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Authors: Jaqueline Coelho 1 ; 2 ; Guilherme Bispo 1 ; 2 ; Guilherme Vergara 3 ; 1 ; 4 ; Gabriela Saiki 1 ; 2 ; André Serrano 3 ; 1 ; 5 ; Li Weigang 3 ; 1 ; 2 ; Clovis Neumann 3 ; 1 ; 5 ; Patricia Martins 1 ; 6 ; Welber Santos de Oliveira 3 ; 1 ; 4 ; Angela Albarello 1 ; 2 ; Ricardo Casonatto 1 ; 5 ; Patrícia Missel 3 ; 1 ; Roberto Medeiros Junior 7 ; 8 ; Jefferson Gomes 7 ; 8 ; Carlos Rosano-Peña 9 and Caroline F. da Costa 7 ; 8

Affiliations: 1 Projectum, Research Group for Math. Methodologies Applied to Management, University of Brasilia, Brasilia, Brazil ; 2 Department of Computer Science, University of Brasilia, Federal District, Brazil ; 3 Latitude - Decision Making Technologies Lab, University of Brasilia, Campus Darcy Ribeiro, Brasilia, Brazil ; 4 Department of Electrical, University of Brasilia, Federal District, Brazil ; 5 Department of Production Engineering, Faculty of Technology, University of Brasília, Federal District, Brazil ; 6 Department of Economics, University of Brasília, Federal District, Brazil ; 7 SENAI, National Service of Industrial Learning, Federal District, Brazil ; 8 UNITEC, Innovation, and Technology Unit, National Service of Industrial Learning, Federal District, Brazil ; 9 Department of Administration, University of Brasilia, Federal District, Brazil

Keyword(s): Artificial Intelligence, Automatic Classifier, Innovation, Productivity in Industry, Sustainability, SLR.

Abstract: The advent of Artificial Intelligence (AI) has opened up new possibilities for improving productivity in various industry sectors. In this paper, we propose a novel framework aimed at optimizing systematic literature reviews (SLRs) for industrial productivity. By combining traditional keyword selection methods with AI-driven classification techniques, we streamline the review process, making it more efficient. Leveraging advanced natural language processing (NLP) approaches, we identify six key sectors for optimization, thereby reducing workload in less relevant areas and enhancing the efficiency of SLRs. This approach helps conserve valuable time and resources in scientific research. Additionally, we implemented four machine learning models for category classification, achieving an impressive accuracy rate of over 75%. The results of our analyses demonstrate a promising pathway for future automation and refinements to boost productivity in the industry.

CC BY-NC-ND 4.0

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Paper citation in several formats:
Coelho, J.; Bispo, G.; Vergara, G.; Saiki, G.; Serrano, A.; Weigang, L.; Neumann, C.; Martins, P.; Santos de Oliveira, W.; Albarello, A.; Casonatto, R.; Missel, P.; Medeiros Junior, R.; Gomes, J.; Rosano-Peña, C. and F. da Costa, C. (2023). Enhancing Industrial Productivity Through AI-Driven Systematic Literature Reviews. In Proceedings of the 19th International Conference on Web Information Systems and Technologies - WEBIST; ISBN 978-989-758-672-9; ISSN 2184-3252, SciTePress, pages 472-479. DOI: 10.5220/0012235000003584

@conference{webist23,
author={Jaqueline Coelho. and Guilherme Bispo. and Guilherme Vergara. and Gabriela Saiki. and André Serrano. and Li Weigang. and Clovis Neumann. and Patricia Martins. and Welber {Santos de Oliveira}. and Angela Albarello. and Ricardo Casonatto. and Patrícia Missel. and Roberto {Medeiros Junior}. and Jefferson Gomes. and Carlos Rosano{-}Peña. and Caroline {F. da Costa}.},
title={Enhancing Industrial Productivity Through AI-Driven Systematic Literature Reviews},
booktitle={Proceedings of the 19th International Conference on Web Information Systems and Technologies - WEBIST},
year={2023},
pages={472-479},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012235000003584},
isbn={978-989-758-672-9},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Web Information Systems and Technologies - WEBIST
TI - Enhancing Industrial Productivity Through AI-Driven Systematic Literature Reviews
SN - 978-989-758-672-9
IS - 2184-3252
AU - Coelho, J.
AU - Bispo, G.
AU - Vergara, G.
AU - Saiki, G.
AU - Serrano, A.
AU - Weigang, L.
AU - Neumann, C.
AU - Martins, P.
AU - Santos de Oliveira, W.
AU - Albarello, A.
AU - Casonatto, R.
AU - Missel, P.
AU - Medeiros Junior, R.
AU - Gomes, J.
AU - Rosano-Peña, C.
AU - F. da Costa, C.
PY - 2023
SP - 472
EP - 479
DO - 10.5220/0012235000003584
PB - SciTePress