Context (archaeology)

Engineering

Interconnectivity

Pure mathematics

Biology

Automation

Industry 4.0

Embedded system

Field (mathematics)

Computer science

Paleontology

Mechanical engineering

Mathematics

Artificial intelligence

Deep learning

Knowledge management

Engineering management

Sustainability

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.

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