Abstract
Smart assistants in manufacturing can guide and aid on decision-making while also provide means to collect additional insights and information available to the users. A general approach for building a smart assistant that provides users with machine learning forecasts and a sequence of decision-making options is presented in this work. The system provides means for knowledge acquisition by gathering data from users. To minimize interactions and friction with users, we envision active learning can be used to get data labels for most data instances expected to be most informative. The system is demonstrated on a demand forecasting use case in manufacturing. The methodology can be extended to several use cases in manufacturing.
P. Zajec and J. M. Rozanec—Equal contribution.
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Notes
- 1.
A video demonstrating the application is available at https://youtu.be/Kx5UnE_yTM0.
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Acknowledgemnts
This work was supported by the Slovenian Research Agency and the European Union’s Horizon 2020 program projects FACTLOG under grant agreement H2020-869951 and STAR under grant agreement number H2020-956573.
This document is the property of the STAR consortium and shall not be distributed or reproduced without the formal approval of the STAR Management Committee. The content of this report reflects only the authors’ view. The European Commission is not responsible for any use that may be made of the information it contains.
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Zajec, P., Rožanec, J.M., Novalija, I., Fortuna, B., Mladenić, D., Kenda, K. (2021). Towards Active Learning Based Smart Assistant for Manufacturing. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 633. Springer, Cham. https://doi.org/10.1007/978-3-030-85910-7_31
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DOI: https://doi.org/10.1007/978-3-030-85910-7_31
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