Supply chains and fake news: a novel input–output neural network approach for the US food sector

Cheilas, Panagiotis T.; Melissaropoulos, Ioannis G.; Xidonas, Panos; Michaelides, Panayotis G.

doi:10.1007/s10479-022-04817-x

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…OIPT reasons that firms need a stabilizing mechanism by possessing resources and capacities in operations to cope with uncertainties and manage unforeseen events that disturb normal business and supply chain operations (Wong et al, 2020). Scholarship suggests that SCDs could be caused by disinformation (e.g., Konstantakis et al, 2022;Xu et al, 2020). It is ultimately inevitable for supply chains to cultivate the capability and capacity to proactively engage the filtration of the information and news to improve supply chain operations.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

et al. 2022

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Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

et al. 2022

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Solar events and the US energy sector: a novel sectoral spillover GVAR approach introducing indirect GIRFs (IGIRF)

et al. 2023

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Supplier Evaluation in Supply Chain Environment Based on Radial Basis Function Neural Network

Liu,

Yu,

Kim

2024

International Journal of Information Technology and Web Engineering

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The comprehensive evaluation and selection of suppliers under the environment of supply chain management has become a key factor affecting the success of supply chain. How to select suppliers and the strategic partnership between suppliers under the environment of supply chain management has become an important challenge. To solve this problem, this paper takes the supplier evaluation and selection of Guangzhou Automobile Toyota Company as the research object, constructs the index system of supplier comprehensive evaluation and selection, uses the RBF neural network algorithm to establish the supplier evaluation and selection model, and makes an experimental study. The results show that radial basis function neural network is a local approximation network, which has a unique and definite solution to the problem, and there is no local minimum problem in BP network. It is a method that enables enterprises and suppliers to have a clear understanding and seek further promotion together. The research provides theoretical data support for enterprise managers to make decisions.

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Supply chains and fake news: a novel input–output neural network approach for the US food sector

Cited by 3 publications

References 33 publications

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Solar events and the US energy sector: a novel sectoral spillover GVAR approach introducing indirect GIRFs (IGIRF)

Supplier Evaluation in Supply Chain Environment Based on Radial Basis Function Neural Network

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