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Technicians and service operators in machine and plant engineering need more and more information to assemble the more complex products. Additional effort is necessary, when technicians have to document their work progress or report problems. One reason for this is that production is generally not integrated into the digital flow of information. Case studies from the industry show the high potential of innovative software solutions by integrating the production perfectly into the digital information flow.

Technological progress offers opportunities for introducing new business models but also opens new paths for attackers. Security-by-design integrates factory security from the start and introduces a continuous security process built upon a variety of components. This article illustrates some of these components by highlighting secure processes with specific case studies: anomaly detection within value networks and secure production planning with simulation.

The popularity of the Cloud rises, but there is still much insecurity in the IT-Security of a cloud solution. Companies want to be able to check, if the processes in the cloud are secure and trustworthy. This is possible with an external auditing system, which compares measured data with predefined rules. But there is a chance, that the measured data are manipulated on its way to the auditing system, before leaving the cloud. To prevent these threads, the measured data must be protected right after it is generated. This is possible with the forward integrity method, which builds a chain-of-trust for the measured data without need of a TPM.

Securing the IoT poses specific challenges which are still only partially solved. These include the often very limited computing, storage, and energy resources of wirelessly connected IoT devices, the high demands on the user-friendliness, as well as the high-cost pressure. Upon this background, the research project PROPHYLAXE (“Providing Physical Layer Security for the Internet of Things”), funded by the BMBF, pursued a novel approach in which the physical properties of the wireless transmission link between two devices are exploited. Based on these properties, cryptographic keys can be generated that are known only to the communicating devices and can be used to secure and authenticate a connection.

In the project CrimpProd-S the integration of decentralized, self-learning production control systems in the context of Industry 4.0 in combination with additive manufacturing is investigated. The overall target is the economic evolutionary integration of lot size 1 in existing production facilities. Therefore a procedure model and customized methods are developed to analyze the potentials regarding the existing company’s strategy, value chain as well as the business model. This integrated procedure guarantees the efficient and customer-oriented transformation.

Due to the advancement of robotics in industrial production, the strict separation of workspaces is gradually dissolving (HRC - human-robot collaboration). HRC combines the strength and efficiency of robots with the skills and cognitive abilities of humans through seamless cooperation [1]. Manufacturing companies, in which HRC scenarios are to be implemented, require a transparent and reflective selection of technology with regard to safeguarding the worker against hazards posed by the robot. To address this challenge, this paper proposes a process model that helps technology developers and systems integrators select appropriate technologies and solutions for HRC scenarios.

Solid forming companies are always faced with the challenge of producing high-quality products that meet the strict requirements of the customers. The quality has to be reproducible despite fluctuations occurring along the value chain. In order to meet the requirements, solutions for an improved process stability and quality are needed. This paper presents a data-driven approach that aims to adapt quality fluctuations. Thus, process data is modeled in-memory in a multidimensional database. Based on the results of an online analytical processing, the process is controlled in real-time.

The Increasing digitization forces companies to deal with their opportunities and challenges. The study underlying this article identifies the economic potential of technological trends and provides an inventory of the current situation in Industry 4.0. To this end, a total of 106 industrial companies have made a self-assessment of their current maturity levels of various technologies in their company and their current and future economic significance. This results in valuable insights for companies in the manufacturing industry, so that they can better deal with the range of different approaches and concepts.

Due to shorter product life cycles, increasing product variants and shrinking lot sizes, manufacturing companies are expected to produce according to frequently modified product specifications. In order to enable this, the available manufacturing machines have to be checked whether they can cope with such changing requirements. A computer-readable skill description of a machine can be used for an automated matching of changing product requirements. Accordingly, this contribution presents a product-oriented description approach of manufacturing machine skills. The application of the concept is demonstrated by an example of a drilling station.

There are numerous analysis methods available to support engineers working on continuous improvement projects. Digital transformation facilitates to reduce the effort for data acquisition and processing. The Institute of Production Management and Technology and the medical company Dräger have jointly developed a web application for multi-method analysis. This article describes its data structure and technology.