AI-based demand forecasting creates planning reliability in the textile industry Sadie Harley Scientific Editor Robert Egan Senior Editor How can sales figures be forecast more reliably, production capacities planned fully digitally, and employee know-how systematically integrated at the same time? To address this issue, Fraunhofer IWU developed an AI-powered demand forecasting tool for frottana Textil GmbH & Co. KG, the company behind the MÖVE brand.

The tool intelligently analyzes historical sales data and provides companies with a robust, data-driven basis for sales and order planning; in a subsequent step, production planning could also be adapted. Planning is still often based on Excel, experience, and personal judgment The terry cloth and home textiles industry is largely characterized by medium-sized enterprises, and demand is subject to seasonal fluctuations. While there is a stable base demand, seasonal peaks—such as in spring, during the holiday season or in the Christmas season—pose major challenges for planning and scheduling.

In practice, sales and production decisions in many companies still rely on Excel planning tools, individual employee calculations and the experiential knowledge of long-serving experts. In concrete terms, some organizations continue to recreate Excel spreadsheets every month for thousands of products—or even resort to handwritten lists. Although extensive historical sales and production data is available, it is often not systematically analyzed.

The growing shortage of skilled workers, along with age-related departures that result in the loss of valuable institutional know-how, further exacerbates the situation. The result is high planning uncertainty, increased manual scheduling effort, inefficient production adjustments and avoidable costs, such as the labor-intensive transfer of data from ERP systems (Enterprise Resource Planning) into manual tools. Some companies are now deliberately embracing digitalization to place their planning on a solid data foundation.

Intelligent data use with AI and process mining One of these companies is the long-established textile manufacturer frottana Textil GmbH & Co. KG in Upper Lusatia. With its MÖVE brand, the company stands for high-quality terry, bath and home textiles.

For frottana Textil GmbH & Co. KG, the IWU project team—working together with Logsol GmbH—developed a demand forecasting tool that predicts monthly sales figures based on historical sales data while automatically identifying trends and seasonal demand patterns. Artificial intelligence methods are used, particularly neural networks, to analyze complex relationships in the sales data and generate reliable forecasts.

This enables companies to obtain a transparent, traceable and reliable basis for planning and scheduling decisions. High forecast quality despite limited data availability The project results demonstrate a high level of forecasting accuracy, even with a limited database: - 82.7% of sales fluctuations are explained by the model - Robust representation of stronger deviations in individual months (particularly accounting for the impact of forecasting errors) At an average of 340 units sold per month, the forecast shows a typical deviation of only about 38 units—about 9%—from actual sales.