Industry 4.0

Current advances result in increasingly complex production programs. Through combination of additive manufacturing and Industry 4.0, new elements can be formed and - as a whole - enable to economically manufacture the above mentioned programs. The Bionic Smart Factory 4.0 provides a framework, structuring them in terms of relation and interaction. Their development and implementation is being promoted through their evaluation against the determinants of complex production programs.

Against the background of latest developments such as Cyber-Physical Systems and Internet of Things, manufacturers need to adapt their business models. This digital transformation is often associated with implementation challenges, even for market leaders. It will require further efforts and time before customers will benefit from these new trends. This article highlights the implementation reality in German and Swiss mechanical engineering companies, based on a multi-case study with examples from pharma, packaging and construction industry.

As a result of digitalization, the dynamics and complexity of our work environment are continuously expanding, facing employees with increasing man-machine interaction and constantly changing work activities. This technical progress might introduces us to new or altered psychological strains for employees. Results of half-structured interviews show that digitalization leads directly and indirectly to new psychological strains. In order to prevent potential health consequences, it is crucial to consider new dimensions such as flexibility and monitoring in the work place risk assessment.

Shaping intelligent production environments is one core element of Industry 4.0, but has progressed differently in the various branches. While concepts are well advanced in automotive, SMEs within textile industry still need assistance. As demands on individualization and responsiveness are increasing, a Smart Factory for textile SMEs must be designed. The futureTEX consortium therefor is working on shaping a textile Smart Factory and implements it prototypically by using demonstrators.

In the context of this article, a methodology for an efficient transfer of knowledge from research into industrial practice regarding cyber-physical production systems is presented. The methodology serves above all to sensitize small and medium-sized (SME) enterprises to the possible potentials of the so-called Industry 4.0. The starting point for this is the need-oriented and individual specification of knowledge required for a practical knowledge transfer and the development of tailor-made competence profiles of future employees in smart SMEs

Recent advancements in cyber physical systems (CPS) and industry 4.0 concepts are expected to result in a disruptive change of business processes in industry and commerce. In particular, this refers also to supply chain management (SCM) and logistics systems and processes. Methodically, maturity models can be used to determine the maturity level of SCM and logistics organisations. In this paper we present an extension for a maturity model being able to check the industry 4.0 compatibility of SCM systems and processes. Moreover, the aim is to provide a tool supporting the transformation towards SCM 4.0-ready systems and processes. The requirements for the digital transformation process are described and important fields of actions are discussed.

The Smart Factory concept describes the extensively networked production of industry 4.0, which affects the entire life cycle of a factory and, in particular, factory planning and factory operation. Both classic and more up-to-date factory planning approaches come to their limits through the new requirements. This paper identifies the requirements that are important for the future structural planning of factories and presents the need for a holistic virtual validation of the factory structure. Furthermore, a methodological approach is addressed for the solution of the challenges.

The Industrial Data Space enables business based on networked data and data sovereignty at the same time. The technological solution and the certification scheme of the Industrial Data Space ensures secure access to data which companies usually do not dare to share today. This gives new opportunities and provides new data-driven business models. The Industrial Data Space® initiative consists of a research project owned by the Fraunhofer-Gesellschaft and the Industrial Date Space Association.

Predictive, or condition-based, maintenance is a shining goal for industrial customers and the Industrie 4.0 initiative. Supplementing, or even replacing, inefficient schedule-based maintenance with software promises to reduce equipment failures and unplanned downtime, improve safety and ultimately help the bottom line in an era of unstable prices and uncertain budgets. Predictive maintenance might be taking longer than expected, but in the next five years you might see it move faster than expected.

Currently the implementation of digital technologies in response to important competition requirements is promoted in many places. Consequently, the working environment of employees in operative logistics is going to change significantly. This article provides an overview of the possible uses of future-oriented technologies in different logistics processes as well as the thereby changing subtasks and competence requirements of operative employees in the working world 4.0.