Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis

Wong, Lai‐Wan; Tan, Garry Wei‐Han; Ooi, Keng‐Boon; Lin, Bin; Dwivedi, Yogesh K.

doi:10.1080/00207543.2022.2063089

Cited by 76 publications

(40 citation statements)

References 138 publications

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“…Wong et al ( 2022) introduced a partial least squaresbased structural equation modeling (PLS-SEM) in artificial neural network (ANN) in supply chain finance businesses [22], which face a wide range of challenges in supplier credit risk. Commercial banks pay attention to supplier credit risk management if they intend to reduce and prevent threats.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

Wei

2022

International Transactions on Electrical Energy Systems

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In today’s complex and ever-changing world, a distribution network in lending impact analysis is an evaluation of a client’s procedures, rules, and financial well-being to evaluate as considerable risk and it provides to the contracting company. A creditor’s capability to pay the current lender’s obligations is considered while doing a lender’s threat assessment. Traditionally, it refers to the concern that the borrower may not be able to collect the sequence and interest. The challenges in lenders’ threat assessment are a lack of adequate data storage and retrieval, problematic delays caused by a lack of access to the relevant data at the right time, extended lead times that lead their shipments at risk, and demand for speedier deliveries. This paper introduces a machine learning-based linear regression algorithm (ML-LRA) for supplier credit risk (SCR) assessment based on supply chain management (SCM) in credit risk frameworks that depend significantly on modeling ML. Regression models are logistical constraints that can be used to simulate the impacts of multiple variables on a customer’s creditworthiness. The chain of distribution forecasting tool assesses specific decisions based on assumptions in variability. As a result of the findings in this study, it can be assumed that ML-LR approaches have a significant role in a variety of business processes such as supplier selection, risk prediction along with the supply chain, and demand and sales estimation. Finally, the study’s consequences for the most critical constraints and obstacles are examined to enhance the supply chain management system and ensure overall system sustainability.

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Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

Wei

2022

International Transactions on Electrical Energy Systems

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“…Ko et al (2018) argued that flexibility is crucial in promoting production innovation performance of SMEs. Wong et al (2022) confirmed the use of artificial intelligence in enhancing agility in SCRM by concerning SMEs. Studies focus on SMEs point that there needs more information on risk management of SMEs, and they also appeal to discover more about capabilities, strategies, and even obstacles of SMEs in dealing with SCR (Papadopoulos et al , 2020).…”

Section: Theoretical Foundation and Literature Backgroundmentioning

confidence: 61%

Highlight risk management in supply chain finance: effects of supply chain risk management capabilities on financing performance of small-medium enterprises

Qiao

Zhao

2023

SCM

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Purpose The purpose of this paper is to explore the important role of supply chain risk management (SCRM) capabilities as pre-factors for SMEs to improve supply chain financing performance (SCFP), also incorporating the effect of supply chain integration (SCI). Design/methodology/approach From the intersection of SCRM and SCF literature, this paper proposed hypothesis to discuss the impact of SCRM capabilities on SCFP and the role of SCI, aiming at combine SCRM with supply chain financing management. The research model was validated applying structural equation modeling on survey data from 286 Chinese small and medium-sized enterprises (SMEs). Findings Four dimensions of SCRM capabilities have significant positive effects on SCFP with different significant levels, confirming that they are important pre-factors in supply chain finance (SCF). In addition, the impact of SCRM capabilities on SCFP differ when SCI varies, indicating the promoting effect of SCI. Practical implications SMEs should establish SCRM capabilities as supply chain risks greatly influence the evaluation of financial providers and the achievement of SCF. Meanwhile, SCI should be attached for it enables superior SCFP even if SCRM capabilities are relatively limited. Originality/value This study represents a pioneering attempt to analyze the pre-factors of SMEs in improving SCFP by combing SCRM with SCF management. Few prior studies have highlighted the importance of SCRM in SCF.

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“…Lastly, although the metaverse is still in its nascent phase, it is arguable whether the development of a firm's technological capabilities for innovation can be achieved solely through dependence on one's own capacities and market size in an interconnected economy. Here, the metaverse challenges conventional wisdom, and many studies 53,54,55 found that well-timed information sharing greatly impacts operations and allows firms to better manage decision makings that lead to improved resource utilization and lower costs. With adequate infrastructures, and capacity for innovation and production, firms would be able to generate value-added through mutual exchanges because of unrestricted access to resources and capital such as technologies, innovations, skills, and data 56 and in our opinion, metaverse provides the opportunity for various stakeholders to move away from unilateral dependencies.…”

Section: Opportunitiesmentioning

confidence: 99%

Shaping the Metaverse into Reality: A Holistic Multidisciplinary Understanding of Opportunities, Challenges, and Avenues for Future Investigation

Koohang

Nord

Ooi

et al. 2023

Journal of Computer Information Systems

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217

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The term metaverse is described as the next iteration of the Internet. Metaverse is a virtual platform that uses extended reality technologies, i.e., augmented reality, virtual reality, mixed reality, 3D graphics, and other emerging technologies to allow real-time interactions and experiences in ways that are not possible in the physical world. Companies have begun to notice the impact of the metaverse and how it may help maximize profits. The purpose of this paper is to offer perspectives on several important areas, i.e., marketing, tourism, manufacturing, operations management, education, the retailing industry, banking services, healthcare, and human resource management that are likely to be impacted by the adoption and use of a metaverse. Each includes an overview, opportunities, challenges, and a potential research agenda.

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Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis

Cited by 76 publications

References 138 publications

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

A Machine Learning Algorithm for Supplier Credit Risk Assessment Based on Supply Chain Management

Highlight risk management in supply chain finance: effects of supply chain risk management capabilities on financing performance of small-medium enterprises

Shaping the Metaverse into Reality: A Holistic Multidisciplinary Understanding of Opportunities, Challenges, and Avenues for Future Investigation

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