Open Access Articles

In transport logistics the utilization of transports is often unknown. As a consequence it is not possible to use this information during the transport planning phase and it cannot be used for operational transport control. This report describes technologies which can measure the utilization of shipping spaces. Due to the lack of market-ready systems, a new system was developed that uses ultrasonic sensors to determine the transport utilization. The system was built on a trailer and was tested during live operation. It was shown that the system fulfills the given requirements and it is suggested to expand the tests.

The large number of systems offered on the market makes a well-founded selection process necessary from the requirement survey to the final system selection. A comprehensive model that systematically supports and simplifies this process is the subject of this two-part article. The methodology goes beyond a questionnaire-based query and verifies system capabilities using structured case studies. The first part of the article [1] describes the process steps from the survey and collection of requirements of the customer to the system to their structuring in customer specifications. The present second part outlines the process steps of the system rough selection up to its fine selection. The individual selection steps are methodically supported by practical references as well as by the use of concrete tools. Using the described methodology selected systems can be objectively compared - a prerequisite for effective and efficient system selection for industrial companies.

Functional materials, so called smart materials, have the ability to adapt to external environmental conditions i.e. they are able to change their properties through external physical stimuli so that, they are optimally adapted to their surroundings. These processes are accompanied by energy conversion processes. These properties predestine smart materials to act as sensors and actuators. They enable high functionality in simplified structures, already on the material level. Therewith, a new and better quality can be achieved in product design.

When aiming for an Industry 4.0 vision, companies are well-advised to not only focus on technology and data. With any digital transformation, the careful consideration of all elements of the company’s “socio-technical triangle” (man, technology, and organization) is a central success factor. Based on a qualitative survey, we identified qualification, organization, and leadership as central dimensions of the work system. Integrative measures include identification of competence requirements, training in data-thinking as well as agile working methods and structures. Finally, leadership plays a central role in orchestrating the digital transformation.

Recently, implementation procedures of automatic production, digitalization and Industrial Internet of Things technologies (IIoT) play an increasing role in industrial manufacturing processes. Subsequently, the competence requirements for employees change. These changes cannot be anticipated by traditional learning approaches. The following contribution faces this challenge and will show a new integrated learning factory approach which combines the application of new technologies with a flexible production environment. Thus establishing production surroundings that are familiar to the learner. The contribution demonstrates this approach using a quality control process in the context of logistics.

Digitization has a considerable impact on companies and their business environment. With extensive digital pilot projects and digitization programs, large corporations show that they are increasingly internalizing the digital transformation. Small and medium-sized enterprises (SMEs), on the other hand, often have a need to catch up. In addition to the technical aspects of digital transformation, the human factor is playing an increasingly important role. With the help of a cross-sectional analysis of German SMEs, findings on digitization competence were derived and analyzed across industries. The term work 4.0 was divided into the dimensions of qualification, organization and leadership and these were considered as influencing factors. In individual industries, there are clear deficits in the area of digitization competence. It shows that these competences depend to a large extent on the dimensions of the work 4.0.

The story behind “Industry 4.0” has a much bigger scope as it is talked about, according to the authors particularly in the management of small and medium-sized enterprises (SMEs). For this reason the paper on one hand lists the essential prerequisites for Industry 4.0, on the other hand it describes the features of the “Digitalisation” which make the upcoming move revolutionary. A consequent digitalisation of processes in organisations in terms of automation takes away people’s effort for decision-making as well as semi-autonomous, networked artificial intelligence (AI) does. This facilitates and irritates participants of organisation equally. The digital transformation will have consequences for production and organisation therefore, i.e. this change will influence technology and corporate culture.