Open Access Articles

Using industry 4.0 maturity models, companies can systematically record their performance in the context of industry 4.0. When the status quo is determined, the question “Where do we want to be in future?” is usually associated at the same time. However, companies are not always in a position to introduce what is fundamentally possible. Therefore, this question is not trivial. If a company is supposedly aware of its I4.0 target position, external influences often lead to the fact that the achievement of the target is made more difficult or hindered. It is therefore important to take these circumstances into account. This paper shows how environmental developments can be taken into account when determining a promising I4.0 target position. The target position forms the starting point for the implementation of industry 4.0 in the company.

The advancing technological change, the globalization of markets as well as increasing customer requirements have led to a significant increase in complexity in manufacturing companies and their supply chains. Companies and entire value chains are countering this development with product modularization strategies. In this context, however, the investigation of the influences of product modularization on the supply chain receives little attention. This can lead to unused potentials and additional risks, such as the loss of core competencies. Therefore, this article deals with necessary processes and success factors that result from a joint consideration of product modularization along the supply chain. On the basis of a systematic analysis of scientific literature and guideline-supported expert interviews, a process model with different phases and steps was developed and currently necessary success factors were identified.

Blockchain (BC) comprises features that are relevant for supply chain management. Product recalls continue to increase due to complex supply chains. The challenge is to efficiently prepare a product recall, perform it adequately and execute the root cause analysis. The BC can support as a tool and create transparency. A reaction to required product recalls can occur timely, cost efficiently and situationally appropriate.

The digital transformation of the industry, with its technological components, has a direct impact on the alignment of logistics processes within companies as well as in entire corporate networks. The development and integration of new technologies is triggering more and more rigid corporate structures and control architectures. The vision ranges from decentralized networks of modular conveyor and storage technology to the application of artificial intelligence for smart services in logistics. There is a requirement to identify the logistic objects, to locate them, to control them and to record their states, in order to achieve a goal-oriented interaction in the sense of a holistic networking.

Digitalization is a strategic core issue that has to be anchored in the strategy of every company. The challenge in this context is that there is no uniform pattern for the digital transformation of a company. Instead, each company has to develop its own company-specific plan how it will position itself in the context of digitalization. Furthermore, the development of an individual digitalization strategy is required. The following article presents a planning approach for the development of such a digitalization strategy, based on three major steps.

Companies have always been in a constant state of change. This change is today closely linked to the buzzword’s “digitization” and “agility”. Agile methods, especially in complex projects, can pave the way for targeted digitization and, on the other hand, provide a more agile way of working for digital technologies. Through group discussions with managers from small and medium-sized IT companies, this article focuses on the question of whether agility is the precondition or consequence of targeted digitization. This article is aimed at decision-makers from SMEs who want to increase the degree of agility in the company in the context of increasing digitization.

Data sets increasing data bases and computing power as well as decreasing costs for computing and storage capacities form the basis for the use of Machine Learning (ML) in production. The challenges are the identification of promising application areas, the recognition of the associated learning tasks as well as the uncovering of suitable data sets. This article therefore answers the following questions: Which application areas in production offer the greatest potential for the use of ML? Which freely accessible data sets are suitable for gaining experience and which learning tasks are associated with them? What are best practices for the application areas?

The integration of industrial laser assistance systems for worker guidance into the control layer opens up possibilities of digital transformation for manufacturing companies. These are illustrated using the example of the Digital Factory of the Leuphana University Lüneburg. In a practice project a manual assembly station using an industrial laser assistance system is developed and integrated into the SIMATIC control level of the digital factory. The worker interacts with the assistance system and is guided by the latter through the order-related assembly process. The worker stands in the center of action.

Small and medium-sized enterprises can hardly exploit the potential of digital transformation. In the BMBF research project »ScaleIT« an Industry 4.0 platform was developed with which individual process steps can be improved with the help of apps. There are both ready to use apps and open source tools that make it easy to develop new apps. Companies do not run the risk of a profound change in their IT processes, but can optimize their value chain step-by-step by implementing and installing new Industry 4.0 apps. A methodology helps to uncover the greatest digitization potential in companies.

The trend towards customer-specific mass production poses great challenges for the classic production methods of small and medium-sized companies. The combination of flexible robotic solutions and artificial intelligence approaches is promising to enable production efficiency and fast adaptability in modern production systems. This paper presents such a solution in the form of a realized demonstrator setup composed of a collaborative robot assistant. The robotic system independently interprets the activities of a human employee and supports the employee in his or her activities by imitation.