Artificial intelligence applications in supply chain: A descriptive bibliometric analysis and future research directions

2021 ◽  
Vol 173 ◽  
pp. 114702
Author(s):  
Youssra Riahi ◽  
Tarik Saikouk ◽  
Angappa Gunasekaran ◽  
Ismail Badraoui
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Bibliometric Analysis ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions

The contemporary state of big data analytics and artificial intelligence towards intelligent supply chain risk management: a comprehensive review

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Harsh M. Shah ◽  
Bhaskar B. Gardas ◽  
Vaibhav S. Narwane ◽  
Hitansh S. Mehta
Keyword(s):  
Artificial Intelligence ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Research Directions ◽  
Content Type ◽  
Future Research Directions

PurposeThis paper aims to conduct a systematic literature review of the research in the field of Artificial Intelligence (AI) and Big Data Analytics (BDA) in Supply Chain Risk Management (SCRM). Finally, future research directions in this field have been suggested.Design/methodology/approachThe papers were searched using a set of keywords in the SCOPUS database. These papers were filtered using the Title abstract keywords principle. Further, more papers were found using the forward-backward referencing method. The finalized papers were then classified into eight categories.FindingsThe previous papers in AI and BDA in SCRM were studied. These papers emphasized various modelling and application techniques for AI and BDA in making the supply chain (SC) more resilient. It was found that more research has been done into conceptual modelling rather than real-life applications. It was seen that the use of AI-based techniques and structural equation modelling was prominent.Practical implicationsAI and BDA help build the risk profile, which will guide the decision-makers and risk managers make their decisions quickly and more effectively, reducing the risks on the SC and making it resilient. Other than this, they can predict the risks in disasters, epidemics and any further disruption. They also help select the suppliers and location of the various elements of the SC to reduce the lead times.Originality/valueThe paper suggests various future research directions that fellow researchers can explore. None of the previous research examined the role of BDA and AI in SCRM.

Applications of artificial intelligence and machine learning within supply chains:systematic review and future research directions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hassan Younis ◽  
Balan Sundarakani ◽  
Malek Alsharairi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Supply Chain ◽  
Future Research ◽  
Unique Contribution ◽  
Research Directions ◽  
Holistic View ◽  
Content Type ◽  
Future Research Directions ◽  
Research Studies

Purpose The purpose of this study is to investigate how artificial intelligence (AI), as well as machine learning (ML) techniques, are being applied and implemented within supply chains (SC) and to develop future research directions from thereof. Design/methodology/approach Using a systematic literature review methodology, this study analyzes the publications available on Web of Science, Scopus and Google Scholar that linked both AI and supply chain from one side and ML and supply chain from another side. A total of 388 research studies have been identified through the before said three database searches which are further screened, sorted and finalized with 50 studies. The research thoroughly reviews and analyzes the final lists of 50 studies that were found relevant and significant to the theme of AI and ML in supply chain management (SCM). Findings AI and ML applications are still at the infant stage and the opportunity for them to elevate supply chain performance is very promising. Some researchers developed AI and ML-related models which were tested and proved to be effective in optimizing SC, and therefore, the application of AI and ML in supply chain networks creates competitive advantages for firms. Other researchers claim that AI and ML are both currently adding value while many other researchers believe that they are still not fully exploited and their tools and techniques can leverage the supply chain’s total value. The research found that adoption of AI and ML have the ability to reduce the bullwhip effect, and therefore, further supports the performance of supply chain efficiency and responsiveness. Research limitations/implications This research was limited in terms of scope as it covered AI and ML applications in the supply chain while there are other dimensions that could be investigated such as big data and robotics but it was found too lengthy to include these additional dimensions, and therefore, left for future research studies that other researchers could explore and pursue. Practical implications This study opens the door wide for other researchers to explore how AI and ML can be adopted in SCM and what are the models that are already tested and proven to be viable. In addition, the paper also identified a group of research studies that confirmed the unexploited avenues of AI and ML which could be of high interest to other researchers to explore. Originality/value Although few earlier research studies touch based on the AI applications within manufacturing and transportation, this study is different and makes a unique contribution by offering a holistic view on the AI and ML implications within SC as a whole. The research carefully reviews a number of highly cited papers classifying them into three main themes and recommends future direction.

scholarly journals Mapping and Clustering Analysis on Environmental, Social and Governance Field a Bibliometric Analysis Using Scopus

2021 ◽  
Vol 13 (13) ◽  
pp. 7304
Author(s):  
Shang Gao ◽  
Fanchen Meng ◽  
Zhouyang Gu ◽  
Zhiyuan Liu ◽  
Muhammad Farrukh
Keyword(s):  
Bibliometric Analysis ◽  
Clustering Analysis ◽  
Research Agenda ◽  
Future Research ◽  
Unique Contribution ◽  
Research Directions ◽  
Current Trends ◽  
Bibliographic Data ◽  
Future Research Directions ◽  
Scopus Database

Academic interest in ESG has grown significantly in recent years. Nevertheless, bibliometric and visualization research on this topic is still insufficient. This study aims to conduct publication metrics on the literature connected with ESG and attempt to give a research agenda for future research. In this study, we used data from the Scopus database. Various bibliometric techniques, such as bibliographic coupling and co-occurrence analysis, were combined with assorted themes to present an overview. To the best of our knowledge, there is no study that analyses the bibliographic data on ESG fields; this study is a unique contribution to the literature. This study also provides an overview of the trends and trajectories with a visual and schematic frame for the research of this topic. This may help researchers understand the current trends and future research directions, and enable future authors to conduct their studies more effectively.

A systematic review of the literature on supply chain agility

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bharat Singh Patel ◽  
Murali Sambasivan
Keyword(s):  
Supply Chain ◽  
Cost Benefit ◽  
Business Environment ◽  
Future Research ◽  
Research Directions ◽  
Content Type ◽  
Trade Offs ◽  
Supply Chain Agility ◽  
Agility Assessment ◽  
Future Research Directions

Purpose The purpose of this study is to critically examine the scholarly articles associated Murali Sambasivan with the diverse aspects of supply chain agility (SCA). The review highlights research insights, existing gaps and future research directions that can help academicians and practitioners gain a comprehensive understanding of SCA. Design/methodology/approach The present study has adopted author co-citation analysis as the research methodology, with a view to thoroughly investigating the good-quality articles related to SCA that have been published over a period of 22 years (1999-2020). In this study, 126 research papers on SCA – featuring diverse aspects of agility – from various reputed journals have been examined, analysed and assimilated. Findings The salient findings of this research are, namely, agility is different from other similar concepts, such as flexibility, leanness, adaptability and resilience; of the 13 dimensions of agility discussed in the literature, the prominent ones are quickness, responsiveness, competency and flexibility; literature related to SCA can be categorised as related to modelling the enablers, agility assessment, agility implementation, leagility and agility maximisation. This research proposes a more practical definition and framework for SCA. The probable areas for future research are, namely, impediments to agility, effective approaches to agility assessment, cost-benefit trade-offs to be considered whilst implementing agility, empirical research to validate the framework and SCA in the domain of healthcare and disaster relief supply chains. Practical implications This paper provides substantial insights to practitioners who primarily focus on measuring and implementing agility in the supply chain. The findings of this study will help the supply chain manager gain a better idea about how to become competitive in today’s dynamic and turbulent business environment. Originality/value The originality of this study is in: comprehensively identifying the various issues related to SCA, such as related concepts, definitions, dimensions and different categories of studies covered in literature, proposing a new definition and framework for SCA and identifying potential areas for future research, to provide deeper insights into the subject and highlight areas for future research.

Retail Customer Engagement

2019 ◽  
pp. 1-26
Author(s):  
Andrea Moretta Tartaglione ◽  
Giuseppe Granata
Keyword(s):  
Literature Review ◽  
Bibliometric Analysis ◽  
Scientific Community ◽  
State Of The Art ◽  
Customer Engagement ◽  
Future Research ◽  
Research Directions ◽  
Study Results ◽  
Research Findings ◽  
Future Research Directions

Customer engagement is one of the most debated topics in marketing literature. The great interest of the scientific community resulted in a large amount of research on this topic making it difficult for scholars to understand how to really contribute to advance the research. Based on these considerations, this chapter aims to provide an overview of the research findings and trends of previous studies to guide the researcher to the most influential works, results, and issues that need more insights. In particular, this chapter offers a literature review on customer engagement and retail customer engagement using bibliometric analysis and scientific mapping study. Results show the most productive authors, most cited publications, most frequent words, and clusters of related words. The analysis provides a description of the state of the art of retail customer engagement and suggests future research directions.

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

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