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In developing countries approx. 10 % of all medicines are falsified. The securPharm system in Germany prevents this high level of counterfeiting. It allows to identify and stop counterfeit drugs along the supply chain. The identification works via a data storage and polling system on supranational level. Based on system difficulties, pharmacies do not accept the system fully. Besides online pharmacies are an issue because they are not as much secured as the stationary distributors. Subsequently a comparison of the European and the US system shows their equality in most of the elements.

This article describes a predictive maintenance approach in which a flexible sensor toolkit records and a prediction model monitors the component wear within technical systems. The condition of the components is not determined continuously, but based on time-discrete measurements. The prediction model predicts the presumable remaining useful life of the components based on the recorded data. A machine learning tool is trained with historical wear curves and used to generate the prediction. The training data is collected through statistical tests in which the influencing variables and characteristic curves of different types of wear are identified.

The SARS-CoV-2 virus poses unexpected challenges for the global economy. Demand for goods collapsed, and supply declined because companies suddenly ran out of employees or intermediate products. As a result, economic indicators turned negative. However, although an economic recovery is now underway, uncertainty about future developments is still on a record high. This is due not least to global value chains. Their disruption was a major factor in the aforementioned distortions, but they are still central to local production.

Simulation is frequently used for prediction of the outcome of adjustments in production systems. Real decision processes must be represented in the simulation. To achieve this, complex real decision processes have to be transferred into the simulation. This leads to a high effort for the creation of simulation models. This is resolved by the concept of iterative optimization-based simulation. Instead of transferring complex decision processes into the simulation, the predicted parameters are exported and existing decision processes determine a solution.

In view of the digital transformation, companies are facing radical changes which could have a big impact on their business success. Change management could help companies to deal with this transformation and to avoid the risk of losing market share to more innovative companies. The more important question at this stage is if traditional Change management models handle this topic well or if they must be adjusted. This article identifies challenges and success factors by using a literature analysis and a survey, by means of which an adapted model for successful Change management is generated.

Severe economic crises lead to a significant increase in the frequency of company insolvencies. The insolvency of one company in turn has a significant negative impact on other companies, which consequently lose key customers or suppliers. For risk management, the quantification of risks and the initiation of coping measures, it is particularly important in a crisis to properly assess the insolvency risks of business partners (counterparty risks). It is essential here that the insolvency risks of companies cannot be viewed independently, but that there is a considerable systematic component.

Innovation activity in the industrial environment is shifting towards product-service systems as a result of digitalisation. Small and medium-sized enterprises have so far focused their development activities strongly on product development. The switch to “smart” products and the coupling to services often requires personnel and financial resources that SMEs cannot provide. Crowdsourcing is one way of opening up the innovation process to external actors and realising cost and speed advantages. However, the integration of crowdsourcing elements faces several challenges. This article shows both the potentials and the barriers of crowdsourcing in the industrial environment.

The implementation of Industry 4.0 solutions offers great potential for meeting growing challenges in the context of digitization. Nevertheless, particularly small and medium-sized companies are hesitant to implement Industry 4.0 solutions. Within the framework of the ERDF research project »Synus«, methods and tools were developed to support small and medium-sized companies in the evaluation and selection of Industry 4.0 solutions. This contribution presents the potential model, which enables small and medium-sized enterprises to select suitable Industry 4.0 solutions.

Markets will change dramatically due to the megatrend platforming [1]. For previously isolated markets, such as Smart Factory, Smart Logistics or Smart Grids, this offers the potential to create interconnected “smart Ecosystems”. Logistics platforms as an instrument of networking are a key driver of this platform economy. In the application for Supply Chain Visibility, logistics platforms promote the transparency and control of logistics chains and thus represent an essential first step towards smart Ecosystems.

Additive Manufacturing has become more important for industrial applications. The technology offers the opportunity of high geometric flexibility and no need of product specific tools including short time to market. The aim is to integrate added value into products by exploiting these possibilities. Therefore, in this work a methodology focusing on these aspects is developed and applied to a structural component.