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

Supply Chain Coordination Contracts under Double Sided Disruptions Simultaneously

Supply chain coordination models are developed in a two-echelon supply chain with double sided disruptions. In a supply chain system, the supplier may suffer from the product cost disruption and the retailer suffers from the demand disruption simultaneously. The purpose of this study is to design proper supply chain contracts, under which the supply chain with double sided disruption can be coordinated. Firstly, the centralized decision-making models are applied to find the optimal price and quantity under three cases as the baseline. The different cases are divided by the different relationship between the product cost disruption and the demand disruption. Secondly, two different types of contracts are introduced to coordinate the whole supply chain. One is all-unit wholesale quantity discount policy (AQDP) contract, and the other one is capacitated linear pricing policy (CLPP) contract. And it is found out that the gap between the demand disruption and the product cost disruption is the key factor to influence the supply chain coordination. Some numerical examples and sensitivity analysis are given to illustrate the models. The AQDP contracts are listed out under different cases to show how to use it under double sided disruptions

Essays on supply chain analytics: Investment and capacity planning under uncertainty

In this dissertation, we study a firm’s investment and capacity planning strategies in the presence of different types of supply uncertainties and risks. Both essays in this dissertation benefit from empirical analysis as the analytical models build on the findings and observations from the corresponding empirical investigation. Each essay shows the benefits from utilizing flexible options that are deemed to be less preferable before conducting the analysis. Wine futures investment represents the flexible option (due to its liquidity) in the first essay, however, it exhibits greater uncertainty in price than the traditional bottled wine. We find in our empirical analysis that both weather and market fluctuations influence the evolution of the price in wine futures, and thus, despite being the flexible option, it also represents the riskier investment. On the other hand, capacity expansion at a geographically remote facility represents the flexible option (due to its greater backup capabilities) in the second essay, however, it is a more costly backup alternative than a nearby facility. As a result, both essays examine the trade-offs between these flexible, yet risker and/or costlier, alternatives, and shed light on the risk-reward structure of these various operational levers

Robust Design of Supply Network Subject to Disruptions by Considering Congestion Effects

This thesis is focused on the supply chain disruptions and it reviews cost-efficient risk mitigation strategies to sustain supply chain functionality when disruptions occur. In particular, we study the robust design of supply flow subject to minor operational risks and major disruptions. The contingent sourcing along with strategic stock is incorporated as risk management strategies. We consider a firm with two suppliers where the main supplier is cost-effective but prone to disruptions and the back-up supplier is reliable but expensive. The back-up supplier can scale up its capacity according to a speed related to its configuration in order to supply the required flow of material when the main supplier disrupts. When minor disruption occurs, the strategic stock can cover the losses. The design problem considered is to determine optimal strategic stock level and response speed of volume-flexible back-up supplier. The back-up supplier might not provide the required supply level instantaneously due to non-steady production state and congestion during the response time. Therefore, there could be material shortages if the actual level of available capacity during the response time is ignored. The first chapter includes the incorporation of the clearing function into a contingency capacity planning model in order to represent the impact of congestion. The appropriate response speed is selected through a decision tree analysis considering different attitudes of the decision maker towards risk. The results show that considering congestion impact is especially critical for risk-neutral decision makers. The second chapter considers the randomness associated with the available capacity through a two-stage robust optimization model. The results show improvement in the quality of optimal solution by considering the randomness. The objective in the third chapter is to find an equitable solution which has an efficient performance with respect to all plausible scenarios. Therefore, the Ordered Weighted Averaging aggregation operator is incorporated in the objective function of a MIP robust model. In order to address the computational complexity associated with large set of scenarios, a novel clustering based scenario reduction model based on location covering model is proposed. The results show that the proposed methodology provide an accurate reduced scenario set within relatively short computational time