A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Manuel Woschank, Erwin Rauch and Helmut Zsifkovits
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Manuel Woschank: Chair of Industrial Logistics, Montanuniversitaet Leoben, 8700 Leoben, Austria
Erwin Rauch: Industrial Engineering and Automation (IEA), Faculty of Science and Technology, Free University of Bozen-Bolzano, 39100 Bolzano, Italy
Helmut Zsifkovits: Chair of Industrial Logistics, Montanuniversitaet Leoben, 8700 Leoben, Austria

Sustainability, 2020, vol. 12, issue 9, 1-23

Abstract: Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

Keywords: industry 4.0; artificial intelligence; machine learning; deep learning; smart logistics; logistics 4.0 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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