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Addressing uncertainties in complex manufacturing environments: A multidisciplinary approach

 
: Dhiman, Hitesh; Plewe, Daniela Alina; Röcker, Carsten

:

Karwowski, W.:
Advances in Manufacturing, Production Management and Process Control : Joint proceedings of the AHFE 2018 International Conference on Advanced Production Management and Process Control, the AHFE International Conference on Human Aspects of Advanced Manufacturing, and the AHFE International Conference on Additive Manufacturing, Modeling Systems and 3D Prototyping, July 21-25, 2018, Loews Sapphire Falls Resort at Universal Studios, Orlando, Florida, USA
Cham: Springer International Publishing, 2019 (Advances in Intelligent Systems and Computing 793)
ISBN: 978-3-319-94195-0 (Print)
ISBN: 978-3-319-94196-7 (Online)
ISBN: 3-319-94195-X
S.103-114
International Conference on Advanced Production Management and Process Control <2018, Orlando/Fla.>
International Conference on Human Aspects of Advanced Manufacturing <2018, Orlando/Fla.>
International Conference on Additive Manufacturing, Modeling Systems and 3D Prototyping <2018, Orlando/Fla.>
International Conference on Applied Human Factors and Ergonomics (AHFE) <9, 2018, Orlando/Fla.>
Englisch
Konferenzbeitrag
Fraunhofer IOSB ()

Abstract
With the introduction of intelligent and autonomous systems into factory environments, workplaces where human employees work alongside digital counterparts will become increasingly informational. We develop a generic framework for hypothetical workplaces to investigate how complexities create to uncertainties. Complexity may be explained through the Level of Abstractions used to model a system, and it is encountered in its dynamic form as an alteration of information flow between agents in a phenomenological relationship. Analyzing these systems as ‘information flows’ brings to light the uncertainity(ies) the workers of the future will have to cope with. We develop first concepts that can be used to develop heuristics to manage these uncertainties in complex manufacturing environments. These heuristics may also be useful in creating optimized workplaces that combine the individual abilities of both humans and machines. The framework proposed in this paper may be subject for an empirical validation of these heuristics in the future.

: http://publica.fraunhofer.de/dokumente/N-506787.html