Design

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.

In this contribution, a method for the implementation of Industry 4.0 projects in production and logistics for small and medium-sized enterprises (SME) is described. This method takes various boundary conditions of SMEs into consideration and has been applied in different projects with SMEs within the “Mittelstand 4.0-Kompetenzzentrum Hamburg” initiative. The method focuses on an integration of new technologies into existing systems and the connection of newly generated data with known information flows.

When digitalising and automating work processes, it is often overlooked that this can trigger serious changes for the organisation. This article shows that such changes can lead to an incongruence between “what an organization does” (practice), “what it can do” (knowledge) and “who it is” (identity). These incongruities must be overcome in order to implement change successfully. If managers are aware of this, many problems such as the collapse of existing routines, knowledge gaps or the departure of important employees can be foreseen and solved.

Management tools are a dime a dozen. The US-American strategy consultancy Bain & Company, for example, analyses regularly the 25 most popular of them worldwide. However, the best tool is only as good as its user. The proper and efficient utilization requires common sense. If a manager has it or not can be identified by the following qualities: He knows that others see him different from how he sees himself. He sets high value on a respectful feedback culture in his company, and he counts on the power of cooperation.