Browsing by Author "Bell, M"

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Embargo
Absorbing Markov Chain Approach to Modelling Disruptions in Supply Chain Networks
(IEEE, 2019-08) Perera, S; Bell, M; Kurauchi, F; Kasthurirathna, D
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.
Open Access
Influence of Supply Chain Network Topology on the Evolution of Firm Strategies
(Institute of Transport and Logistic Studies (ITLS), 2020-01-01) Perera, S; Kasthurirathna, D; Bell, M
This study investigates the influence of the topological structure of a supply chain network (SCN) on the evolution of cooperative and defective strategies adopted by the individual firms. First, a range of topologies representative of SCNs was generated using a fitness-based network growth model, which enabled cross comparisons by parameterising the network topologies with the power law exponent of their respective degree distributions. Then, the inter-firm links in each SCN were considered as repeated strategic interactions and were modelled by the Prisoner’s Dilemma game to represent the self-interested nature of the individual firms. This model is considered an agent-based model, where the agents are bound to their local neighbourhood by the network topology. A novel strategy update rule was then introduced to mimic the behaviour of firms. In particular, the heterogeneously distributed nature of the firm rationality was considered when they update their strategies at the end of each game round. Additionally, the payoff comparison against the neighbours was modelled to be strategy specific as opposed to accumulated payoff comparison analysis adopted in past work. It was found that the SCN topology, the level of rationality of firms and the relative strategy payoff differences are all essential elements in the evolution of cooperation. In summary, a tipping point was found in terms of the power law exponent of the SCN degree distribution, for achieving the highest number of cooperators. When the connection distribution of an SCN is highly unbalanced (such as in hub and spoke topologies) or well balanced (such as in random topologies), more difficult it is to achieve higher levels of co-operation among the firms. It was concluded that the scale-free topologies provide the best balance of hubs firms and lesser connected firms. Therefore, scale-free topologies are capable of achieving the highest proportion of cooperators in the firm population compared to other network topologies.
Embargo
Topological rationality of supply chain networks
(Taylor & Francis, 2020-05-18) Kasthurirathna, D; Perera, S; Bell, M
In this study, we apply a topologically distributed bounded rationality model to quantify the level of rationality in supply chain networks. We use the averaged Jensen-Shannon divergence values between Nash and Quantal Response equilibria for all inter-firm strategic interactions, which are represented as Prisoner’s Dilemma games, to characterise the average level of rationality in a given supply chain network. This is based on the game theoretic assumption that as the rationality of a particular interaction increases, it converges towards Nash equilibrium, in a certain strategic decision making scenario. Using this model, we demonstrate that hub-and-spoke topologies are collectively more rational compared to scale-free and random network topologies. Finally, we compare our theoretical results against the empirical findings reported for networked systems in various domains. In particular, it is shown that network topologies comprising higher average rationality levels emerge under increasingly competitive environments.