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Artificial Intelligence & Agile Innovation: Case of Moroccan Logistics Companies.

Authors :
Bouanba, Nouhayla
Barakat, Ouafa
Bendou, Abdelaziz
Source :
Procedia Computer Science; 2022, Vol. 203, p444-449, 6p
Publication Year :
2022

Abstract

For over a decade, artificial intelligence (AI) has been accelerating in its development and adoption. It allows people, and therefore companies, to be more productive and cost-effective, and ensures the sustainability and growth of the firm. The logistics industry is already using artificial intelligence, especially for multi-criteria route optimization. AI makes it easier to extract useful information from customer interaction, and also relevant patterns from the mass of information coming from the Internet of Things. In fact, artificial intelligence as an agile innovation has several benefits for the logistics sector. So, the rapid pace of technological evolution experienced by our society requires speed, responsiveness and an ability to understand the systemic dimension of challenges, whatever the sector or the type of organizations and actors involved, which is why companies started embracing agility through AI technologies. It is in this perspective that the present research is conducted, seeking to understand the extent to which AI can be qualified as an agile innovation that can optimize supply chain performance in Moroccan logistics companies. To this end, a qualitative methodological approach of an exploratory nature was adopted. To this end, fifteen semi-structured interviews will be conducted with logistics and supply chain managers in Moroccan companies, and data analysis will be conducted using the IRAMUTEQ analysis software. Our findings show that the majority of managers interviewed don't follow an AI strategy within their company due to many reasons, mainly their low budget and the lack of skills. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
18770509
Volume :
203
Database :
Supplemental Index
Journal :
Procedia Computer Science
Publication Type :
Academic Journal
Accession number :
158514021
Full Text :
https://doi.org/10.1016/j.procs.2022.07.059