Robustness Of Supply Chain Networks Research Articles

Rewiring Strategy Based on Directed Betweenness to Mitigate Disruptions of Large-Scale Supply Chain Networks

In current large-scale supply chain networks, unexpected disruptions degrade the supply availability and network connectivity for modern enterprises. How to improve the robustness of supply chain networks is very important for modern enterprises. In this paper, we explore how to improve the robustness of supply chain networks from a topological perspective. Firstly, through the empirical data-driven study, we show that the directed betweenness metric is more suitable than the other topological metrics in evaluating the robustness of supply chain networks. Then, we propose a rewiring algorithm based on directed betweenness to improve network robustness under the impact of disruptions. The experimental results in the large-scale supply chain network show that the rewiring algorithm based on directed betweenness effectively improves the network robustness.

Robustness of supply chain networks against underload cascading failures

In today’s global economy, supply chain (SC) entities have become increasingly interconnected with demand and supply relationships due to the need for strategic outsourcing. Such interdependence among firms not only increases efficiency but also creates more vulnerabilities in the system. Natural and human-made disasters such as floods and transport accidents may halt operations and lead to economic losses. Due to the interdependence among firms, the adverse effects of any disruption can be amplified and spread throughout the systems. This paper aims at studying the robustness of SC networks against cascading failures. Considering the upper and lower bound load constraints, i.e., inventory and cost, we examine the fraction of failed entities under load decrease and load fluctuation scenarios. The simulation results obtained from synthetic networks and a European supply chain network (Cardoso et al., 2015) both confirm that the recovery strategies of surplus inventory and backup suppliers often adopted in actual SCs can enhance the system robustness, compared with the system without the recovery process. In addition, the system is relatively robust against load fluctuations but is more fragile to demand shocks. For the underload-driven model without the recovery process, we found an occurrence of a discontinuous phase transition. Differently from other systems studied under overload cascading failures, this system is more robust for power-law distributions than uniform distributions of the lower bound parameter for the studied scenarios.

Structural complexity and robustness of supply chain networks based on product architecture

Supply chain networks are dynamic and complex, but also vulnerable. The supply chain network configuration is a context for action and its design should depend on the manufactured product. Hence, this study develops a framework to assess topological complexity and robustness of the supply chain networks taking into consideration the product architecture. The framework includes a morphological analysis and evaluates quantitative indicators of complexity and robustness based on network characteristics. Additionally, overall metrics for complexity and robustness are presented. As a result, a decision matrix is drawn to allow the comparison between different product architectures and their corresponding supply chain network. Moreover, a cost analysis is performed to analyse the trade-offs between complexity vs. cost and robustness vs. cost. The results suggest that complexity is required to achieve robustness and an increase in cost is needed to attain a balanced level of complexity and robustness. The presented framework can be used during supply chain and product design phases to compare different product architectures and supply chain structures and to consider the implications of robustness, complexity and cost of the proposed designs.

Supply Chain Network Robustness Against Disruptions: Topological Analysis, Measurement, and Optimization

This paper focuses on understanding the robustness of a supply network in the face of a disruption. We propose a decision support system for analyzing the robustness of supply chain networks against disruptions using topological analysis, performance measurement relevant to a supply chain context, and an optimization for increasing supply network performance. The topology of a supply chain network has considerable implications for its robustness in the presence of disruptions. The system allows decision makers to evaluate topologies of their supply chain networks in a variety of disruption scenarios, thereby proactively managing the supply chain network to understand vulnerabilities of the network before a disruption occurs. Our system calculates performance measurements for a supply chain network in the face of disruptions and provides both topological metrics (through network analysis) and operational metrics (through an optimization model). Through an example application, we evaluate the impact of random and targeted disruptions on the robustness of a supply chain network.

Exploring the Impact of Network Structure and Demand Collaboration on the Dynamics of a Supply Chain Network Using a Robust Control Approach

A supply chain network might exhibit complex dynamics in the face of increasingly volatile and uncertain environment. The impact of network structure and collaboration on the dynamics and robustness of supply chain network, however, remains to be explored. In this paper, a unified state space model for a two-layer supply chain network composed of multiple distributors and multiple retailers is developed. A robust control algorithm is advocated to reduce both order and demand fluctuations for unknown demand. Numerical simulations demonstrate that the robust control approach has the advantage to reduce both inventory and order fluctuations. In the simulation experiment, it is interesting to notice that complex network structure and collaborations might contribute to the reduction of inventory and order oscillations. This paper yields new insights into the overestimated bullwhip effect problem and helps us understand the complexities of supply chain networks.

Development of supply chain network robustness index

As an event-driven system, a supply chain network will face not only the uncertainties in the supply chain, but also the unexpected events outside the supply chain network such as contingency, disruption, disaster and terrorism. These uncertainties and unexpected events have negative impacts on the survival and performance of a supply chain network. Robustness of supply chain networks is an important research issue as a vulnerable supply chain network may not be able to operate at all. While there has been extensive work on the measurement of supply chain performance, little of this work has focused on measuring a supply chain's robustness, that is, the ability to cope with deviation and disruption. This paper presents a system-wide approach to quantifying robustness index of supply chain networks. This approach considers both network structural robustness and network functional robustness. A hypothetical supply chain network is employed to demonstrate the proposed approach.