Predicting substantial events in your supply chain
The goal of “predictive analytics” is the development of methods to analyze and control logistical value networks based on data. Research in this field of competence focuses on the proactive control of the supply chain. Proactive decisions can be supported by estimating the impact of relevant changes or optimizing performance in the supply chain while taking into account uncertainties.
What is the influence of geo locations in your value network?
What does your value network look like on the map? We link your network with data from your ERP system so that a joint visualization and analysis are possible. The goal is to display all decision-relevant information, which is necessary for management. In addition, rules can be defined to decide which events are critical and should therefore be displayed automatically. This ensures that management has all the necessary information immediately at its disposal to make the best possible decisions. For example, the stock levels of suppliers can be displayed or customers for whom decisions are still open.
How can performance be improved under uncertain conditions?
The material flow in your network forms the basis for the success of your company! How will the material flow change in the future? What risks can influence it?
How can the material flow be optimized? To answer these questions, realistic models are used, and wide varieties of risks are simulated so that their effects can be estimated. In addition, modern optimization algorithms can be used to find (approximately) optimal solutions.
Due to the worldwide allocation of production sites and global integration, it is becoming increasingly important to be able to analyze and visualize data automatically. In the SME sector, it is often still possible and the only economical option to obtain information in person or via e-mail. However, the benefits of automated processes are undisputed here as well. We are also researching solutions for SMEs and support your company in this challenge.
For more information please contact the research field manager:
Mag.rer.nat. Mattias Winter
Tel: +43 5 0804 33225