This dissertation examines the challenges associated with disruptions in supply chain financing and the railway transportation network. The study is divided into six chapters:
In Chapter 1, we introduce the core problems under investigation. Chapter 2 investigates supply chain financing, emphasizing trade credit and bank credit—two predominant external financing mechanisms. Given the inherent uncertainties in demand, interest rates, and supplier credit ratings, this chapter introduces a stochastic programming model accounting for demand uncertainty. Subsequently, a robust optimization program is applied, whose complexity demands a specialized solution methodology. By analyzing a case study centered around a prominent U.S. retailer, the research reveals key insights into decision-making processes related to financing, the effects of bargaining power on portfolio mix and profits, and the relative importance of interest rate uncertainties over supplier credit ratings.
Chapter 3 introduces a game-theoretical model designed to hedge financing risks in supply chains, with a focus on the application of insurance for both trade and bank credits. To support the design of effective supply chain finance contracts, three distinct contracts are developed, aiming to synchronize both financial and material flows within the supply chain. A significant feature of this chapter is the data-driven approach employed to address the potential bankruptcy risks that can arise from borrowing loans. Alongside this, a novel solution algorithm is introduced to solve the proposed non-convex models. A case study involving Ford Motor Company and a Chicago-based retailer enriches the research with real-world context. The findings offer several managerial insights: the strategic advantages of different insurance services vary based on the risk attitudes and profit margins of participants. For example, when a retailer operates with a lower profit margin, the use of Trade Credit Insurance (TCI) is recommended in conjunction with a risk-seeking retailer, while a risk-averse retailer might diminish the benefits of TCI. Conversely, with high profit margin retailers, the adoption of Payment Protection Insurance (PPI) is advised under all conditions.
In Chapter 4, a game-theoretical model for risk mitigation within railway transportation is introduced. This model addresses random disruptions by employing strategies like repair, re-routing, third-party services, and leasing capacity from competing rail companies. Through a U.S. case study, the efficacy of these strategies is examined, with renting railcars emerging as a particularly potent approach to enhance resilience and reduce third-party expenses. The research further suggests that negotiations extending delivery dates can significantly diminish post-disruption costs.
Finally, Chapter 5 summarizes the primary contributions of this research, laying the groundwork for prospective studies in this domain. / Thesis / Doctor of Philosophy (PhD)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/29540 |
| Date | January 2024 |
| Creators | Alavi, Seyyed Hossein |
| Contributors | Verma, Manish, Business Administration |
| Source Sets | McMaster University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
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