A systematic review of machine learning in logistics and supply chain management: current trends and future directions

Akbari, Mohammadreza, and Do, Thu Nguyen Anh (2021) A systematic review of machine learning in logistics and supply chain management: current trends and future directions. Benchmarking, 28 (10). pp. 2977-3005.

[img] PDF (Published Version) - Published Version
Restricted to Repository staff only

View at Publisher Website: https://doi.org/10.1108/BIJ-10-2020-0514
 
7


Abstract

Purpose – This paper presents a review of the existing state-of-the-art literature on machine learning (ML) in logistics and supply chain management (LSCM) by analyzing the current literature, contemporary concepts, data and gaps and suggesting potential topics for future research.

Design/methodology/approach – A systematic/structured literature review in the subject discipline and a bibliometric analysis were organized. Information regarding industry involvement, geographic location, research design and methods, data analysis techniques, university, affiliation, publishers, authors, year of publications is documented. A wide collection of eight databases from 1994 to 2019 were explored using the keywords “Machine Learning” and “Logistics“, “Transportation” and “Supply Chain” in the title and/or abstract. A total of 110 articles were found, and information on a chain of variables was gathered.

Findings – Over the last few decades, the application of emerging technologies has attracted significant interest all around the world. Analysis of the collected data shows that only nine literature reviews have been published in this area. Further, key findings show that 53.8 per cent of publications were closely clustered on transportation and manufacturing industries and 54.7 per cent were centred on mathematical models and simulations. Neural network is applied in 22 papers as their exclusive algorithms. Finally, the main focuses of the current literature are on prediction and optimization, where detection is contributed by only seven articles.

Research limitations/implications – This review is limited to examining only academic sources available from Scopus, Elsevier, Web of Science, Emerald, JSTOR, SAGE, Springer, Taylor and Francis and Wiley which contain the words “Machine Learning” and “Logistics“,“Transportation” and “Supply Chain”in the title and/or abstract.

Originality/value – This paper provides a systematic insight into research trends in ML in both logistics and the supply chain.

Item ID: 73088
Item Type: Article (Research - C1)
ISSN: 1758-4094
Keywords: Structured literature review, Artificial intelligence, Machine learning, Logistics, Supply chain management, Transportation, Emerging technologies
Copyright Information: © Emerald Publishing Limited.
Date Deposited: 30 May 2022 23:23
FoR Codes: 35 COMMERCE, MANAGEMENT, TOURISM AND SERVICES > 3509 Transportation, logistics and supply chains > 350903 Logistics @ 40%
35 COMMERCE, MANAGEMENT, TOURISM AND SERVICES > 3509 Transportation, logistics and supply chains > 350909 Supply chains @ 50%
46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 10%
SEO Codes: 24 MANUFACTURING > 2412 Machinery and equipment > 241202 Autonomous and robotic systems @ 30%
24 MANUFACTURING > 2415 Transport equipment > 241599 Transport equipment not elsewhere classified @ 40%
28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280106 Expanding knowledge in commerce, management, tourism and services @ 30%
Downloads: Total: 7
Last 12 Months: 1
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page