Structural characteristics of complex supply chain networks

Perera, Supun; Perera, H. Niles; Kasthurirathna, Dharshana

doi:10.1109/mercon.2017.7980470

Cited by 15 publications

(16 citation statements)

References 35 publications

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“…Supply chain data provided by (Willems 2008) and analyzed in (Perera et al 2017b) has been used to investigate the applicability of the proposed framework on different supply chain networks. The dataset contains 38 multiechelon supply chains used for inventory optimization purposes.…”

Section: Datasetmentioning

confidence: 99%

“…It is interesting to note that average resilience of the networks synthesized using our approach are generally comparable to or better than the considered real-world networks under both disruption scenarios. (Perera et al 2017b) analyzed the SCNs that we use as target networks and concluded that the networks show high structural resilience. This implies that the actions used in this research can be used to make informed decisions leading to design of resilient supply networks as the synthesized networks have resilience comparable to the target networks.…”

Section: Resilience Analysismentioning

confidence: 99%

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Modeling topologically resilient supply chain networks

Arora

Ventresca

2018

Appl Netw Sci

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The ubiquity of supply chains along with their increasingly interconnected structure has ignited interest in studying supply chain networks through the lens of complex adaptive systems. A particularly important characteristic of supply chains is the desirable goal of sustaining their operation when exposed to unexpected perturbations. Applied network science methods can be used to analyze topological properties of supply chains and propose models for their growth. Network models focusing on the critical aspect of supply chain resilience may provide insights into the design of supply networks that may quickly recover from disruptions. This is vital for understanding both static and dynamic structures of complex supply networks, and enabling management to make informed decisions and prioritizing particular operations. This paper proposes an action-based perspective for creating a compact probabilistic model for a given real-world supply network. The action-based model consists of a set of rules (actions) that a firm may use to connect with other firms, such that the synthesized networks are topologically resilient. Additionally, it captures the heterogeneous roles of different firms by incorporating domain specific constraints. Results analyzing the resilience of networks subjected to node disruptions show that networks synthesized using the proposed model can generally outperform its real-world counterpart.

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Section: Datasetmentioning

confidence: 99%

Section: Resilience Analysismentioning

confidence: 99%

Modeling topologically resilient supply chain networks

Arora

Ventresca

2018

Appl Netw Sci

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“…Due to the difficulty in obtaining large scale datasets on SCNs (supplier-customer relationships), which are often proprietary and confidential, early studies have relied on computer simulations to generate network topologies (through various growth mechanisms) supposedly representative of real world SCNs [3]. Recently however, a number of data driven studies have appeared in literature, which used Bloomberg database to obtain SCN data for publicly listed firms [5,6,7]. These network-based models of complex supply chain systems have shown the existence of non-trivial and universal topological footprints, from which valuable system level insights can be obtained [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

“…Recently however, a number of data driven studies have appeared in literature, which used Bloomberg database to obtain SCN data for publicly listed firms [5,6,7]. These network-based models of complex supply chain systems have shown the existence of non-trivial and universal topological footprints, from which valuable system level insights can be obtained [6,7,8]. For instance, studies have found that the SCNs tend to be scale free in nature (with distinct hub firms), where the distribution of the number of firm level connections in the SCN (i.e.…”

Section: Introductionmentioning

confidence: 99%

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Absorbing Markov Chain Approach to Modelling Disruptions in Supply Chain Networks

Perera

Bell

Kurauchi

et al. 2019

2019 Moratuwa Engineering Research Conference (MERCon)

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Recent developments in the area of network science has encouraged researchers to adopt a topological perspective in modelling Supply Chain Networks (SCNs). While topological models can provide macro level insights into the properties of SCN systems, the lack of specificity due to high level of abstraction in these models limit their real-world applicability, especially in relation to assessing the impact on SCNs arising due to individual firm or supply channel level disruptions. In particular, beyond the topological structure, a more comprehensive method should also incorporate the heterogeneity of various components (i.e. firms and inter-firm links) which together form the SCN. To fill the above gap, this work proposes using the idea of absorbing Markov chains to model disruption impacts on SCNs. Since this method does not require path enumeration to identify the number of supply chains which form the SCN, it is deemed more efficient compared to the other traditional methods. KEY WORDS:supply chain network; absorbing markov chain; supply network disruptions AUTHORS:

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Action-Based Model for Topologically Resilient Supply Networks

Arora

Ventresca

2017

Complex Networks &Amp; Their Applications VI

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Structural characteristics of complex supply chain networks

Cited by 15 publications

References 35 publications

Modeling topologically resilient supply chain networks

Modeling topologically resilient supply chain networks

Absorbing Markov Chain Approach to Modelling Disruptions in Supply Chain Networks

Action-Based Model for Topologically Resilient Supply Networks

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