Review of quantitative methods for supply chain resilience analysis
Supply chain resilience (SCR) manifests when the network is capable to withstand, adapt,
and recover from disruptions to meet customer demand and ensure performance. This paper …
and recover from disruptions to meet customer demand and ensure performance. This paper …
Supply chain risk management and artificial intelligence: state of the art and future research directions
G Baryannis, S Validi, S Dani… - International Journal of …, 2019 - Taylor & Francis
Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to
identify, assess, mitigate and monitor unexpected events or conditions which might have an …
identify, assess, mitigate and monitor unexpected events or conditions which might have an …
A machine learning based approach for predicting blockchain adoption in supply Chain
The purpose of this paper is to provide a decision support system for managers to predict an
organization's probability of successful blockchain adoption using a machine learning …
organization's probability of successful blockchain adoption using a machine learning …
[HTML][HTML] Do blockchain and circular economy practices improve post COVID-19 supply chains? A resource-based and resource dependence perspective
Purpose Using the resource-based and the resource dependence theoretical approaches of
the firm, the paper explores firm responses to supply chain disruptions during COVID-19 …
the firm, the paper explores firm responses to supply chain disruptions during COVID-19 …
Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis
This study posits that the use of artificial intelligence (AI) enables supply chains (SCs) to
dynamically react to volatile environments, and alleviate potentially costly decision-makings …
dynamically react to volatile environments, and alleviate potentially costly decision-makings …
Bayesian networks for supply chain risk, resilience and ripple effect analysis: A literature review
S Hosseini, D Ivanov - Expert systems with applications, 2020 - Elsevier
In the broad sense, the Bayesian networks (BN) are probabilistic graphical models that
possess unique methodical features to model dependencies in complex networks, such as …
possess unique methodical features to model dependencies in complex networks, such as …
Understanding trade-offs and synergies of ecosystem services to support the decision-making in the Beijing–Tianjin–Hebei region
Z Feng, X Jin, T Chen, J Wu - Land Use Policy, 2021 - Elsevier
Understanding ecosystem service trade-offs and synergies is the foundation to achieve the
efficient management of the ecosystem and improve human well-being. However, the …
efficient management of the ecosystem and improve human well-being. However, the …
[HTML][HTML] Big Data in food safety-A review
C Jin, Y Bouzembrak, J Zhou, Q Liang… - Current Opinion in Food …, 2020 - Elsevier
The massive rise of Big Data generated from smartphones, social media, Internet of Things
(IoT), and multimedia, has produced an overwhelming flow of data in either structured or …
(IoT), and multimedia, has produced an overwhelming flow of data in either structured or …
Bayesian network modelling for supply chain risk propagation
Supply chain risk propagation is a cascading effect of risks on global supply chain networks.
The paper attempts to measure the behaviour of risks following the assessment of supply …
The paper attempts to measure the behaviour of risks following the assessment of supply …
Visualisation of ripple effect in supply chains under long-term, simultaneous disruptions: a system dynamics approach
Supply chains (SCs) are exposed to multiple risks and vulnerable to disruption propagation
(ie the ripple effect). Despite established literature, quantitative analysis of the ripple effect in …
(ie the ripple effect). Despite established literature, quantitative analysis of the ripple effect in …