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Inadequate communication with media discontinuities makes it difficult to implement simulataneous engineering. Missing process data prevent data-based control measures and the layout of design adaptations to unfinished components. Therefore, the subject of this article is the description of a system for real-time data acquisition and the digitalization of the information flow in shipbuilding.

In order to cope with the complexity and automation of cyberphysical production systems (CPPS), knowledge-based maintenance (KBM) strategies and models have been identified as a key factor. They are intended to secure and improve machine availability and process stability. Although many companies are willing to invest in these innovations, they lack the certainty of having the necessary competence and capacity. In order to overcome this problem, the authors present a holistic process model for the evaluation and identification of strengths and weaknesses on an operational, tactical and strategic level by applying a multidimensional analytical approach.

Digitization of industrial production processes allows the introduction of datadriven business models using Cyber Physical Systems (CPS) and Industrial Internet of Things (IIoT). To ensure efficient manufacturing, production systems must be able to communicate and interact with their environment, to monitor wear conditions, and to selfadapt their behavior to a given situation. This article gives an overview about the historical development of intelligent production systems with focus on condition monitoring and predictive maintenance in an availability oriented business model.

Today, manufacturing facilities are designed for mass-producing identical goods. Although they often have a certain flexibility, they are not fully changeable. Changes are associated with a high cost. Changeability allows producers to react more quickly to changing demand situations and to efficiently produce small quantities. The Digital Twin is a key concept for implementing the required changeability. In the reference project BaSys 4.0, funded by the German Federal Ministry of Education and Research (BMBF), we have developed a manufacturing concept that enables changeable production. Our open source middleware Eclipse BaSyx provides a reference implementation of the BaSys 4.0 concepts.

By definition, a digital twin is the “electronic image or mirror of a real or virtual object, e. g. a product or a logical organization”. This mirror image of an organization is useful for visibility and control, the feasible simulations, however, enable better planning und prediction. In fact, digital twin can be used for predictive maintenance, e. g. of trains or planes, and for security aspects. The digital twin of an organization (DTO) is already feasible by using an appropriate SaaS solution that builds a mirror image of an enterprise etc., establishes all processes and communication lines. It enables the management to simulate the benefits and risks of changes to the original, e. g. by creating a new business model.

The automatic identification of workpieces is a compulsory step towards the Digital Twin and for a consistent life cycle file of products. Because different technologies exist, companies often face the challenge to find the right solution which meets their requirements. Especially the high level of prominence of some technologies lead to premature decisions without a profound selection process. This in turn might lead to sub-optimal solutions in the end. In order to support especially industrial companies in this process, a selection algorithm for Auto-ID technologies was developed as part of an industrial project at the department Factory Planning and Factory Management of TU Chemnitz. The selection process has been implemented in a Microsoft Excel-based tool.

The article presents an approach which supports the assessment of the economic effectiveness of measures during the development towards CPPS through a holistic simulation approach. This is based on the mapping of the various interdependencies between the dimensions technology, organisation and personnel according to the socio-technical approach. From the validation of the approach conclusions for the further development of the concept are drawn.

The phase-out of nuclear energy decided by the politicians and the goal of significantly aligning the energy mix with renewable energies will give the industry great growth potential. Digitalization and the resulting technologies, such as sensors, robotics and assistance systems, artificial intelligence, virtual reality and augmented reality, are helping companies realise their potential. The study “Industrialization of the Wind Industry” by the Technical University of Munich has shown that digitalization will have a positive effect on the “Levelized Cost of Energy” (LCOE).

The complexity of the production process and the higher demand for quality and individualization leads to structural changes and challenges in the production process. The analysis of interdependencies of Lean Production Systems as an industrial standard und Industrie 4.0 is able to determine new spaces of action and alternative solutions to optimize the production processes. The analysis shows that the technologies and systems of Industrie 4.0 does not compete but rather support the principles of Lean Production Systems.

The digital transformation of production logistics processes promises great potentials for increasing their efficiency. The processes can thus be better controlled and existing capacities better utilized. For a successful digital transformation, the quality of the information that will be collected and processed is a key factor. This paper presents a method for a systematic digital transformation of production logistics processes, with a focus on information flows and their quality.