A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics
Research output: Contribution to journal › Article › Research › peer-review
Standard
In: Sustainability, Vol. 12.2020, No. 9, 3760, 06.05.2020, p. 1-23.
Research output: Contribution to journal › Article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex - Download
}
RIS (suitable for import to EndNote) - Download
TY - JOUR
T1 - A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics
AU - Woschank, Manuel
AU - Rauch, Erwin
AU - Zsifkovits, Helmut
PY - 2020/5/6
Y1 - 2020/5/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85085952920&partnerID=8YFLogxK
U2 - 10.3390/su12093760
DO - 10.3390/su12093760
M3 - Article
VL - 12.2020
SP - 1
EP - 23
JO - Sustainability
JF - Sustainability
SN - 2071-1050
IS - 9
M1 - 3760
ER -
