Author: admin (Page 2 of 3)

Conventional AI models of supply chain management provide interesting recommendations, but often no real understanding and unreliable predictions, which reduces trust in their advice. In a research project with BMW, a team around Prof. Dr.-Ing. Hendro Wicaksono, Professor of Industrial Engineering at Constructor University, PhD-Student Ishansh Gupta and Master-Student Adriana Martinez has developed a pioneering method that closes existing gaps by taking a causal approach.

In addition to data relating to the reliability of suppliers, for example, human “tribal knowledge” from the company is also incorporated into the model. This makes its recommendations more accurate, trustworthy and realizable.

In a survey at BMW, all the experts questioned favored this causal AI approach over conventional machine learning models.  “The most critical and strategic business decisions are causal questions,” says Prof Wicaksono. “Causal models try to capture the process of data generation and not just fit curves to the data. They are not a black box but can answer the question of ‘why’.”

Reduce costs, increase productivity and simplify processes: Researchers at Constructor University in Bremen are developing a digital platform for managing transport infrastructures in a more efficient way. Professor Hendro Wicaksono’s project is funded by the German Federal Ministry of Digital Affairs and Transport (BMDV).

The platform that Wicaksono and his team are setting up works with so-called “digital twins”: with digital asset counterparts of real projects, systems or infrastructures. They contain all the relevant technical information on which they are based. The digital assets are fed with real-time data and cover the entire life cycle of a form of infrastructure. With the help of these digitalization measures in the transport sector, resources can be used more efficiently and thus the management of infrastructures can be optimized.

“With such a tool, workflows and decisions can be tested and optimized in the digital world before they are transferred to the real world,” says Wicaksono, Professor of Industrial Engineering, describing one of the benefits. Another is that by integrating various data, plant operators can determine at an early stage when, for example, maintenance or servicing of a bridge is necessary.

The portal is intended to be used by the various companies involved in the construction or maintenance of an infrastructure. Small and medium-sized enterprises in particular will be involved, thus promoting cooperation between them. In the project itself, Wicaksono is the scientific coordinator and responsible for semantic data management and the development of predictive models, among other things. Here, machine learning and probability calculation are used to predict outcomes. Wicaksono designed a specific form of artificial intelligence, “Explainable AI”, which makes decisions of a machine learning model transparent and comprehensible. In addition to Constructor University, two companies – Vectorsoft AG and Concedra – are also involved in the project.