Fraunhofer Institute for Material Flow and Logistics IML
Predictive analytics for forecasting demand quantities in textile logistics
Companies in the textile industry are regularly faced with the challenge of having to determine their required capacities (machines, vehicle fleet, storage spaces and resources) in advance. These are often subject to large fluctuations, irregularities and unpredictable influences. An accurate estimation of the expected quantities allows a better assessment of the required capacities and is a great support for planning. Artificial intelligence (AI) methods can be used to develop customized models that support companies in planning their capacities. It‘s shown in industrial applications, that the compiled interdependencies and forecasts lead to an improved planning situation.
Various algorithms from the field of predictive analytics are used to forecast future demand quantities. The forecast quantities are used as a data basis for holistic logistics planning. This includes the selection of optimal production, storage and handling locations, taking into account all relevant influencing variables. Foresighted fleet planning can also be optimized on the basis of these quantities, taking into account customer-specific requirements.
We offer support in the strategic planning of transport logistics with a focus on specific solutions for the textile industry. Our proven processes are tailored exactly to the needs of our customers.