4.7 Article

The relationship between nested patterns and the ripple effect in complex supply networks

期刊

出版社

TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2020.1831096

关键词

Complexity analysis; supply network; supply chain resilience; networks; ripple effect

向作者/读者索取更多资源

Research has shown that supply networks are more robust under random disruptions, but more vulnerable to hub disruptions under cascade conditions. In contrast, nested structures are less resilient as they do not benefit from a response strategy where buyers seek alternative suppliers.
Supply networks (SNs) play a vital role in fuelling trade and economic growth. Due to their interconnectedness, firm-level disruptions can cause perturbations to ripple through SNs, magnifying initial impact. Contemporary research on ripple effects focussed on understanding various structural features of SNs to predict and control disruption propagation. Our work adds to this body of knowledge by analysing an intriguing topological property that emerges in SNs: 'nestedness', which is defined as a pattern of organisation where products that are supplied by specialist suppliers are a subset of products that are supplied by generalist suppliers. In other words, generalists are also specialists. While previous research examined the emergence of nestedness and its possible reasons, its relationship to SN resilience remained unknown. Here, we develop a cascade model by bringing together the product-supplier-buyer structure; which provides us with fine-grained information on SN dependencies. We simulate disruptions in nested and non-nested organisations of the global automotive SN, and find that nested organisations are significantly more robust to random disruptions but vulnerable to hub disruptions under cascade conditions. However, nested structures are not as resilient; as they do not benefit from a response strategy where buyers seek alternative suppliers; because alternative suppliers do not exist. On the other hand, randomly connected SNs are vulnerable to cascades but can allow network reconfiguration.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

Article Economics

An entropy maximizing approach to the ferry network design problem

Michael G. H. Bell, Jing-Jing Pan, Collins Teye, Kam-Fung Cheung, Supun Perera

TRANSPORTATION RESEARCH PART B-METHODOLOGICAL (2020)

Article Engineering, Industrial

Topological rationality of supply chain networks

Supun Perera, Dharshana Kasthurirathna, Michael Bell, Michiel Bliemer

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH (2020)

Article Computer Science, Artificial Intelligence

A self controlled RDP approach for feature extraction in online handwriting recognition using deep learning

Sukhdeep Singh, Vinod Kumar Chauhan, Elisa H. Barney Smith

APPLIED INTELLIGENCE (2020)

Article Computer Science, Artificial Intelligence

Stochastic trust region inexact Newton method for large-scale machine learning

Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS (2020)

Article Economics

An eigenvector centrality analysis of world container shipping network connectivity

Kam-Fung Cheung, Michael G. H. Bell, Jing-Jing Pan, Supun Perera

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW (2020)

Article Transportation Science & Technology

A data-driven method to assess the causes and impact of delay propagation in air transportation systems

Vaggelis Giannikas, Anna Ledwoch, Goran Stojkovic, Pablo Costas, Alexandra Brintrup, Ahmed Ali Saeed Al-Ali, Vinod Kumar Chauhan, Duncan McFarlane

Summary: This study proposes a data-driven method to analyze the propagation of flight delays and their impact on airline schedules using a multi-layer network approach. Empirical results demonstrate that incorporating information in a multi-layered manner leads to a more robust assessment of delay propagation.

TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES (2022)

Article Computer Science, Interdisciplinary Applications

Real-time large-scale supplier order assignments across two-tiers of a supply chain with penalty and dual-sourcing

Vinod Kumar Chauhan, Stephen Mak, Ajith Kumar Parlikad, Muhannad Alomari, Linus Casassa, Alexandra Brintrup

Summary: Supplier selection and order allocation are strategic decisions in supply chain management that have a significant impact on supply chain performance. However, the lack of attention to scalability in the SSOA problem has hindered the adoption of SSOA algorithms by industrial practitioners. This paper proposes a novel double order allocation model with dual-sourcing and penalty constraints in a two-tier supply chain, promoting cooperation between suppliers and facilitating supplier preferences through bidding. Mixed-Integer Programming models and Genetic Algorithm approaches are proposed to solve the problem. A case study demonstrates the effectiveness of the model, showing that Mathematical Programming outperforms Genetic Algorithm in solving SSOA. The model has been successfully deployed in a large international sourcing conference, resulting in significant procurement cost reductions for a manufacturing company.

COMPUTERS & INDUSTRIAL ENGINEERING (2023)

Meeting Abstract Oncology

The effect of SGLT2i and DPP4i on new-onset gastric cancer and gastric diseases in type 2 diabetes mellitus: A population-based cohort study

H. I. Chou, V. K. Chauhan, L. Lu, C. T. Chung, T. T. L. Lee, S. Lee, H. Liu, T. K. R. Pang, A. Kaewdech, B. Cheung, G. Tse, J. Zhou

ANNALS OF ONCOLOGY (2023)

Article Chemistry, Analytical

Improving Diagnostics with Deep Forest Applied to Electronic Health Records

Atieh Khodadadi, Nima Ghanbari Bousejin, Soheila Molaei, Vinod Kumar Chauhan, Tingting Zhu, David A. Clifton

Summary: An electronic health record (EHR) is a crucial part of medical concepts, and discovering implicit correlations within this data can improve treatment and management. This paper introduces Patient Forest, an innovative approach for learning patient representations from tree-structured data, which outperforms existing models in predicting readmission and mortality. Experiments on MIMIC-III and eICU datasets demonstrate the accuracy and reliability of Patient Forest, especially when training data is limited. The qualitative evaluation using t-SNE visualization further confirms the effectiveness of this model in learning EHR representations.

SENSORS (2023)

Proceedings Paper Computer Science, Information Systems

COPER: Continuous Patient State Perceiver

Vinod Kumar Chauhan, Anshul Thakur, Odhran O'Donoghue, David Andrew Clifton

Summary: This paper proposes a novel model called COPER for handling irregular time-series in EHRs. The model utilizes a Perceiver model and neural ODEs to learn the continuous time dynamics of patient state. Experimental results demonstrate the effectiveness of the proposed model.

2022 IEEE-EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS (BHI) JOINTLY ORGANISED WITH THE IEEE-EMBS INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN'22) (2022)

Article Computer Science, Software Engineering

LIBS2ML: A library for scalable second order machine learning algorithms

Vinod Kumar Chauhan, Anuj Sharma, Kalpana Dahiya

Summary: LIBS2ML is a unique machine learning library that focuses on scalable second order methods and utilizes the MATLAB/Octave interface for faster learning and easy input/output operations.

SOFTWARE IMPACTS (2021)

Article Transportation

Identifying container shipping network bottlenecks along China's Maritime Silk Road based on a spectral analysis

Jingjing Pan, Michael G. H. Bell, Kam-Fung Cheung, Supun Perera

Summary: The 21st Century Maritime Silk Road initiated by China aims to establish a new form of regional economic cooperation with associated countries, focusing on eliminating maritime bottlenecks and improving connectivity. A recursive spectral bi-partitioning method is proposed in this paper to detect bottlenecks in the container shipping network along the MSR, successfully identifying strategic bottlenecks along key trade lanes for connectivity improvement.

MARITIME POLICY & MANAGEMENT (2021)

Article Economics

Resurgence of demand responsive transit services - Insights from BRIDJ trials in Inner West of Sydney, Australia

Supun Perera, Chinh Ho, David Hensher

RESEARCH IN TRANSPORTATION ECONOMICS (2020)

暂无数据