Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Digital Technologies Selection under Hesitant Fuzzy Information: The Case of the Automotive Sector

Maryam Gallab
* ORCID logo ,
Youssef Lamrani
,
Hafida Bouloiz
,
Mario Di Nardo
ORCID logo ,
Mohamed Tkiouat
,
Sara Jebbor
,
Adil Elfakir
Version 1 : Received: 21 July 2023 / Approved: 24 July 2023 / Online: 25 July 2023 (03:12:44 CEST)

How to cite: Gallab, M.; Lamrani, Y.; Bouloiz, H.; Di Nardo, M.; Tkiouat, M.; Jebbor, S.; Elfakir, A. Digital Technologies Selection under Hesitant Fuzzy Information: The Case of the Automotive Sector. Preprints 2023, 2023071633. https://doi.org/10.20944/preprints202307.1633.v1 Gallab, M.; Lamrani, Y.; Bouloiz, H.; Di Nardo, M.; Tkiouat, M.; Jebbor, S.; Elfakir, A. Digital Technologies Selection under Hesitant Fuzzy Information: The Case of the Automotive Sector. Preprints 2023, 2023071633. https://doi.org/10.20944/preprints202307.1633.v1

Abstract

With advances in information technology, big data, mobile communications, and robotics, digital technologies are increasingly being used in factories around the world. This digital transformation is named industry 4.0. Today, industrial companies are looking at how to adopt this era and implement these technologies 4.0 while improving their performance and generating more profits. The objective of this paper is to help companies to better choose the appropriate digital technologies according to their activities using a multi-experts-multi-criteria decision-making approach under hesitant fuzzy information. The proposed model is a generic model based on Multi-Agent Systems allowing to have an idea of the parameters necessary to apply the adopted approach. The adopted approach allows a better representation of uncertainty and subjectivity of experts’ judgments. It would be of great interest, especially, when exact quantitative data are not available. A real case company example is exposed (automotive company) towards putting into practice the proposed approach.

Keywords

Industry 4.0; Digital Technologies; MCDM Approach; Hesitant fuzzy set; uncertainty management

Subject

Engineering, Industrial and Manufacturing Engineering

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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