Artificial intelligence

Implicit experiential knowledge will be lost through the retirement of the babyboomer generation. This know-how is difficult to capture and transfer. The KI_eeper project aims to develop an efficient AI-based system that automatically identifies and stores knowledge in the work process. The resulting knowledge base will provide assistance to all employees. The system will be designed in cooperation with employees according to their needs to gain high user acceptance.

The competitive pressure in the contract logistics industry is intense. Logistics providers must respond to tenders quickly and with convincing concepts. This article presents initial approaches to how logistics process planning in tender management can be supported using supervised learning methods. Under the premise that similar processes from past projects can be transferred and adapted to a project to be planned, an AI-based assistance system suggests appropriate process steps and MTM (Methods-Time Measurement) codes during planning. This procedure can accelerate process planning and lead to an increased quality of logistics processes to be planned. (Only in German)

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.

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.