Artificial Intelligence

The circular economy (CE) promises a more sustainable use of resources by managing products in a cycle and striving for a transformation from a linear to a circular supply chain. In particular, digital technologies as enablers for the circular economy have been increasingly researched and applied in practice in recent years. This article describes which digital technologies offer potential for increasing circularity in the production of circular photovoltaic (PV) systems.

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.

This paper examines various forecasting models used to predict business crises in the automotive and electronic manufacturing industries, with a focus on German companies. By comparing the performance of these models, we aim to identify the best approach for each industry. We also discuss real-world business case scenarios to demonstrate the practical implications of our findings, including the role of risk management in supply chain and procurement departments. Our results show that the most effective model for forecasting crises in the automotive industry is the VAR model, while the EWS model is best suited for the electronic manufacturing industry. Furthermore, we identify key risk factors that supply chain and procurement departments must consider enhancing their resilience in the face of crises.

Artificial Intelligence (AI) can make a major contribution to the future viability of our economy and society—whether by improving existing processes or new products and services that promise greater efficiency, more robust structures and more climate protection. At present, however, SMEs in particular are still reluctant to use AI systems. The frequently cited reason is that data protection hurdles appear to be too high. This article discusses the opportunities of data-based value creation. The central question is how AI applications in industry can generate economic added value from data while maintaining data protection and security.

Additive manufacturing is an increasingly important manufacturing technology with huge economical potential. However, its popularity is accompanied by high material and time losses, as defects are often detected at a very late stage. One solution for a more sustainable production is the automated detection of manufacturing defects using artificial intelligence. This article describes the digitization of the defect detection process in additive manufacturing using a system based on a neural network. In addition to the steps for automated defect detection, system performance is also discussed.

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.

Procurement has the task to supply an organization with required but not self-produced goods. The goods vs. payment exchange with suppliers is laid down in contracts. “Electronic contracts" or “Smart Contracts” represent the logic digitally and thus enhance transparency. This can still evolve. In the future, improved algorithms and artificial intelligence will not only be able to administer contracts, but also to design them. This article presents the status quo of "Smart Contracting", places it in the "Legal Tech" topic and shows how artificial intelligence could be used in procurement.