Analytics

Digitalization opens up new forms of operational training. A growing focus is on smart services, which allow for proactive engagement with customer demands and empower businesses in times of digitalization. While traditional learning environments are rarely tailored toward individual needs, smart services offer new opportunities. Decentralization, previously only a utopic vision, is becoming the reality now.

Short delivery times are essential in competitive markets. In addition to product selection and quality requirements, customers are also demanding more and more from logistics. However, high product variance complicates the situation, as it involves complex material flows and therefore leads to long throughput times. However, a four-step process analysis and modeling can help to reduce throughput times and strengthen the competitiveness of companies from within.

The crises of recent years have highlighted the importance of robust business processes. Even if the concept of robustness is often not clearly defined in the context of entrepreneurial activity, it can certainly be defined on the basis of various factors such as agility, adaptability and resilience. A systematic analysis of robustness and its prerequisites in the corporate context is therefore highly relevant, especially in times characterized by uncertainty.

More than 99% of German companies are small and medium-sized enterprises (SMEs), which therefore represent an important part of industrial supply chains. New regulations are increasing the pressure on companies to create transparency along the supply chain so that the role of SMEs is also coming into focus. However, they are often confronted with limited financial and human resources. Based on a quantitative survey and a literature review, this article deals with the question of what SME-friendly approaches could look like.

The Enterprise-Control System Integration of the operational applications is described in IEC-62264 and also referred to as the automation pyramid. This integration model is built on the MRP-II model developed in the 1980s. This model was groundbreaking for its time and still forms the basis of operational IT systems today. According to this concept, operational applications are run through hierarchically-sequentially (waterfall principle), which results in disadvantages such as: many interfaces, time delays, data loss, inconsistencies, etc. This sequential model neither meets the current requirements nor the informational and technical possibilities of Industry 4.0. It can be replaced by the concept of the digital control twin, which has corresponding effects on the automation pyramid.

In order to withstand disruptions manufacturing companies need to improve their robustness. In the past only infrastructures and resources were considered in the context of robustness, neglecting the interconnectedness of processes. However, a consideration of processes in the context of robustness is highly relevant. Process modeling is used for the design and analysis of processes. This paper describes the requirements for modeling methodology and evaluates existing approaches. (Only in German)

The logistics industry offers numerous opportunities for data-driven solutions, such as improving the part feeding problem in assembly line industries. A data-based approach for will lead to an improvement of cost-effectiveness through optimized processes, resource utilization, and consistent supply to the assembly line. The generated approach is a mixed integer programming model which considers limited storage space, uses constraints, and various cost factors related to transport, replenishment, and picking.

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.