[Zurück]

S. Thalmann, H. Gursch, J. Suschnigg, M. Gashi, H. Ennsbrunner, A. Fuchs, T. Schreck, B. Mutlu, J. Mangler, G. Kappel, C. Huemer, S. Lindstaedt:
"Cognitive Decision Support for Industrial Product Life Cycles: A Position Paper";
in: "Proceedings of the Eleventh International Conference on Advanced Cognitive Technologies and Applications (COGNITIVE 2019)", herausgegeben von: Marta Franova, Charlotte Sennersten, Jayfus T. Doswell (eds); IARIA, Venice, Italy, 2019, ISBN: 978-1-61208-705-4, S. 3 - 9.

Current trends in manufacturing lead to more intelligent
products, produced in global supply chains in shorter cycles,
taking more and complex requirements into account. To manage
this increasing complexity, cognitive decision support systems,
building on data analytic approaches and focusing on the product
life cycle, stages seem a promising approach. With two high-tech
companies (world market leader in their domains) from Austria,
we are approaching this challenge and jointly develop cognitive
decision support systems for three real world industrial use cases.
Within this position paper, we introduce our understanding of
cognitive decision support and we introduce three industrial use
cases, focusing on the requirements for cognitive decision support.
Finally, we describe our preliminary solution approach for each
use case and our next steps

Current trends in manufacturing lead to more intelligent
products, produced in global supply chains in shorter cycles,
taking more and complex requirements into account. To manage
this increasing complexity, cognitive decision support systems,
building on data analytic approaches and focusing on the product
life cycle, stages seem a promising approach. With two high-tech
companies (world market leader in their domains) from Austria,
we are approaching this challenge and jointly develop cognitive
decision support systems for three real world industrial use cases.
Within this position paper, we introduce our understanding of
cognitive decision support and we introduce three industrial use
cases, focusing on the requirements for cognitive decision support.
Finally, we describe our preliminary solution approach for each
use case and our next steps

Life Cycle, Validation, Big Data Value Chain

https://publik.tuwien.ac.at/files/publik_281823.pdf