Künstliche Intelligenz

Artificial intelligence has the potential to bring companies and entire industries to a completely new technological level. The prerequisite is data with a high degree of maturity, with which companies can automate complex processes, calculate forecasts or create analyses. With the right data strategy, structuring and achieving the necessary data quality are no longer dreams of the future.

Digital twins are an important part of the Industry 4.0 idea. They mirror physical goods in the digital world and enhance them with additional capabilities and functions for analysis, forecasting and decisionmaking. This paper contributes to the classification and assessment of Digital Twins using a multidimensional maturity model. The presented method "DT-Assess" enables an application-specific assessment of Digital Twins. The developed maturity model consists of seven categories with a total of 31 characteristics to be evaluated. The systematic evaluation in five application scenarios allows, for the first time, a classification of the respective "digital twin" implementation or concept with the aim of identifying further development options and weaknesses.

Huge amounts of information are stored in the memory of every employee. How a product works, best practices in departments, customer-specific information, general market and competitive knowledge, etc. This expertise is a blind spot in most companies. So how do you make in-house know-how available? And in a time when many employees work remotely, in home offices, even across countries or continents?

Artificial intelligence (AI) is already an important part of business models and processes of many companies. In the future, AI systems will pro- foundly change our working environment. AI systems can develop completely new potential for companies in a wide variety of sectors and domains - especially in industry. Existing busi- ness models can be optimized along the value chain by optimizing production flows and processes or avoiding production downtimes with predictive maintenance. At the same time, AI systems can enable completely new business models and thus radically change existing mar- ket structures through new players. The AI economy of tomorrow will be more individual, more precise and more sustainable: Competitive value creation without AI will not be possible in many areas of industry.

An important element for companies to deal with the demands of the world of work is the continuous and needs-specific further training of employees. The possibility of learning close to the workplace has a major role to play here.

Artificial intelligence (AI) is the next stage in the digitalization of the economy. The technology also offers great potential for small and mediusized enterprises (SMEs). However, many SMEs are still reluctant to introduce AI and are only at the beginning of digitization: only around one fifth of all SMEs in Germany have thoroughly digitized their own processes and departments. What does this mean for the use of AI in companies? What steps should businesses take now to take advantage of the opportunities AI offers? And what stumbling blocks should be avoided? This article presents practical implementation concepts for companies with different levels of digital maturity and AI deployment capabilities and shows the range of potential benefits of AI applications in different industries and with different value creation architectures in medium-sized companies.

To be able to use artificial intelligence, a company does not necessarily need a qualified specialist. The Fraunhofer study “Cloud-based AI Platforms - Opportunities and Limits of Services for Machine Learning as a Service” shows how small and medium-sized companies can proceed instead. This article summarizes the arguments and results of the study, citations from it are therefore not marked accordingly.

Intense global competition, shorter product life cycles and an increasing number of variants require flexible and adaptable, but at the same time economical production and logistics systems. This requires constant replanning of factories and logistics systems. Value-adding processes are being outsourced to contract logistics providers. Contract logistics planners must respond to tenders as quickly as possible and develop a proposal with an initial planning concept and a cost estimation. Despite standardization efforts in planning, the knowledge is often only implicit at the planners. This article describes the need for support by an AI-based assistance system during the planning process and how a digital platform for such an assistance system should look like.

In the last few years, quantum computing has achieved new successes, such as Google’s quantum supremacy experiment [1], and has been showing adoption by large industrial firms to tackle complex problems. But what has led up to these developments? What kinds of problems can we expect to be able to solve in the near term with quantum computing? What are the challenges that we encounter with this technology and deploying within industrial settings?