The modern supply chain is long, complex, interconnected and global, and plays a fundamental role in business competitiveness. These conditions, along with various supply chain management trends in recent years have increased risks in supply chains which threaten supply chain performance. Greater impact, especially on cost, from an increased threat of supply disruptions is one area of particular concern. Companies today are struggling to find effective means to manage this increased risk and avoid adverse financial impacts. An approach to managing supply disruption risk in supply chains based on the minimization of the total cost of ownership (TCO) of the supply chain is explored in this thesis. Insights are provided into an appropriate view of supply chain risk and a general four step risk management process to guide the design and evaluation of a new risk management tool based on such an approach. A prototype of the new total cost-based, modeling and simulation tool was created in partnership with ProModel Corporation and a government contractor that requested to remain anonymous. A preliminary assessment of the effectiveness of this tool in minimizing TCO and providing an interface useable by non-modelers is provided. This study also reviews and compares a sample set of current supply chain risk management methods and tools and compares them with the new tool for relevance in aiding users in managing supply disruption risk. Based on literature findings and preliminary feedback from pilot contextual demonstrations of the tool, the total cost approach to risk modeling appears promising, although the execution needs to be improved with further enhancements made to the prototype tool. In this preliminary study and evaluation, sufficient evidence is not available to determine that the new prototype tool is any more effective than other currently available risk management tools to provide necessary information to make supply chain risk management decisions that minimize TCO of a supply chain. Suggestions for further development of the tool, especially for improvement of the total cost approach, are provided as well as a preliminary evaluation procedure and survey instruments for a more robust evaluation of the new tool.

This dissertation studies firms' strategic interactions in anticipation of random service disruption following technology failure. In particular it is aimed at understanding how contracting decisions between a vendor and one or multiple clients affect the firms' subsequent decisions to ensure disruption response and recovery are managed as efficiently as possible. This dissertation consists of three studies that were written as standalone papers seeking to contribute to the literature on contract design and technology management in operations management. Together, the three studies justify the importance of structuring the right incentives to mitigate disruption risks. In the first study we contribute to this literature by means of an analytical model which we use to examine how a client and vendor should balance investments in response capacity when both parties' efforts are critical in resolving disruption and each may have different risk preferences. We study the difference in the client's optimal expected utility between a case in which investment in response capacity is observable and a case in which it is not and refer to the difference in outcomes between the two cases as the cost of complexity. Firstly, we show that the cost of complexity to the client is decreasing in the risk aversion of vendor but increasing in her own risk aversion. Secondly, we find that a larger difference in risk aversion between a client and vendor leads to underinvestment in system uptime in case the client's investment is observable, yet the opposite happens when the client’s investment is not observable. In the second study we further examine the context of the first study through a controlled experiment. We examine how differences in risk aversion and access to information on a contracting partner’s risk preferences interact in affecting contracting and investment decisions between the client and vendor. Comparing subject decisions with the conditionally optimal benchmarks we arrive at two observations that highlight possible heuristic decision biases. Firstly, subjects tend to set and hold on to an inefficiently high investment level even though it is theoretically optimal to adjust decisions under changing differences in risk preferences. Secondly, subjects tend to set and hold on to a penalty that is too high when interacting with more risk averse vendors and too low in case the vendor is equally risk averse. Furthermore, cognitive feedback on the vendor’s risk aversion appears to have counterproductive effects on subject’s performance in the experiment, suggesting cognitive overload can have a reinforcing effect on the heuristic decision biases observed. In the third study we construct a new analytical model to examine the effect of contract design on a provider's response capacity allocation in a setting where multiple clients may be disrupted and available response capacity is limited. The results show that while clients may be incentivized to identify and report network disruptions, competition for scarce emergency resources and the required investment in understanding their own exposure may incentivize clients to deliberately miscommunicate with the vendor.

The purpose of this study is to examine the disruption risk resolution process in supply chains; specifically, to assess how risk attributes impact the approach firms select to resolve risks and the associated final outcomes. We propose that high magnitude risks are positively associated with mutually beneficial problem resolution; on the other hand, low likelihood risks have the opposite effect, they are negatively associated with mutually beneficial resolution. Our conceptual contribution lies in our articulation of the mechanisms though which risk magnitude and risk likelihood impact mutual problem resolution. We posit that high magnitude risks and low likelihood (uncommon) risks mobilize the social network of actors, triggering vigilant monitoring for risks, communication among actors and across firm boundaries, and resource sharing and coordination which facilitate collaborative problem solving and mutual resolutions. These mobilization mechanisms help supply chain partners to overcome the challenges of complexity and allow for information and resource flows among actors and between firms. Our statistical analysis demonstrates that the impact of risk attributes on mutual problem solutions is fully mediated by timely problem identification and collaborative problem solving.

Random disruptions resulting in loss of functionality in service legs or intermodal terminals of transportation networks are an inevitable part of operations, and considering the crucial role of aforementioned networks, it is prudent to strive towards avoiding high-consequence disruption events. The magnitude of the negative impact of a disruption is dependent on component criticality; therefore, limited resources of disruption mitigation should be assigned to the infrastructure with the highest priority. However, categorizing the service legs and terminals based on their actual post-disruption impact is computationally heavy and inefficient. We propose a methodology based on the combination of a bi-objective hazmat shipment planning optimization model and machine learning to identify critical infrastructure more efficiently. The proposed methodology is applied to part of CSX Corporation’s intermodal rail-truck network in the United States as a realistic size problem instance, in order to gain managerial insight and to evaluate the performance of the methodology. / Thesis / Master of Science (MSc)

No description available.

evaluate disruption risks in the supply chain of petroleum production. This methodology is developed to formalise and facilitate the systematic integration and implementation of various models; such as analytical hierarchy process (AHP) and partial least squares structural equation model (PLS-SEM) and various statistical tests. The methodology is validated with the case of Nigeria’s oil industry. The study revealed the need to provide a responsive approach to managing the influence of geopolitical risk factors affecting supply chain in the petroleum production industry. However, the exploration and production risk, and geopolitical risk were identified as concomitant risk factors that impact performance in Nigeria’s oil industry. The research findings show that behavioural-based mechanisms successfully predict the ability of the petroleum industry to manage supply chain risks. The significant implication for this study is that the current theoretical debate on the supply chain risk management creates the understanding of agency theory as a governing mechanism for supply chain risk in the Nigerian oil industry. The systematic approach results provide an insight and objective information for decisions-making in resolving disruption risk to the petroleum supply chain in Nigeria. Furthermore, this study highlights to stakeholders on how to develop supply chain risk management strategies for mitigating and building resilience in the supply chain in the Nigerian oil industry. The developed systematic method is associated with supply chain risk management and performance measure. The approach facilitates an effective way for the stakeholders to plan according to their risk mitigation strategies. This will consistently help the stakeholders to evaluate supply chain risk and respond to disruptions in supply chain. This capability will allow for efficient management of supply chain and provide the organization with quicker response to customer needs, continuity of supply, lower costs of operations and improve return on investment in the Nigeria oil industry. Therefore, the methodology applied provide a new way for implementing good practice for managing disruption risk in supply chain. Further, the systematic approach provides a simplistic modelling process for disruption risk evaluation for researchers and oil industry professionals. This approach would develop a holistic procedure for monitoring and controlling disruption risk in supply chains practices in Nigeria.