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Discrete-event simulation of logistics and production systems plays an important role in the context of digital transformation. Its integration into modern planning and control processes is urgently required in order to realize Industry 4.0 concepts. In addition, simulation models will be an important part of the so-called digital twin in the planning and operation. However, the requirements for simulation models and tools are not yet comprehensively defined, and technical solutions have not been adequately implemented. This article presents the fields of action for the implementation.

The increasing interconnectedness of production systems and the use of IoT devices generates a considerable amount of employee or customer data - whether directly or indirectly. The EU General Data Protection Regulation (EU-GDPR), effective 25 May 2018, results in a massive increase in the rights of data subjects and documentation obligations arising from the processing of personal data [1]. Those who do not respect these rights and/or fail to comply with their obligations face painfully increased fines of up to EUR 10 million (in serious cases EUR 20 million) or 2 % (4 %) of the annual turnover.

The transformation of business models, based on the digitalisation of products, is becoming more and more in the focus of the companies and is also affecting the Load Carrier branch. Intelligent components are integrated into the products, to collect and analyse data in order to create new services. The digital transformation of intelligent products into cyber-physical-systems, which includes a setup of a cloud-based Service System, enables traditional Load Carrier manufacturer to develop new business areas to optimize the supply chain of their customers by offering new services.

The large number of systems offered on the market makes a well-founded selection process from requirement collection to final selection necessary. A comprehensive model that systematically supports and simplifies this process is the subject of the following article. The methodo-logy goes beyond a questionnaire-based query and validates system capabilities using structured case studies. The first part of the article describes the process steps from the survey of the requirements of the system to their structuring in customer specifications. The second part of the system selection process is described in detail in the second part of the article.

Whilst bIoTope should be understood as a highly flexible and dynamic ecosystem capable of seamlessly integrating arbitrary proprietary IoT platforms, it nevertheless builds upon several core components, which provide essential functionality. Those functionalities referenced in this article, are agnostic to other ecosystem-external systems. Inside of the ecosystem, a micro-service-architecture MSA approach is applied. Using O-MI for technical Interoperability and O-DF for syntactic and semantic interoperability are foundation pillars of the ecosystem.

The Industrial Internet of Things (IIoT) offers a multitude of opportunities in a wide variety of applications. Similar to the IoT, the high degree of distribution, the heterogeneity, size and dynamics of the network pose new challenges to the security of such systems. A pivot for security in IIoT is trusted identities of the participating subsystems. Common IT security approaches are often too costly and inflexible. Furthermore, mechanisms to disseminate trusted information and to ensure the authenticity of data and transactions are often required. The article describes concepts for dealing with these challenges and outlines suitable solutions as well as approaches from current research for the special features in the IIoT.

The topic Internet of Things (IoT) is omnipresent and has already found its way into many living rooms under the term Smart Home [1]. Be it lamps, heaters or the television set - everything is interconnected and easy to control from anywhere. The corresponding counterpart in manufacturing is Industry 4.0, sometimes named the Industrial Internet of Things. Companies and research institutions are working intensively on the possibilities offered by the so-called fourth industrial revolution. The forward-looking positioning of the German economy in international competition requires that the development and distribution of concepts and solutions in the context of Industry 4.0 be actively shaped by the industry itself and thereby to play a pioneering role in innovation [2]. Due to the rapid pace of development, it is essential for German companies to identify, develop and adapt new methods and technologies at an early stage and to transform them into marketable solutions.

The Industrial Internet of Things (IIoT) radically changes value creation in the manufacturing industry. Dynamic environmental factors, technical complexity, and limited resources cause many companies to be left behind. As a principle borrowed from strategic planning, business ecosystems can provide new ways of interorganizational collaboration and thus dynamize efforts to internalize specific IIoT knowledge. Especially in an early phase of trend scouting and idea generation, ecosystems can be seen as a highly effective venturing concept.

The core task of logistics can be described by the so-called “seven R”. IT means that the right product must be available in the right condition at the right time and at the right place. In addition, further key factors of a well-functioning logistics system are the right quantity, the right costs and the right information. Providing right or incorrect information at the wrong time can have a significant repercussion on the entire process. In particular, the human being who is the recipient of the information has the demanding task of correctly select and interpret it. Therefore, the human-machine interface plays a decisive role in the communication success between human employees and the digital systems surrounding the human at work. On that account, new user interfaces that do not burden people and provide information in a way that it can be perceived intuitively are needed in the future. This is where data glasses come into play.

A core element of Industry 4.0 and IoT is the ability to collect extremely high volumes of data in real time. [1] In order to achieve a benefit for business process optimizations, it is necessary to generate knowledge from this data. For this, conditions must be created with regard to infrastructure and organization. This article describes how to use a knowledge discovery process model in production logistics in combination with the Acatech-I-4.0 reference model to derive a procedure that uses data analytics to systematically exploit the benefits of I-4.0 in business processes.