Automation

Remanufacturing, previously characterized by manual and cost-intensive processes, is a critical step on the way to a resource-efficient circular economy. Industry and research agree that the introduction of Industry 4.0 technologies is the key to the development of automated and economical remanufacturing systems. Based on a systematic literature review, this paper is dedicated to the analysis of promising Industry 4.0 approaches with a focus on the overall process as well as the sub-processes of disassembly and inspection. The results suggest that there is a need for additional knowledge, experience and research in the development and real demonstration of the approaches and their transferability to broader application fields.

Smart Factory is the vision of a production environment in which manufacturing plants and logistics systems organize themselves as far as possible without human intervention. The article describes a project, at the start of which none of the participants created a relation to “Smart Factory” or “Industry 4.0”. Rather, the objective was to drastically reduce the current delivery time of 6-8 weeks. The result is a completely digitized business process from order creation, product development, design, manufacturing as well as processing for “batch size 1” with a reduction in lead time to less than 10 %.

The construction industry has taken first steps towards digitalized processes with the use of Building Information Modeling (BIM) systems and the modelling of processes. However, there are few successful examples of IT-supported processes in the largely manual construction phase. This article provides insights from a practical study, which examined the implementation of a workflow management system as a potential next step.

The development of Industry 4.0 changes the role of humans in operations systems. In sociotechnical systems, there is ongoing interaction between humans and technology, impacting human life and work. However, human factors are broadly ignored in research on Industry 4.0 technologies and implementation. In this work, a process model is described that supports the evaluation of the impact of a technology implementation on human factors and performance indicators. This can avoid negative consequences for employees as well as phantom profits and can contribute to a successful digital transformation.

In process engineering, one thinks of production operations that are controlled or regulated by sensors and actuators. And any realization of matter transformation is based on a physical substratum, which holds equally for living systems and their behaviour. The article distinguishes between three system levels: the functional level, the interface to the environment and the cognitive level of. Using these three levels, the learning cycle or the previous Cognitive Loop can be very well illustrated. If one compares with this way of distinction the Bio-Informatization of human intelligence with the technical development stages of mechanization, automation, regulation and deep learning, then the cybernetic-sociological term “operational closure” becomes understandable. It becomes obvious that in the context of a digitized culture of production and organization, we should be prepared for a new kind of cognitive loop based on silicon (SI), an intelligent system behavior via ...

Media disruption interferes with consistent and universal digitalization. Data is easily lost, time and resources wasted. Heterogeneous and isolated applications produce partial relief; however they fail to integrate redundant information from separately operated systems into a homogeneously processible data mass. The employment of a centralized data hub proposes a strategy to effectively advance digitalization in process management, connecting scheduling of involved parties, defect tracking and progress processes. It also automatizes reportings on project progress.

When digitalising and automating work processes, it is often overlooked that this can trigger serious changes for the organisation. This article shows that such changes can lead to an incongruence between “what an organization does” (practice), “what it can do” (knowledge) and “who it is” (identity). These incongruities must be overcome in order to implement change successfully. If managers are aware of this, many problems such as the collapse of existing routines, knowledge gaps or the departure of important employees can be foreseen and solved.

Digitization in engineering promises automated workflows, higher speed and lower costs in the development of automation solutions. The prerequisite for this is not only modularization based on a structured description language, but also uniform, interdependent modeling that ensures automated data exchange across system boundaries. In order to achieve a broad application, the underlying ontology should be based on existing norms and standards and be available in open source applications. However, the collaborative and consistent development of such an ontology requires a structured, methodical procedure and an associated modelling map that serves as an orientation for standardized, work-sharing modelling. A possible approach for the required procedure model and the related map will be presented in this article and validated using AutomationML. The presented approach should point out a possible direction and stimulate further process-controlled modelling efforts of ontologies.