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

Riahi, Youssra; Saikouk, Tarik; Gunasekaran, Angappa; Badraoui, Ismail

doi:10.1016/j.eswa.2021.114702

Cited by 191 publications

(108 citation statements)

References 69 publications

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“…Bibliometrics analysis can be applied in many fields such as logistics [99], economics, biology [100], and in industry 4.0 [23,101]. In this article, we look for the articles using the WoS search engine according to certain criteria to avoid possible sources of error [102]. Furthermore, we evaluate the collected publications with some statistical metrics such as productivity, number of citations, frequency of citations, publications, impacts measure, and hybrid measures.…”

Section: Bibliometric Analysismentioning

confidence: 99%

Artificial intelligence and real-time predictive maintenance in industry 4.0: a bibliometric analysis

et al. 2022

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The purpose of this article is to study the issues of industrial maintenance, one of the critical drivers of Industry 4.0 (I4.0), which has contributed to the advent of new industrial challenges. In this context, predictive maintenance 4.0 (PdM4.0) has seen a significant progress, providing several potential advantages among which: increase of productivity, especially by improving both availability and quality and ensuring cost-saving through automated processes for production systems monitoring, early detection of failures, reduction of machine downtime, and prediction of equipment life. In the research work carried out, we focused on bibliometric analysis to provide beneficial guidelines that may help researchers and practitioners to understand the key challenges and the most insightful scientific issues that characterize a successful application of Artificial Intelligence (AI) to PdM4.0. Even though, most of the exploited articles focus on AI techniques applied to PdM, they do not include predictive maintenance practices and their organization. Using Biblioshiny, VOSviewer, and Power BI tools, our main contribution consisted of performing a Bibliometric study to analyze and quantify the most important concepts, application areas, methods, and main trends of AI applied to real-time predictive maintenance. Therefore, we studied the current state of research on these new technologies, their applications, associated methods, related roles or impacts in developing I4.0. The result shows the most common productive sources, institutes, papers, countries, authors, and their collaborative networks. In this light, American and Chinese institutes dominate the scientific debate, while the number of publications in I4.0 and PdM4.0 is exponentially growing, particularly in the field of data-driven, hybrid models, and digital twin frameworks applied for prognostic diagnostic or anomaly detection. Emerging topics such as Machine Learning and Deep Learning also significantly impacted PdM4.0 development. Subsequently, we analyzed factors that may hinder the successful use of AI-based systems in I4.0, including the data collection process, potential influence of ethics, socio-economic issues, and transparency for all stakeholders. Finally, we suggested our definition of trustful AI for I4.0.

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Section: Bibliometric Analysismentioning

confidence: 99%

Artificial intelligence and real-time predictive maintenance in industry 4.0: a bibliometric analysis

et al. 2022

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“…Inventory has a considerable influence on responsiveness. Smart warehousing, order management, and retail management increase the chances of meeting demand by ensuring product availability at the right place and time (Riahi et al, 2021). These systems would be able to track the movement of goods automatically, thus monitoring the inventory in real-time.…”

Section: Technological Capability and Supply Chain Responsivenessmentioning

confidence: 99%

“…These systems would be able to track the movement of goods automatically, thus monitoring the inventory in real-time. The service level is maintained by avoiding over or understocking (Riahi et al, 2021). AI also helps to identify factors responsible for failure in a process with its root cause analysis.…”

Section: Technological Capability and Supply Chain Responsivenessmentioning

confidence: 99%

Technology-Driven Responsiveness in Times of COVID-19: A Fuzzy Delphi and Fuzzy AHP-Based Approach

Chauhan

Akram

Gaur

2021

JGBC

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Supply chain responsiveness creates value for firms and their stakeholders and is a key towards generating above-normal profits for firms. Consequently, there has been a surge of research on it, which has expanded due to the pressures to be responsive during the pandemic of COVID19. Responsiveness is an attribute wherein the supply chains are equipped to respond resolutely and within a suitable timeframe to consumer requirements. The trouble to businesses posed by COVID-19 caught several firms off-guard. Further, there is limited research on the augmentation of supply chain responsiveness through new technologies. The present research, therefore, applies a hybrid approach by combining fuzzy Delphi and fuzzy AHP to understand the technology-driven enablers of supply chain responsiveness by employing a case company in food retail. The investigation reveals that supply chain integration technologies, sustainable manufacturing technologies, and smart warehousing are the most important enablers of supply chain responsiveness in the context of food supply chains. The results aid the key enablers that need attention and resources to be directed towards these enablers to eliminate a chance of missing on a successful transition into a more responsive supply chain.

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“…In recent years, artificial intelligence (AI)-based applications have progressed in numerous fields, including SC (Olan et al, 2021); this has enabled systems to automatically perform tasks and make decisions without human interference (Riahi et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions

et al. 2022

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The sustainable supply chain (SSC) has received significant attention worldwide in the last few years because it integrates sustainability dimensions into its process.Recent artificial intelligence (AI)-based advancements in technology make it possible to overcome many problems associated with supply chain (SC) networks. The current study was performed to explore the role of AI in establishing an SSC. The research contribution in the field of AI and SSC was examined through a systematic literature review. A total of 353 articles were gathered from the Scopus database in the selected research field. A big data-based approach, namely, structural topic modelling (STM), was applied to generate emerging thematic topics from shortlisted articles of AI in SSC. Moreover, using R package, a bibliometric analysis was conducted to examine the research trends in the field of AI in SSC. The research trends were then analysed, with generated thematic topics discussed. The proposed research framework can help researchers and practitioners to develop an SSC model using AI-based techniques. Propositions for future research are given and implications are suggested.

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

Cited by 191 publications

References 69 publications

Artificial intelligence and real-time predictive maintenance in industry 4.0: a bibliometric analysis

Artificial intelligence and real-time predictive maintenance in industry 4.0: a bibliometric analysis

Technology-Driven Responsiveness in Times of COVID-19: A Fuzzy Delphi and Fuzzy AHP-Based Approach

Reviewing the applications of artificial intelligence in sustainable supply chains: Exploring research propositions for future directions

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