Research graph
References from Inferring Supply Chain Plasticity from a Social–Ecological Systems Perspective: A Regime-Conditioned Probabilistic Framework. Local targets link to admitted publications; unresolved targets remain external evidence.
Responding to the ripple effect from systemic disruptions: Empirical evidence from the semiconductor shortage during COVID-19
10.1108/mscra-03-2024-0011 · 2024 · External reference
Plastic response to disruptions: Significant redesign of supply chains
10.1111/jbl.12321 · 2023 · External reference
Supply chain plasticity: A responsive network capability to ensure resilience
10.1111/jbl.12398 · 2024 · External reference
Coping in supply chains: A conceptual framework for disruption management
10.1108/ijlm-05-2021-0305 · 2023 · External reference
Resilience, adaptability and transformability in social–ecological systems
10.5751/es-00650-090205 · 2004 · External reference
Resilience and regime shifts: Assessing cascading effects
10.5751/es-01678-110120 · 2006 · External reference
Supply chain plasticity: Redesigning supply chains to meet major environmental change
10.1111/jbl.12226 · 2019 · External reference
Supply chain plasticity during a global disruption: Effects of CEO and supply chain networks on operational repurposing
10.1111/jbl.12291 · 2022 · External reference
Building an antifragile supply chain: A capability blueprint for resilience and post-disruption growth
10.1111/jscm.12313 · 2024 · External reference
A capability theory of the firm: An economics and (strategic) management perspective
2019 · External reference
Dynamic capabilities framework and its transformative contributions
10.1057/s41267-024-00758-8 · 2025 · External reference
Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal
10.1016/j.lrp.2018.12.001 · 2019 · External reference
The foundations of enterprise performance: Dynamic and ordinary capabilities in an (economic) theory of firms
10.5465/amp.2013.0116 · 2014 · External reference
Dynamic capabilities: Axiomatic formation of firms’ competitive competencies
2023 · External reference
Plasticity in supply networks: Leveraging generative AI for flexible and resilient supply chain design
2025 · External reference
A Hybrid Deep Learning-Based Approach for Disruption Detection and Recovery Planning in a Prototype Cognitive Digital Supply Chain Twin
10.1016/j.eswa.2025.129531 · 2026 · External reference
Two perspectives on supply chain resilience
10.1111/jbl.12271 · 2021 · External reference
A Supply Chain View of the Resilient Enterprise
2005 · External reference
Unresolved reference
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From superstorms to factory fires: Managing unpredictable supply chain disruptions
2014 · External reference
Increasing supply chain robustness through process flexibility and inventory
10.1111/poms.12887 · 2018 · External reference
Supply chain resilience: A dynamic and multidimensional approach
10.1108/ijlm-04-2017-0093 · 2018 · External reference
Supply Chain Capabilities, Risks, and Resilience
10.1016/j.ijpe.2016.09.008 · 2017 · External reference
Unresolved reference
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Unresolved reference
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Finite mixture models
10.1146/annurev-statistics-031017-100325 · 2019 · External reference
Unresolved reference
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10.1017/cbo9780511499531
10.1017/cbo9780511499531 · External reference
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10.1007/0-387-28982-8
10.1007/0-387-28982-8 · External reference
A Systematic Review of Hidden Markov Models and Their Applications
10.1007/s11831-020-09422-4 · 2021 · External reference
Denoising and recognition using Hidden Markov Models with observation distributions modeled by Hidden Markov Trees
10.1016/j.patcog.2009.11.010 · 2010 · External reference
The Hidden Markov Model and its applications in bioinformatics analysis
10.1016/j.gendis.2025.101729 · 2025 · External reference
Using Hidden Markov Models to develop ecosystem indicators from non-stationary time series
10.1016/j.ecolmodel.2024.110800 · 2024 · External reference
Hidden Markov Models with serial correlation for identifying stock–recruitment regime shifts
10.1139/cjfas-2025-0001 · 2025 · External reference
Theoretical foundations and application of Hidden Markov Models
10.9734/jsrr/2024/v30i82303 · 2024 · External reference
Flexible Markov-switching models with evolving regime-specific parameters: An application to Brazilian business cycles
10.1080/00036846.2024.2305621 · 2024 · External reference
Conditional Markov chain and its application in economic time series analysis
10.1002/jae.1140 · 2011 · External reference
Unresolved reference
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Unresolved reference
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Denoising diffusion probabilistic models
2020 · External reference
Catastrophic supply chain disruptions and supply network changes: A study of the 2011 Japanese earthquake
10.1108/ijopm-09-2020-0614 · 2021 · External reference
Exploring supply chain structural dynamics: New disruptive technologies and disruption risks
10.1016/j.ijpe.2020.107886 · 2020 · External reference
A survey of graph edit distance
10.1007/s10044-008-0141-y · 2010 · External reference
Distance between the normalized Laplacian spectra of two graphs
10.1016/j.laa.2017.05.025 · 2017 · External reference
10.4159/9780674029095
10.4159/9780674029095 · External reference
Co-evolution of knowledge diffusion and network structure in electric vehicle supply chain: An agent-based modeling approach
10.1016/j.eswa.2026.131217 · 2026 · External reference
10.1201/b20790
10.1201/b20790 · External reference
Initialization of Hidden Markov and Semi-Markov Models: A Critical Evaluation of Several Strategies
10.1111/insr.12436 · 2021 · External reference
Mixed Hidden Markov Models: An extension of the Hidden Markov Model to the longitudinal data setting
10.1198/016214506000001086 · 2007 · External reference
MAMOT: Hidden Markov modeling tool
10.1093/bioinformatics/btn201 · 2008 · External reference
Improved coarse-graining of Markov state models via explicit consideration of statistical uncertainty
10.1063/1.4755751 · 2012 · External reference
Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
10.1016/s0167-9473(02)00163-9 · 2003 · External reference
Localizing the Latent Structure Canonical Uncertainty: Entropy Profiles for Hidden Markov Models
10.1007/s11222-014-9494-9 · 2016 · External reference
An Entropy Criterion for Assessing the Number of Clusters in a Mixture Model
10.1007/bf01246098 · 1996 · External reference
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
10.1109/34.865189 · 2000 · External reference
Resilience-based network component importance measures
10.1016/j.ress.2013.03.012 · 2013 · External reference
Unresolved reference
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An OGSM-based multi-objective optimization model for partner selection in fresh produce supply chain considering carbon emissions
10.1016/j.cie.2024.110402 · 2024 · External reference
An integrated framework for solving the green supplier selection and order allocation problem in steam procurement
10.1016/j.eswa.2026.131386 · 2026 · External reference
Collaborative truck-drone delivery optimization in humanitarian logistics for vulnerable populations: An improved adaptive large neighborhood search
10.1016/j.ssci.2026.107341 · 2026 · External reference
Conditional Markov chain and its application in economic time series analysis
10.1002/jae.1140 · ExternalCitation · doi-reference
10.1007/0-387-28982-8
10.1007/0-387-28982-8 · ExternalCitation · doi-reference
An Entropy Criterion for Assessing the Number of Clusters in a Mixture Model
10.1007/bf01246098 · ExternalCitation · doi-reference
A survey of graph edit distance
10.1007/s10044-008-0141-y · ExternalCitation · doi-reference
Localizing the Latent Structure Canonical Uncertainty: Entropy Profiles for Hidden Markov Models
10.1007/s11222-014-9494-9 · ExternalCitation · doi-reference
A Systematic Review of Hidden Markov Models and Their Applications
10.1007/s11831-020-09422-4 · ExternalCitation · doi-reference
An OGSM-based multi-objective optimization model for partner selection in fresh produce supply chain considering carbon emissions
10.1016/j.cie.2024.110402 · ExternalCitation · doi-reference
Using Hidden Markov Models to develop ecosystem indicators from non-stationary time series
10.1016/j.ecolmodel.2024.110800 · ExternalCitation · doi-reference
A Hybrid Deep Learning-Based Approach for Disruption Detection and Recovery Planning in a Prototype Cognitive Digital Supply Chain Twin
10.1016/j.eswa.2025.129531 · ExternalCitation · doi-reference
Co-evolution of knowledge diffusion and network structure in electric vehicle supply chain: An agent-based modeling approach
10.1016/j.eswa.2026.131217 · ExternalCitation · doi-reference
An integrated framework for solving the green supplier selection and order allocation problem in steam procurement
10.1016/j.eswa.2026.131386 · ExternalCitation · doi-reference
The Hidden Markov Model and its applications in bioinformatics analysis
10.1016/j.gendis.2025.101729 · ExternalCitation · doi-reference
Supply Chain Capabilities, Risks, and Resilience
10.1016/j.ijpe.2016.09.008 · ExternalCitation · doi-reference
Exploring supply chain structural dynamics: New disruptive technologies and disruption risks
10.1016/j.ijpe.2020.107886 · ExternalCitation · doi-reference
Distance between the normalized Laplacian spectra of two graphs
10.1016/j.laa.2017.05.025 · ExternalCitation · doi-reference
Building dynamic capabilities for digital transformation: An ongoing process of strategic renewal
10.1016/j.lrp.2018.12.001 · ExternalCitation · doi-reference
Denoising and recognition using Hidden Markov Models with observation distributions modeled by Hidden Markov Trees
10.1016/j.patcog.2009.11.010 · ExternalCitation · doi-reference
Resilience-based network component importance measures
10.1016/j.ress.2013.03.012 · ExternalCitation · doi-reference
Collaborative truck-drone delivery optimization in humanitarian logistics for vulnerable populations: An improved adaptive large neighborhood search
10.1016/j.ssci.2026.107341 · ExternalCitation · doi-reference
Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
10.1016/s0167-9473(02)00163-9 · ExternalCitation · doi-reference
10.1017/cbo9780511499531
10.1017/cbo9780511499531 · ExternalCitation · doi-reference
Dynamic capabilities framework and its transformative contributions
10.1057/s41267-024-00758-8 · ExternalCitation · doi-reference
Improved coarse-graining of Markov state models via explicit consideration of statistical uncertainty
10.1063/1.4755751 · ExternalCitation · doi-reference
Flexible Markov-switching models with evolving regime-specific parameters: An application to Brazilian business cycles
10.1080/00036846.2024.2305621 · ExternalCitation · doi-reference
MAMOT: Hidden Markov modeling tool
10.1093/bioinformatics/btn201 · ExternalCitation · doi-reference
Supply chain resilience: A dynamic and multidimensional approach
10.1108/ijlm-04-2017-0093 · ExternalCitation · doi-reference
Coping in supply chains: A conceptual framework for disruption management
10.1108/ijlm-05-2021-0305 · ExternalCitation · doi-reference
Catastrophic supply chain disruptions and supply network changes: A study of the 2011 Japanese earthquake
10.1108/ijopm-09-2020-0614 · ExternalCitation · doi-reference
Responding to the ripple effect from systemic disruptions: Empirical evidence from the semiconductor shortage during COVID-19
10.1108/mscra-03-2024-0011 · ExternalCitation · doi-reference
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
10.1109/34.865189 · ExternalCitation · doi-reference
Initialization of Hidden Markov and Semi-Markov Models: A Critical Evaluation of Several Strategies
10.1111/insr.12436 · ExternalCitation · doi-reference
Supply chain plasticity: Redesigning supply chains to meet major environmental change
10.1111/jbl.12226 · ExternalCitation · doi-reference
Two perspectives on supply chain resilience
10.1111/jbl.12271 · ExternalCitation · doi-reference
Supply chain plasticity during a global disruption: Effects of CEO and supply chain networks on operational repurposing
10.1111/jbl.12291 · ExternalCitation · doi-reference
Plastic response to disruptions: Significant redesign of supply chains
10.1111/jbl.12321 · ExternalCitation · doi-reference
Supply chain plasticity: A responsive network capability to ensure resilience
10.1111/jbl.12398 · ExternalCitation · doi-reference
Building an antifragile supply chain: A capability blueprint for resilience and post-disruption growth
10.1111/jscm.12313 · ExternalCitation · doi-reference
Increasing supply chain robustness through process flexibility and inventory
10.1111/poms.12887 · ExternalCitation · doi-reference
Hidden Markov Models with serial correlation for identifying stock–recruitment regime shifts
10.1139/cjfas-2025-0001 · ExternalCitation · doi-reference
Finite mixture models
10.1146/annurev-statistics-031017-100325 · ExternalCitation · doi-reference
Mixed Hidden Markov Models: An extension of the Hidden Markov Model to the longitudinal data setting
10.1198/016214506000001086 · ExternalCitation · doi-reference
10.1201/b20790
10.1201/b20790 · ExternalCitation · doi-reference
10.4159/9780674029095
10.4159/9780674029095 · ExternalCitation · doi-reference
The foundations of enterprise performance: Dynamic and ordinary capabilities in an (economic) theory of firms
10.5465/amp.2013.0116 · ExternalCitation · doi-reference
Resilience, adaptability and transformability in social–ecological systems
10.5751/es-00650-090205 · ExternalCitation · doi-reference
Resilience and regime shifts: Assessing cascading effects
10.5751/es-01678-110120 · ExternalCitation · doi-reference
Theoretical foundations and application of Hidden Markov Models
10.9734/jsrr/2024/v30i82303 · ExternalCitation · doi-reference
