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.

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.

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.

Big Data Analytics Driven Supply Chain Transformation

2019 ◽  
pp. 106-124 ◽  
Author(s):  
Mondher Feki
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Research Directions ◽  
Digital Platforms ◽  
New Frontier ◽  
Future Research Directions ◽  
Chain Transformation

Big data has emerged as the new frontier in supply chain management; however, few firms know how to embrace big data and capitalize on its value. The non-stop production of massive amounts of data on various digital platforms has prompted academics and practitioners to focus on the data economy. Companies must rethink how to harness big data and take full advantage of its possibilities. Big data analytics can help them in giving valuable insights. This chapter provides an overview of big data analytics use in the supply chain field and underlines its potential role in the supply chain transformation. The results show that big data analytics techniques can be categorized into three types: descriptive, predictive, and prescriptive. These techniques influence supply chain processes and create business value. This study sets out future research directions.

scholarly journals Supply chain risk management modelling: A systematic literature network analysis review

2020 ◽  
Vol 31 (4) ◽  
pp. 387-416
Author(s):  
Marcus Vinicius Carvalho Fagundes ◽  
Eduardo Oliveira Teles ◽  
Silvio A B Vieira de Melo ◽  
Francisco Gaudêncio Mendonça Freires
Keyword(s):  
Risk Management ◽  
Supply Chain ◽  
Network Analysis ◽  
Future Research ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Risk Models ◽  
Research Directions ◽  
Research Areas ◽  
Future Research Directions

Abstract The modelling of supply chain risk management (SCRM) has attracted increasing attention from researchers and professionals. However, a systematic network analysis of the literature to understand the development of research over time is lacking. Therefore, this study reviews SCRM modelling and its evolution as a scientific field. We collected 566 papers published in the Scopus database and shortlisted 120 for review. We have analysed the field's performance, mapped the most influential studies, as well as the generative and evolutionary research areas, and derived future research directions. Using bibliometric methods and tools for citation network analysis to understand the field's dynamic development, we find that five generative research areas provide the fundamental knowledge for four evolutionary research areas. The interpretation of gaps and trends in these areas provides an SCRM modelling timeline with 14 future research directions, which should consider adopting a holistic SCRM approach and developing prescriptive and normative risk models. The holistic approach enables more research on key factors—like process integration, design, information risk, visibility and risk coordination—that directly impact industry, decision-makers and sustainability needs. Risk models with evolved prescriptive and normative typology should respect both business model strategies and actual supply chain performance.

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.

Big Data Analytics Driven Supply Chain Transformation

2022 ◽  
pp. 1413-1432
Author(s):  
Mondher Feki
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Research Directions ◽  
Digital Platforms ◽  
New Frontier ◽  
Future Research Directions ◽  
Chain Transformation

Big data has emerged as the new frontier in supply chain management; however, few firms know how to embrace big data and capitalize on its value. The non-stop production of massive amounts of data on various digital platforms has prompted academics and practitioners to focus on the data economy. Companies must rethink how to harness big data and take full advantage of its possibilities. Big data analytics can help them in giving valuable insights. This chapter provides an overview of big data analytics use in the supply chain field and underlines its potential role in the supply chain transformation. The results show that big data analytics techniques can be categorized into three types: descriptive, predictive, and prescriptive. These techniques influence supply chain processes and create business value. This study sets out future research directions.

A critical analysis of supply chain risk management content: a structured literature review

2017 ◽  
Vol 14 (1) ◽  
pp. 69-90 ◽  
Author(s):  
Surya Prakash ◽  
Gunjan Soni ◽  
Ajay Pal Singh Rathore
Keyword(s):  
Risk Management ◽  
Supply Chain ◽  
Literature Review ◽  
Critical Analysis ◽  
Future Research ◽  
Supply Chain Risk Management ◽  
Supply Chain Risk ◽  
Marked Rise ◽  
Content Type ◽  
Risk Management Process

Purpose The research on supply chain risk management (SCRM) is visibly on the rise, although its literature still lacks the state of the art that critically analyzes its content. The SCRM literature seems to require studies that utilize risk typology, sources of risk, etc. for reviewing the topic. The purpose of this paper is to bridge the gap by synthesizing the information obtained from 343 articles across 85 journals. This study also presents a critical analysis of the content of SCRM in a structured manner to identify the directions for future research. Design/methodology/approach A systematic literature review (SLR) was devised and adopted, which involved the selection, classification, and evaluation of 343 research articles published over a period of 11 years (2004-2014). The content of extant SCRM literature was critically analyzed and synthesized from the perspective of the risk management process (RMP). Findings The analysis of extant literature shows that there is a marked rise in research in the SCRM area, especially after the year 2005. It was observed that not only risk but also different forms of uncertainties make supply chain (SC) operations difficult to manage. The SCRM actions yielded most benefits when their implementation was at chain or network level and managed strategically. The analysis also reveals that the manufacturing sector is most affected by risks and highly investigated by researchers. Practical implications A complete process for SCRM based on risk stratification, objectives of risk management, and RMP will be a guiding model for firms to manage risks. The research gaps identified and future directions provided here will encourage researchers and managers to devise new methods, tools, and techniques to address the risks in modern SC operations. Originality/value An SLR and risk-based content classification of SCRM literature were performed. To identify, locate, select, and analyze the SCRM literature, a structured and systematic process was adopted with some very rarely used methods such as two levels of search keywords, and strings were formulated to locate the most relevant articles in major academic databases.

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.

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