Using complex network theory to model supply chain network resilience: a review of current literature

Traditionally, supply networks are modelled as multi-agent systems, in order to represent explicit communications between various entities involved. However, due to the increasingly complex and interconnected nature of the global supply networks, a recent trend of research work has focussed on modelling supply networks as complex adaptive systems. This approach has enabled researchers to investigate various topological properties which give rise to resilience characteristics in a given supply network. This paper presents a critical review of the published research work on this field. Key insights provided by this paper include; (1) the importance of defining the concepts of ‘resilience’ and ‘disruptions’ as measurable variables; (2) the limitations of existing network models to realistically represent supply networks; (3) potential improvements to the currently used growth mechanisms, which rely on node ‘degree’ to derive attachment probability instead of the more realistic and relevant node ‘fitness’; (4) importance of incorporating operational aspects, such as flows, costs, and capacities of connections between the nodes as well as topological aspects; and (5) derivation of a new set of resilience metrics capturing operational as well as topological aspects. Finally, a conceptual approach incorporating the above improvements to the existing supply network modelling approach is presented

The moderating impact of supply network topology on the effectiveness of risk management

While supply chain risk management offers a rich toolset for dealing with risk at the dyadic level, less attention has been given to the effectiveness of risk management in complex supply networks. We bridge this gap by building an agent based model to explore the relationship between topological characteristics of complex supply networks and their ability to recover through inventory mitigation and contingent rerouting. We simulate upstream supply networks, where each agent represents a supplier. Suppliers’ connectivity patterns are generated through random and preferential attachment models. Each supplier manages its inventory using an anchor-and-adjust ordering policy. We then randomly disrupt suppliers and observe how different topologies recover when risk management strategies are applied. Our results show that topology has a moderating effect on the effectiveness of risk management strategies. Scale-free supply networks generate lower costs, have higher fill-rates, and need less inventory to recover when exposed to random disruptions than random networks. Random networks need significantly more inventory distributed across the network to achieve the same fill rates as scale-free networks. Inventory mitigation improves fill-rate more than contingent rerouting regardless of network topology. Contingent rerouting is not effective for scale-free networks due to the low number of alternative suppliers, particularly for short-lasting disruptions. We also find that applying inventory mitigation to the most disrupted suppliers is only effective when the network is exposed to frequent disruptions; and not cost effective otherwise. Our work contributes to the emerging field of research on the relationship between complex supply network topology and resilience

Development of a Framework to Support Informed Shipbuilding Based on Supply Chain Disruptions

In addition to stresses induced by the Covid-19 pandemic, supply chains worldwide have been growing more complex while facing a continuous onslaught of disruptions. This paper presents an analysis and extension of a graph based model for modeling and simulating the effects of such disruptions. The graph based model combines a Bayesian network approach for simulating risks with a network dependency analysis approach for simulating the propagation of disruptions through the network over time. The initial analysis provides evidence supporting extension to for using a multi-layered approach allowing for the inclusion of cyclic features in supply chain models. Initial results for individual layers and the aggregate model are presented and discussed. The paper is concluded with a discussion and recommended directions for future work

Supply network science: Emergence of a new perspective on a classical field.

Supply networks emerge as companies procure goods from one another to produce their own products. Due to a chronic lack of data, studies on these emergent structures have long focussed on local neighbourhoods, assuming simple, chain-like structures. However, studies conducted since 2001 have shown that supply chains are indeed complex networks that exhibit similar organisational patterns to other network types. In this paper, we present a critical review of theoretical and model based studies which conceptualise supply chains from a network science perspective, showing that empirical data do not always support theoretical models that were developed, and argue that different industrial settings may present different characteristics. Consequently, a need that arises is the development and reconciliation of interpretation across different supply network layers such as contractual relations, material flow, financial links, and co-patenting, as these different projections tend to remain in disciplinary siloes. Other gaps include a lack of null models that show whether the observed properties are meaningful, a lack of dynamical models that can inform how layers evolve and adopt to changes, and a lack of studies that investigate how local decisions enable emergent outcomes. We conclude by asking the network science community to help bridge these gaps by engaging with this important area of research

Resilence of complex supply networks

During recent decades supply chains have grown, and became increasingly interconnected due to globalisation and outsourcing. Empirical and theoretical studies now characterise supply chains as complex networks rather than the hierarchical, linear chain structures often theorised in classical literature. Increased topological complexity resulted in an increased exposure to risk, however existing supply chain risk management methodologies are designed based on the linear structure assumption rather than interdependent network structures. There is a growing need to better understand the complexities of supply networks, and how to identify, measure and mitigate risks more efficiently. The aim of this thesis is to identify how supply network topology influences resilience. More specifically, how applying well-established supply chain risk management strategies can decrease disruption impact in different supply network topologies. The influence of supply network topology on resilience is captured using a dynamic agent-based model based on empirical and theoretical supply network structures, without a single entity controlling the whole system where each supplier is an independent decision-maker. These suppliers are then disrupted using various disruption scenarios. Suppliers in the network then apply inventory mitigation and contingent rerouting to decrease impact of disruptions on the rest of the network. To the best of author’s knowledge, this is the first time the impact of random disruptions and its reduction through risk management strategies in different supply network topologies have been assessed in a fully dynamic, interconnected environment. The main lessons from this work are as follows: It has been observed that the supply network topology plays a crucial role in reducing impact of disruptions. Some supply network topologies are more resilient to random disruptions as they better fulfil customer demand under perturbations. Under random disruptions, inventory mitigation is a well-performing shock absorption mechanism. Contingent rerouting, on the other hand, is a strategy that needs specific conditions to work well. Firstly, the strategy must be applied by companies in supply topologies where the majority of supply chain members have alternative suppliers. Secondly, contingent rerouting is only efficient in cases when the reaction time to supplier’s disruption is shorter than the duration of the disruption. It has also been observed that the topological position of the individual company who applies specific risk management strategy heavily impacts costs and fill-rates of the overall system. This property is moderated by other variables such as disruption duration, disruption frequency and the chosen risk management strategy. An additional, important lesson here is that, choosing the supplier that suffered the most from disruptions or have specific topological position in a network to apply a risk management strategy might not always decrease the costs incurred by the whole system. In contrast, it might increase it if not applied appropriately. This thesis underpins the significance of topology in supply network resilience. The results from this work are foundational to the claim that it is possible to design an extended supply network that will be able reduce the impact of certain disruption types. However, the design must consider topological properties as well as moderating variables.PhD in Manufacturin

A network tool to analyse and improve robustness of system architectures

The architecture of a system is decided at the initial stage of the design. However, the robustness of the system is not usually assessed in detail during the initial stages, and the exploration of alternative system architectures is limited due to the influence of previous designs and opinions. This article presents a novel network generator that enables the analysis of the robustness of alternative system architectures in the initial stages of design. The generator is proposed as a network tool for system architectures dictated by their configuration of source and sink components structured in a way to deliver a particular functionality. Its parameters allow exploration with theoretical patterns to define the main structure and hub structure, vary the number, size, and connectivity of hub components, define source and sink components and directionality at the hub level and adapt a redundancy threshold criterion. The methodology in this article assesses the system architecture patterns through robustness and modularity network based metrics and methods. Two naval distributed engineering system architectures are examined as the basis of reference for the simulated networks. The generator provides the capacity to create alternative complex system architecture options with identifiable patterns and key features, aiding in a broader explorative and analytical, in-depth, time and cost-efficient initial design process

A network science approach to analysing manufacturing sector supply chain networks: Insights on topology

Due to the increasingly complex nature of the modern supply chain networks (SCNs), a recent research trend has focussed on modelling SCNs as complex adaptive systems. Despite the substantial number of studies devoted to such hypothetical modelling efforts, studies analysing the topological properties of real world SCNs have been relatively rare, mainly due to the scarcity of data. This paper aims to analyse the topological properties of twenty-six SCNs from the manufacturing sector. Moreover, this study aims to establish a general set of topological characteristics that can be observed in real world SCNs from the manufacturing sector, so that future theoretical work modelling the growth of SCNs in this sector can mimic these observations. It is found that the manufacturing sector SCNs tend to be scale free with degree exponents below two, tending towards hub and spoke configuration, as opposed to most other scale-free networks which have degree exponents above two. This observation becomes significant, since the importance of the degree exponent threshold of two in shaping the growth process of networks is well understood in network science. Other observed topological characteristics of the SCNs include disassortative mixing (in terms of node degree as well as node characteristics) and high modularity. In some networks, we find that node centrality is strongly correlated with the value added by each node to the supply chain. Since the growth mechanism that is most widely used to model the evolution of SCNs, the Barabasi - Albert model, does not generate scale-free topologies with degree exponent below two, it is concluded that a novel mechanism to model the growth of SCNs is required to be developed