Künstliche Intelligenz

Manufacturing companies must work economically while delivering quality - in some industries with a zero-defect tolerance. Quality control often is carried out manually and with a time delay, thus errors can only be corrected at a late stage. Using artificial intelligence (AI), visual quality control can be automated, carried out in real time and integrated into the production process - making it more accurate, efficient and cost-effective. A case example shows the advantages of tackling AI issues in interdisciplinary teams with partners.

Artificial intelligence (AI) and self-learning systems have immense economic potential and are a driving force for digitalisation. Artificial Intelligence is radically changing value chains, business models, and employment in industry. Data-driven services are added to traditional products in almost all industries. Integrating Artificial Intelligence in products and services as well as using data from the production process provides opportunities for new business models in an increasing competitive international environment.

Due to competitive pressure pharmaceutical companies are also driven to increase the efficiency of their processes. In this paper an approach for the predictive detection of malfunctions of filling systems for powdery pharmaceutical products using machine learning is presented. The focus is on the prediction of filling deviations with recurrent neural networks, with the objective to detect a drift in the process stability to intervene accordingly.

Due to the advancing digitalization of business sectors and increasing competitive pressures, industrial companies are forced to promote their own digital transformation to sustain on the market. Here, the literature regards the CFO as a key corporate function to induct digitization initiatives within organizations. The blockchain technology, due to its features of transparency, immutability and cryptography combined with its ability to coordinate data flows of e. g. the IoT or AI, constitutes a suitable instrument for the CFO to meet the requirements of the Industry 4.0. The results are improvements of business processes in regard to efficiency and automation, a relocation of the CFO’s strategic role, improvements of CFO-relevant KPIs through integrating machines into payment networks as well as the emergence of integrated business ecosystems facilitating new forms of inter-organizational collaboration.

Machine learning is the key technology of almost every instance of modern Artificial Intelligence. Enormous datasets are produced in digitized industrial processes and in the Internet of Things, which can well be exploited by learning in deep artificial neural networks. Standard machine learning algorithms require these datasets to be centralized before learning a model. Several good reasons - ranging from data privacy over latency to economic efficiency - favor learning at the edge so that reasoning is fast and no local data is transferred. The article shows how decentralized learning works and how to evaluate it. Moreover, we point to special resource-efficient learning algorithms and discuss small remaining risks of data reconstruction.

The digital transformation led to disruptive changes in business models of leading companies. Big Data serves as one of the key enables in this area. The transfer of this concept in the production domain towards an Industrial Big Data is key challenge for producing companies. Although exemplary key projects exist, no available characterization of structural elements in Industrial Big Data Processes exists. Therefore, this article aims at presenting initial structural elements of Industrial Big Data projects based on exemplary use cases.

One of the implications of Industry 4.0 is the emergence of a new collaboration between humans, robots and virtual agents as teams - robots are no competitors, but typically take over tasks that are time-consuming, harmful or even extremely dangerous for humans. These Hybrid Teams must communicate efficiently, should be flexible and broadly applicable. How can one implement such a team and what has to be considered? The article describes the organization and properties of Hybrid Teams and proposes a system architecture that is based on experiences from the ongoing research project HyScoiaTea (FKZ 01IW14001, BMBF).