Impact of additive manufacturing on the Vietnamese transportation industry: An exploratory study

Akbari, Mohammadreza; Ha, Nghiep

doi:10.1016/j.ajsl.2019.11.001

Cited by 31 publications

(18 citation statements)

References 57 publications

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“…Historically, supply chain management has been a data-driven process with many partners systematically involved in collecting and sharing the information to make better decisions (Akbari and Ha, 2020;Schinkus et al, 2019). In this context, several applications of information technology have been developed to provide managers with more data-driven decision-making capabilities such as business intelligence (BI) and optimization tools from the domain of operations research (OR) (Abbasi et al, 2016).…”

Section: Machine Learning In Lscmmentioning

confidence: 99%

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

Akbari

Anh

2021

BIJ

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PurposeThis 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/approachA 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.FindingsOver 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/implicationsThis 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/valueThis paper provides a systematic insight into research trends in ML in both logistics and the supply chain.

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Section: Machine Learning In Lscmmentioning

confidence: 99%

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

Akbari

Anh

2021

BIJ

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“…Although without a definitive definition (Tay et al, 2018;Tjahjono et al, 2017), there is general acceptance that I4.0 comprises a range of cutting-edge and disruptive technologies that could transform supply chains (Frederico, 2021a(Frederico, , 2021bWamba and Queiroz, 2020). Of note, are the essential eight technologies (see Figure 2) that possess greatest business impact across industries, namely, artificial intelligence (AI), blockchain, drones, Internet of Things (IoT), robotics, 3D printing, AR and VR (Akbari and Ha, 2020;PWC, 2020;Tay et al, 2018).…”

Section: Industry 40mentioning

confidence: 99%

A systematic review of AR/VR in operations and supply chain management: maturity, current trends and future directions

Akbari

Kok

2022

JGOSS

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Purpose This research aims to provide systematic insight into the current maturity of augmented reality (AR) and virtual reality (VR) in operations and supply chain management (OSCM), by analyzing the existing literature, contemporary concepts, data and gaps for future research directions. Design/methodology/approach This research uses information from existing literature regarding timeline trends, publishers, research design and method, industry engagement, geographic location, active authors and affiliated universities, concentrated topics, theories and maturity in its review. A collection of publishing databases from 1997 to 2021 were explored using the keywords “Augmented Reality,” “Virtual Reality” and “Operations” and/or “Supply Chain” in their title and abstract to examine which publications to be included. Based on the search, a total of 164 journal articles were selected, and information on a chain of variables was collected. Findings There has been a significant publication growth over the past 25 years regarding the adoption of AR/VR in OSCM. Key findings indicate that 52% of the publications were focused on manufacturing, with only 10% of the existing literature using background theories. AR/VR can be observed at the introduction and growth phase and have yet to reach their maturity. Furthermore, there is limited utilization of AR/VR as drivers in facilitating sustainable practices in OSCM by academics and practitioners, albeit a strong promise exists. Finally, the prospective applications of AR/VR toward post-COVID-19 supply chains recovery require special attention. Research limitations/implications This systematic review is limited to considering only academic articles available from Emerald, Elsevier, Taylor and Francis, Springer, Scopus, JSTOR and EBSCO containing the keyword parameters. Originality/value The study used a bibliometric review to identify the trends and maturity in the evolution of AR/VR in OSCM. This research provides a better understanding of current research practices and offers directions toward the adoption of AR/VR in OSCM.

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“…The global logistics and SCM industries are the circulatory system for traditional manufacturing and international trade (Galadari, 2008;Gibson et al, 2021) and, hence, will be impacted by AM. With the adoption of AM, manufacturing sites would shift to locations near customers and eliminate traditional supply chain facilities, such as manufacturing, assemblage, distribution, warehousing and retail sales facilities (Kub a c and Kodym, 2017;Sanchez-Gonzalez et al, 2019;Akbari and Ha, 2020). This would compel existing supply chain networks to restructure and, thus, result in a decrease in the total freight volume and cost of the chain (Chan et al, 2018;Chen, 2017).…”

Section: Impacts On Global Logistics and Supply Chainmentioning

confidence: 99%

Impact of additive manufacturing on maritime transportation: a review

Teweldebrhan

Maghelal²,

Galadari

2022

JILT

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PurposeAlthough additive manufacturing (AM; 3D printing/3DP) is presently in its infancy, once it becomes economically viable for mass production, it would revolutionize the operation and supply chain network of traditional businesses and manufacturing industries. To this end, approaches for ensuring a smooth transition of the economy, businesses, manufacturing centers and related services are being investigated. This review paper assesses the existing literature on the impact of AM on the maritime transportation sector.Design/methodology/approachThis paper provides a systematic literature review through three methodological phases: (1) a comprehensive review of the number of English language literature studies published on the topics of AM or 3DP (1970–2021); (2) a bibliometric analysis of selected keyword combinations and (3) a detailed review on the impact of AM on different sectors.FindingsThe key findings are that existing studies do not attempt to forecast shipping volume and ton-miles that can be affected by the mainstreaming of the technology. Additionally, existing literature that focuses on the impact of the technology on different shipping categories is limited to studies on container ships.Originality/valueThe review identifies some potential areas of research that since maritime transportation will be affected by mainstreaming AM, it will have economic, social and environmental impacts on global trade that require future assessment.

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Impact of additive manufacturing on the Vietnamese transportation industry: An exploratory study

Cited by 31 publications

References 57 publications

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

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

A systematic review of AR/VR in operations and supply chain management: maturity, current trends and future directions

Impact of additive manufacturing on maritime transportation: a review

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