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.

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.

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.

Capturing the dynamics of interorganizational governance implementation: a process and cybernetic based conceptualization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tao Zhang
Keyword(s):  
Supply Chain ◽  
Design Methodology ◽  
Future Research ◽  
New Paradigm ◽  
Research Directions ◽  
Chain Management ◽  
Content Type ◽  
Governance Strategies ◽  
Future Research Directions ◽  
Governance Processes

PurposeThe effectiveness of interorganizational governance is one of the most significant concerns of firms involved in supply chain management. Previous studies have extensively examined various interorganizational governance strategies. However, the dynamic and implementation details of interorganizational governance receive little attention, which leads to the defects of interorganizational governance literature. This study tries to explore this issue.Design/methodology/approachBased on the process and cybernetic view, this study conceptualized four interorganizational governance processes and their respective critical activities to capture the dynamic and implementation details of interorganizational governance. Furthermore, this study investigated the mapping of governance strategies into different critical activities, which unveil the various manifestations of governance strategies across these critical activities.FindingsFour interorganizational governance processes and their respective critical activities would overarch the dynamic and implementation details of governance strategies. Furthermore, various governance strategies also would have different manifestations across the critical activities of the four processes.Originality/valueThis paper fills the gaps in interorganizational governance literature in which the dynamic details of governance strategies are unclear. The new conceptualization provides a new paradigm for researchers to zoom in on the subtle dynamics of interorganizational governance. The new conceptualization indicates a few promising future research directions.

scholarly journals Nexus of circular economy and sustainable business performance in the era of digitalization

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rohit Agrawal ◽  
Vishal Ashok Wankhede ◽  
Anil Kumar ◽  
Arvind Upadhyay ◽  
Jose Arturo Garza-Reyes
Keyword(s):  
Circular Economy ◽  
Business Performance ◽  
Bibliometric Study ◽  
Future Research ◽  
Unique Contribution ◽  
Sustainable Business ◽  
Research Directions ◽  
Content Type ◽  
Product Service ◽  
Future Research Directions

PurposeThis study aims to conduct a comprehensive review and network-based analysis by exploring future research directions in the nexus of circular economy (CE) and sustainable business performance (SBP) in the context of digitalization.Design/methodology/approachA systematic literature review methodology was adopted to present the review in the field of CE and SBP in the era of digitalization. WOS and SCOPUS databases were considered in the study to identify and select the articles. The bibliometric study was carried out to analyze the significant contributions made by authors, various journal sources, countries and different universities in the field of CE and SBP in the era of digitalization. Further, network analysis is carried out to analyze the collaboration among authors from different countries.FindingsThe study revealed that digitalization could be a great help in developing sustainable circular products. Moreover, the customers' involvement is necessary for creating innovative sustainable circular products using digitalization. A move toward the product-service system was suggested to accelerate the transformation toward CE and digitalization.Originality/valueThe paper discusses digitalization and CE practices' adoption to enhance the SP of the firms. This work's unique contribution is the systematic literature analysis and bibliometric study to explore future research directions in the nexus of CE and SP in the context of digitalization. The present study has been one of the first efforts to examine the literature of CE and SBP integration from a digitalization perspective along with bibliometric analysis.

Integrated supply chain risk management: a systematic review

2017 ◽  
Vol 28 (4) ◽  
pp. 1123-1141 ◽  
Author(s):  
Quan Zhu ◽  
Harold Krikke ◽  
Marjolein C.J. Caniëls
Keyword(s):  
Risk Management ◽  
Supply Chain ◽  
Future Research ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Contingency Analysis ◽  
Research Directions ◽  
Content Type ◽  
Future Research Directions ◽  
Integrated Supply Chain

Purpose Supply chain risks specifically refer to risks that transmit among supply chain members, thus they should be understood and managed as a whole for an end-to-end supply chain. The purpose of this paper is to review literature of integrated supply chain risk management (ISCRM) that connects supply chain integration (SCI) with supply chain risk management. Design/methodology/approach The systematic literature review methodology was used to select and categorize articles between 1998 and 2015 in peer-reviewed journals. A contingency analysis was further applied to detect association patterns and links between category items. Findings Through a systematic literature review, the research has clearly analyzed risk sources, scopes and dimensions of SCI, and scopes and dimensions of performance in the field of ISCRM. Furthermore, by applying the contingency analysis, the paper has proposed future research directions that are based on the extant literature findings. Originality/value The identified insights, gaps, and future research directions will encourage researchers as well as managers to drive the development of ISCRM.

Trust and commitment in supply chain management: a systematic review of literature

2020 ◽  
Vol 27 (10) ◽  
pp. 2831-2862
Author(s):  
Ratna Achuta Paluri ◽  
Aditi Mishal
Keyword(s):  
Systematic Review ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Future Research ◽  
Review Of Literature ◽  
Research Directions ◽  
Chain Management ◽  
Content Type ◽  
Future Research Directions ◽  
Antecedents And Consequences

PurposeTrust and commitment (T&C) among the supply chain partners in the context of supply chain management (SCM) are of interest for both researchers and practitioners. This paper analyses literature on T&C and identifies gaps for further research.Design/methodology/approachThe current literature review paper provides a comprehensive perspective on the topic using bibliometric analysis followed by a systematic review of literature. In all, 207 relevant articles were extracted from the Scopus database using the relevant key word searches. For the purpose of the systematic review, another 48 relevant papers were identified through an iterative process. Hence, 255 papers published between the years 1990–2019 were analysed for the sake of this study.FindingsA total of 15 definitions of trust, nine definitions of commitment, 13 classifications of trust, 40 antecedents of trust, six classifications of commitments, 39 consequences of trust, 11 antecedents of commitment and 15 consequences of commitment were identified and analysed. Future research directions were presented.Research limitations/implicationsThe study is limited to identifying the antecedents and consequences of T&C. A detailed framework could be developed in future research. The antecedent and consequences for T&C could be discussed in greater detail.Practical implicationsImportant implications for managers emerge from this study for building and implementing T&C, as SCM requires a thorough understanding of relationship-building skills. The discussion on the definitions of T&C, types of trust and the antecedents and consequences provides important insights for practitioners for strategy formulation. Results provide important insights and bring about greater clarity for researchers and practitioners on T&C in SCM.Originality/valueThrough rigorous analysis of the prevailing research, this paper extensively reviews literature on T&C in SCM till 2019. It summarises the current status and proposes future research directions.

scholarly journals A review of supply chain risk management: definition, theory, and research agenda

2018 ◽  
Vol 48 (3) ◽  
pp. 205-230 ◽  
Author(s):  
Yiyi Fan ◽  
Mark Stevenson
Keyword(s):  
Risk Management ◽  
Supply Chain ◽  
Research Agenda ◽  
Future Research ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Research Directions ◽  
Content Type ◽  
Future Research Directions ◽  
Theory Use

Purpose The purpose of this paper is to review the extant literature on supply chain risk management (SCRM, including risk identification, assessment, treatment, and monitoring), developing a comprehensive definition and conceptual framework; to evaluate prior theory use; and to identify future research directions. Design/methodology/approach A systematic literature review of 354 articles (published 2000-2016) based on descriptive, thematic, and content analysis. Findings There has been a considerable focus on identifying risk types and proposing risk mitigation strategies. Research has emphasised organisational responses to supply chain risks and made only limited use of theory. Ten key future research directions are identified. Research limitations/implications A broad, contemporary understanding of SCRM is provided; and a new, comprehensive definition is presented covering the process, pathway, and objectives of SCRM, leading to a conceptual framework. The research agenda guides future work towards maturation of the discipline. Practical implications Managers are encouraged to adopt a holistic approach to SCRM. Guidance is provided on how to select appropriate risk treatment actions according to the probability and impact of a risk. Originality/value The first review to consider theory use in SCRM research and to use four SCRM stages to structure the review.

scholarly journals The Use of Artificial Intelligence in Tribology—A Perspective

Lubricants ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 2
Author(s):  
Andreas Rosenkranz ◽  
Max Marian ◽  
Francisco J. Profito ◽  
Nathan Aragon ◽  
Raj Shah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Film Thickness ◽  
Future Research ◽  
Research Directions ◽  
Machine Learning Methods ◽  
Successful Case ◽  
The Coefficient Of Friction ◽  
Future Research Directions

Artificial intelligence and, in particular, machine learning methods have gained notable attention in the tribological community due to their ability to predict tribologically relevant parameters such as, for instance, the coefficient of friction or the oil film thickness. This perspective aims at highlighting some of the recent advances achieved by implementing artificial intelligence, specifically artificial neutral networks, towards tribological research. The presentation and discussion of successful case studies using these approaches in a tribological context clearly demonstrates their ability to accurately and efficiently predict these tribological characteristics. Regarding future research directions and trends, we emphasis on the extended use of artificial intelligence and machine learning concepts in the field of tribology including the characterization of the resulting surface topography and the design of lubricated systems.

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