Supply chain risk management with machine learning technology: A literature review and future research directions

Yang, Mei; Lim, Ming K.; Qu, Yingchi; Ni, Du; Xiao, Zhi

doi:10.1016/j.cie.2022.108859

Cited by 53 publications

(36 citation statements)

References 94 publications

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“…The results of this study indicate that the most frequently adopted techniques in integrated models have been TOPSIS, FMEA, AHP, DEMATEL and QFD, while in Lima-Junior and Carpinetti's study [6] the AHP and Data Envelopment Analysis (DEA) techniques and linear programming predominated. In terms of the SCM processes which had the most applications, in analyzing artificial intelligence techniques in SCM, Yang et al [5] found that the return and make processes had fewer applications, while our study has found that the least studied processes have been return and delivery. On the other hand, while Yang et al [5] found that the enable and plan processes had the most applications, this study has found that the source and enable processes have had the most applications.…”

Section: Comparison Of the Results Of Previous Studiescontrasting

confidence: 48%

“…In terms of the SCM processes which had the most applications, in analyzing artificial intelligence techniques in SCM, Yang et al [5] found that the return and make processes had fewer applications, while our study has found that the least studied processes have been return and delivery. On the other hand, while Yang et al [5] found that the enable and plan processes had the most applications, this study has found that the source and enable processes have had the most applications.…”

Section: Comparison Of the Results Of Previous Studiescontrasting

confidence: 48%

“…Finally, in terms of the economic sectors of the participating companies which have received these applications, Yang et al's study [5] noted the predominance of retail, food, and manufacturing, while our study showed a prevalence in the automotive, health, and electro-electronic industries. These results are somewhat similar to those found by Lima-Junior and Carpinetti [6], who related that the sectors that received the most applications of decision-making models were the automotive, food, and electro-electronic industries.…”

Section: Comparison Of the Results Of Previous Studiescontrasting

confidence: 40%

See 2 more Smart Citations

An Overview of Hesitant Fuzzy Linguistic Term Set Applications in Supply Chain Management: The State of the Art and Future Directions

Lima¹,

Oliveira²,

Resende³

2023

Preprint

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Supply Chain Management (SCM) encompasses a wide variety of decision-making problems that affect business and supply chain performance as a whole. Since most of these problems involve uncertainty and hesitation on the part of Decision Makers (DMs), various studies have emerged recently that present SCM applications of techniques based on Hesitant Fuzzy Linguistic Term Sets (HFLTSs) and HFLTS extensions. Given the relevance of this subject and the lack of literature review studies, this study presents a systematic review of HFLTS and HFLTS extension applications to SCM decision-making problems. In order to answer a set of research questions, the selected papers have been classified in accordance with a group of factors that are pertinent to the origins of these studies, SCM, HFLTSs, and decision-making. The results demonstrate that the Source and Enable processes have been studied with greater frequency, while the most common problems have to do with supplier selection, failure evaluation, and performance evaluation. The companies of the automotive sector and Sustainable SCM and Green SCM strategies predominate in the analyzed studies. Even though most of the studies use techniques based on HFLTSs, we have identified applications of seven distinct HFLTS extensions, with Double Hierarchy Hesitant Fuzzy Linguistic Term Sets and Probabilistic Linguistic Term Sets being the most utilized. The identification of gaps in the literature presents avenues for future studies focused on innovative applications, integrations of techniques, comparisons of techniques, group decision-making, and validation procedures for new models. The results of this study offer a panorama of the state of the art in regard to this subject and can help researchers and practitioners develop new studies which involve the use of methods that employ HFLTSs and HFLTS extensions in SCM decision-making problems.

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Section: Comparison Of the Results Of Previous Studiescontrasting

confidence: 48%

Section: Comparison Of the Results Of Previous Studiescontrasting

confidence: 48%

Section: Comparison Of the Results Of Previous Studiescontrasting

confidence: 40%

See 1 more Smart Citation

An Overview of Hesitant Fuzzy Linguistic Term Set Applications in Supply Chain Management: The State of the Art and Future Directions

Lima¹,

Oliveira²,

Resende³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…More research in the future could look at the morals of using AI in managing risks. Yang et al [44] ML technology makes supply chain risk management better, but we still need to study how to use ML with current ways of doing things.…”

Section: Abdullahi Et Al[1]mentioning

confidence: 99%

A Systematic Literature Review: AI, DL and Machine Learning inCyber Risk Management

Hamid,

Rahman

2024

Preprint

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The integration of AI and ML in both climate change and cyber risk management has significantly bolstered their effectiveness. These advanced technologies enable organizations to handle extensive datasets, whether related to climate patterns or cyber activities, with greater efficiency and making more informed decisions. AI and machine learning are utilized in cyber security to predict potential threats, automate tasks, and manage cyber security risks. Ensuring the accuracy of information, comprehending the functionality of the model, and safeguarding individuals' data pose challenges. It is very important to use personal information ethically and protect it. Researchers should prioritize accuracy in the information utilized, clarity in AI models, and fairness in AI decision-making in the future. New technologies like edge computing, federated learning, explainable AI, and quantum computing are changing the way we manage risk and keep our digital information safe. These new advancements provide opportunities to gain a better understanding of risks, identify threats more rapidly, and improve decision-making. Still, there are challenges concerning data accuracy, understanding model operations, and privacy issues that must be resolved before these technologies can effectively address cyber risks.

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“…In [5], Mehran et al focused on supply chain management, aiming to improve the environmental performance and cost performance of the entire supply chain system. In [6], the risk management issues in supply chain management were reviewed with emphasis. He et al focused on studying the impact of carbon emissions and timeliness in the delivery process on consumer behaviours in supply chain management, providing solutions for supply chain management problems with consumer participation [7].…”

Section: Supply Chain Cost Managementmentioning

confidence: 99%

Research on delivery cost management method of agricultural enterprises supply chain based on heuristic algorithms

CHEN

2023

Sixth International Conference on Computer Information Science and Application Technology (CISAT 2023)

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Reducing logistics costs in the supply chain has been a key concern of major enterprises. Meanwhile, reducing the delivery cost of the supply chain is a key component of supply chain cost management. Therefore, this article mainly focuses on the cost management of agricultural enterprise supply chain delivery and achieves the goal of reducing logistics costs by optimizing the specific process of supply chain delivery. Firstly, a multi-objective mathematical model was established by comprehensively considering multiple objectives such as delivery cost targets and carbon emission targets in the delivery process of agricultural products. Furthermore, the delivery quality of agricultural products during the delivery process was ensured through time window constraints, and corresponding constraint conditions were established based on the actual scenarios during the agricultural product delivery process. On this basis, an Improved Artificial Bee Colony algorithm (IABC), a heuristic algorithm, was designed. And simulation experiments were conducted. The simulation results showed that the algorithm designed in the article can be effectively applied to the cost management problem of agricultural enterprises.

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Supply chain risk management with machine learning technology: A literature review and future research directions

Cited by 53 publications

References 94 publications

An Overview of Hesitant Fuzzy Linguistic Term Set Applications in Supply Chain Management: The State of the Art and Future Directions

An Overview of Hesitant Fuzzy Linguistic Term Set Applications in Supply Chain Management: The State of the Art and Future Directions

A Systematic Literature Review: AI, DL and Machine Learning inCyber Risk Management

Research on delivery cost management method of agricultural enterprises supply chain based on heuristic algorithms

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