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

Purpose: The purpose of this paper is to provide a structured literature review (SLR) and systematic insight into logistics outsourcing research, in order to identify gaps in knowledge, and provide future research direction in the logistics outsourcing discipline.Design/methodology/approach: This paper identifies and synthesises information from academic journals and examines research designs and methods, data analysis techniques, geographic locations, industry engagements, year of publication, publishers, university and author affiliations. A collection of online databases from 1991 to 2016 were explored, using the keywords “third-party”, “logistics” and “outsourcing” in their title and/or abstract, to deliver an inclusive listing of journal articles in this discipline area. Based on this approach, a total of 263 articles were found and data were derived from a succession of variables.Findings: There has been a significant increase and concentration by researchers over the past 25 years focussing on logistics outsourcing. A need to implement best practice has been universally identified, owing to increasing global supply chain complexity. This analysis shows that only seven literature reviews were published in the logistics outsourcing discipline. Additionally, this study revealed that 42 per cent of the work in this field was found to be survey based and this trend in research indicates the development of the structural equation modelling and multi-criterion decision-making methods. Investigations between 2010–2012 and 2015–2016 accounted for 151 of the total 263 articles published. Finally, reverse logistics is an area that requires special attention.Research limitations/implications: This paper is limited to a review of academic articles obtainable from online databases, containing the words “logistics” and “outsourcing” in the title and/or Additionally, only papers from high quality, peer-reviewed journals were evaluated. Other academic sources such as books and conference papers were not included in this study.Originality/value: This review will provide an increased understanding of the existing state of current research, trends and future research directions in the logistics outsourcing discipline.

Purpose: The purpose of the article is to contribute to the body of research on digital transformation among members of the supply chain operating in an emerging economy. This paper researches the digital transformation trends happening across Vietnamese supply chains, by investigating the current adoption rates, predicted impact levels and financial investments being made in key Industry 4.0 technologies.Design/methodology/approach: By using a semi-structured online survey, the experiences of 281 supply chain professionals in Vietnam were captured. Subsequently, statistical techniques examining variances in means, regression analysis and Monte Carlo simulation were applied.Findings: The findings of this study offer a comprehensive understanding of Industry 4.0 technology in Vietnam, highlighting the prevalent technologies being prioritized. Big data analytics and the Internet of things are expected to have the most substantial impact on businesses over the next 5–10 years and have received the most financial investment. Conversely, Blockchain is perceived as having less potential for future investment. The study further identifies several technological synergies, such as combining advanced robotics, artificial intelligence and the Internet of things to build effective and flexible factories, that can lead to more comprehensive solutions. It also extends diffusion of innovation theory, encompassing investment and impact considerations.Originality/value: This study offers valuable insights into the impact and financial investment in Industry 4.0 technologies by Vietnamese supply chain firms. It provides a theoretical contribution via an extension of the diffusion of innovation theory and contributes toward a better understanding of the current Industry 4.0 landscape in developing economies. The findings have significant implications for future managerial decision-making, on the impact, viability and resourcing needs when undertaking digital transformation.

Blockchain (BC) applications in supply chain management (SCM) have recently received extensive attention. It is important to synthesise the extant literature on the field to identify key research themes and navigate potential future directions. This study thus develops an efficient, scalable data-driven review approach that uses text mining and Latent Dirichlet Allocation (LDA)-based topic modelling for automatic content analysis of full-text documents. Our method overcomes the drawbacks of traditional systematic literature reviews using either manual coding or bibliographic analysis for article classifications, which are highly time-consuming and biased when dealing with large amounts of text. 108 papers published between 2017 and 2022 were analysed which identified 10 key research themes, including revenue management, sustainability, traceability, manufacturing system, scheduling in cloud manufacturing, healthcare SCM, anti-counterfeit system, logistics and transportation, system architecture development, and food & agriculture SC. Five future directions are then suggested, including (1) integration of BC and other emerging technologies for global and scalable SCM, (2) crypto-X applications in SCM, (3) BC-enabled closed-loop SCM, (4) the environmental and social impacts of BC-based SCM and (5) decentralised autonomous organisations in SCM.