Conference Proceeding
Refine
Year of publication
- 2021 (91) (remove)
Document Type
- Conference Proceeding (91) (remove)
Language
- English (91) (remove)
Is part of the Bibliography
- yes (91) (remove)
Keywords
Institute
Organizational Aspects of Cyber Security in Family Firms – an Empirical Study of German Companies
(2021)
Adoption of Digital Technologies in Management Accounting – an Empirical Study of German SMEs
(2021)
Adoption of Artificial Intelligence Technologies in German SMEs – Results from an Empirical Study
(2021)
One Single Click is enough – an Empirical Study on Human Threats in Family Firm Cyber Security
(2021)
Adoption of artificial intelligence technologies in German SMEs — Results from an empirical study
(2021)
Preliminary study: Polishing force measurement by viscosity - the return of ketchup polishing
(2021)
Direct Digital Manufacturing - The Role of Cost Accounting for Online Hubs to Access Industry 4.0
(2021)
Improved light scattering characterization by BSDF of automotive interior and 3D printed materials
(2021)
Design and Implementation of a Plug-In Repetitive Controller for a High Precision Axis System
(2021)
VR Live Motion Capture
(2021)
Comparison of deep learning methods for image deblurring on light optical materials microscopy data
(2021)
Impacts of the New General Data Protection Regulation for Small- and Medium-Sized Enterprises
(2021)
Early Detection of Alcohol Use Disorder Based on a Novel Machine Learning Approach Using EEG Data
(2021)
Leveraging Augmented Reality to Support Context-Aware Tasks in Alignment with Business Processes
(2021)
The seamless inclusion of Augmented Reality (AR) with Business Process Management Systems (BPMSs) for Smart Factory and Industry 4.0 processes remains a challenge. Towards this end, this paper contributes an approach integrating context-aware AR into intelligent business processes to support and guide manufacturing personnel tasks and enable live task assignment optimization and support task execution quality. Our realization extends two BPMSs (Camunda and AristaFlow) and various AR devices. Various AR capabilities are demonstrated via a simulated industrial case study.
Software models in the Unified Modeling Language (UML) can been created or automatically reverse-engineered and used for quickly gaining structural insights into larger, legacy, or unfamiliar software. But as the size, structural complexity, and interdependencies between software components in larger systems grows, two-dimensional viewing and modeling has limitations, and new ways of visualizing larger models and numerous associated diagrams of different types are needed to intuitively convey structural and relational insights. To investigate the feasibility of using Virtual Reality (VR) to create an immersive UML-based software modeling experience, this paper contributes a VR solution concept for visualizing, navigating, modeling, and interacting with software models using UML notation. An implementation shows its feasibility while an empirical evaluation highlights its potential.
Production processes in Industry 4.0 settings are usually highly automated. However, many complicated tasks, such as machine maintenance, must be executed by human workers. In current smart factories, such tasks can be supported by Augmented Reality (AR) devices. These AR tasks rely on high numbers of contextual factors like live data from machines or work safety conditions and are mostly not well integrated into the global production process. This can lead to various problems like suboptimal task assignment, over-exposure of workers to hazards like noise or heat, or delays in the production process. Current Business Process Management (BPM) Systems (BPMS) are not capable of readily taking such factors into account. There- fore, this contribution proposes a novel approach for context- integrated modeling and execution of processes with AR tasks. Our practical evaluations show that our AR Process Framework can be easily integrated with prevalent BPMS. Furthermore, we have created a comprehensive simulation scenario and our findings suggest that the application of this system can lead to various benefits, like better quality of AR task execution and cost savings regarding the overall Industry 4.0 processes.
Industry 4.0 production comprises complicated highly automated processes. However, human activities are also a crucial component of these processes, e.g., for machine main- tenance. Task assignment of human resources in this domain is challenging, as many factors have to be taken into account to ensure effective and efficient activity execution and satisfy special conditions (like worker safety). To overcome the limita- tions of current Business Process Management (BPM) Systems regarding activity resource assignment, this contribution provides a BPM-integrated approach that applies fuzzy sets for activity assignment. Our findings suggest that this approach can be easily applied to complex production scenarios, while providing efficient performance even with a large number of concurrent activity assignment requests. Additionally, our evaluation shows its potential for improved work distribution which can lead to cost savings in Industry 4.0 production processes.