Authors:
Stefan Schabus
1
and
Johannes Scholz
2
Affiliations:
1
University of Salzburg, Austria
;
2
Graz University of Technology, Austria
Keyword(s):
Geographic Information Science, Smart Manufacturing, Industry 4.0, Space and Time.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Industrial Automation and Robotics
;
Industrial Engineering
;
Informatics in Control, Automation and Robotics
;
Intelligent Design and Manufacturing
;
Knowledge-Based Systems
;
Performance Evaluation and Optimization
;
Production Planning, Scheduling and Control
;
Resources and Knowledge Management in Industry
;
Symbolic Systems
Abstract:
Productivity of manufacturing processes in Europe is a key issue. Therefore, smart manufacturing and
Industry 4.0 are terms that subsume innovative ways to digitally support manufacturing. Due to the fact, that
geography is currently making the step from outdoor to indoor space, the approach presented here utilizes
Geographical Information Science applied to smart manufacturing. The objective of the paper is to model an
indoor space of a production environment and to apply Geographic Information Science methods. In detail,
movement data and quality measurements are visualized and analysed using spatial-temporal analysis
techniques to compare movement and transport behaviours. Artificial neural network algorithms can support
the structured analysis of (spatial) Big Data stored in manufacturing companies. In this article, the basis for
a) GIS-based visualization and b) data analysis with self-learning algorithms, are the location and time when
and where manufacturing processes happen.
The results show that Geographic Information Science and
Technology can substantially contribute to smart manufacturing, based on two examples: data analysis with
Self Organizing Maps for human visual exploration of historically recorded data and an indoor navigation
ontology for the modelling of indoor production environments and autonomous routing of production assets.
(More)