Automation

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.

The performance of assistance systems, especially in the automotive sector, has become an unique selling point. The trend toward Autonomous driving represents the expected impact of innovation resulting from the exploitation of the latest technologies. Besides autonomous driving, other areas of application for autonomous systems could trigger social change - the prime example being industrial production. The following article presents a planning approach tailored to the complex engineering task of planning and designing autonomous systems for industrial applications.

Recently, implementation procedures of automatic production, digitalization and Industrial Internet of Things technologies (IIoT) play an increasing role in industrial manufacturing processes. Subsequently, the competence requirements for employees change. These changes cannot be anticipated by traditional learning approaches. The following contribution faces this challenge and will show a new integrated learning factory approach which combines the application of new technologies with a flexible production environment. Thus establishing production surroundings that are familiar to the learner. The contribution demonstrates this approach using a quality control process in the context of logistics.