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.
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