This dissertation consists of three essays studying three closely related aspects of supply chain resilience. The first essay is "Value of Supply Disruption Information and Information Accuracy", in which we examine the factors that influence the value of supply disruption information, investigate how information accuracy influences this value, and provide managerial suggestions to practitioners. The study is motivated by the fact that fully accurate disruption information may be difficult and costly to obtain and inaccurate disruption information can decrease the financial benefit of prior knowledge and even lead to negative performance. We perform the analysis by adopting a newsvendor model. The results show that information accuracy, specifically information bias and information variance, plays an important role in determining the value of disruption information. However, this influence varies at different levels of disruption severity and resilience capacity. The second essay is "Quantifying Supply Chain Resilience: A Dynamic Approach", in which we provide a new type of quantitative framework for assessing network resilience. This framework includes three basic elements: robustness, recoverability and resilience, which can be assessed with respect to different performance measures. Then we present a comprehensive analysis on how network structure and other parameters influence these different elements. The results of this analysis clearly show that both researchers and practitioners should be aware of the possible tradeoffs among different aspects of supply chain resilience. The ability of the framework to support better decision making is then illustrated through a systemic analysis based on a real supply chain network. The third essay is "Network Characteristics and Supply Chain Disruption Resilience", in which we investigate the relationships between network characteristics and supply chain resilience. In this work, we first prove that investigating network characteristics can lead to a better understanding of supply chain resilience behaviors. Later we select key characteristics that play a critical role in determining network resilience. We then construct the regression and decision tree models of different supply chain resilience measures, which can be used to estimate supply chain network resilience given the key influential characteristics. Finally, we conduct a case study to examine the estimation accuracy. / Ph. D. / With the trend of industry globalization and regional specification, supply chain networks are becoming more complex and thus more vulnerable to disruptions. The situation is potentially worsened because of dynamic risk diffusion, which is a phenomenon that involves the propagation of a disruption from a company to its suppliers and customers. Disruptions in complex supply chain networks, together with this dynamic risk diffusion process, are hard to predict and difficult to manage. Thus, it is particularly important for supply chains to have resilience capabilities. Supply chain resilience has been a fast-evolving research topic in recent years. Compared with traditional supply chain risk management, which focuses on controlling the risk of disruptions, supply chain resilience emphasizes a supply chain’s capability to be well prepared for, quickly respond to, and recover from a disruption. This forward-looking perspective requires supply chain managers to have a good understanding of both disruptions and their supply chain network in order to build resilience. Based on this perspective, we conduct three studies on disruption information and supply chain network structure in order to contribute to a better understanding of the concept of supply chain resilience. In the first chapter, we aim to provide insights into how information accuracy influences the value of disruption information, which can support better decision making about information investment. As network structure is also critical to supply chain resilience, we then examine the relationship between network structure and supply chain resilience in chapters 3 and chapter 4. Understanding how network structure and, in particular, the key characteristics that define that structure impact supply chain resilience can allow practitioners to design more resilient supply chain networks and achieve resilience without too many additional resources. Although our models are simplified versions of reality, these studies establish a solid foundation for understanding supply chain resilience, and for evaluating different risk mitigation and recovery strategies, hence they can support more effective decision making in practice.

The sustainability of Social Responsible Investments (SRI) markets is highly dependent on the accuracy of social and environmental information. Surprisingly, and in contrast to financial information, there exists no methodological standard for gathering social and environmental information in SRI markets. This work is a first contribution to the understanding of how SRI-analysts verify the accuracy of social and environmental information. A second aim of this thesis is to evaluate if SRI-analysts can produce an accurate output with their respective methodologies. To do so, a case study was performed comparing the assessment of social and environmental information at SRI-screening firms to the more regulated financial auditing process, using legal a categorization of evidence strengths as a model. The findings of this study suggest that practices are not standardized amongst SRI-analysts. Therefore, investors are unlikely to receive an equal degree of information accuracy from one analyst to the next. Moreover, when comparing SRI-screening and financial auditing using the legal categorization of evidence, it was found that screening firms tend to produce outputs that are less carefully verified than seen in their financial counterparts. Nevertheless, the findings also reveal that SRI-analysts generally acknowledge the importance of assessing sources of evidence when controlling information accuracy. In conclusion, a standardized methodology should be welcomed by SRI-analysts, and the legal categorization of evidence strengths could be a good starting point to manage information accuracy in their screening process.