consumers, which varies over time and geography. Moreover, the lifecycle of products is usually very short since every new season they are replaced by new products with few or no historical data. In addition, production usually starts on customer orders received at a very early stage, in which no sales information is available as a reference for the production plan. It is in these challenging domains that artificial intelligence brings the largest benefits.
Improve the quality of sales forecast to optimize inventory management and production planning.
Unveil the relationships between early customers orders for tissue samples and the subsequent actual demand.
Develop a machine learning framework to generate reliable sales predictions based on historical data and adjusted for each customer and tissue variant.
Better control on the inventory and production volumes and reduction of the relative costs. Increase the efficiency in resource utilization. Better understanding of the products seasonal peaks. Increase customer satisfaction.