RISK-AWARE SUPPLIER SELECTION IN MULTI-TIER SUPPLY CHAINS

<p>Manufacturers rely on effective risk assessment to mitigate supplier disruptions and transportation challenges. Recent legislation requiring greater upstream supply chain disclosure provides new opportunities for comprehensive risk analysis. However, existing models often focus on tier 1 suppliers, overlooking risks that may arise from upstream suppliers in the commonly multi-tiered supply networks. This study presents a risk-aware optimization framework integrating supplier selection, order allocation, and risk propagation analysis. It starts with a pre-processing step that leverages open-access data enabled by transparency mandates to assess supplier risk across a multi-tier network, using factors such as geographic vulnerabilities, social compliance, and financial stability; this step is performed pre-optimization to allow for scalability. It then feeds into an optimization stage that determines the optimal supplier selection and order allocation for a given product type, primarily aiming at minimizing supplier disruption risk, with variants that also consider cost, demand and/or supplier uncertainty, transparency, and transportation-related risk. This thesis uses U.S. government datasets on suppliers that provide military field rations, known as Meals Ready to Eat (MREs), as a case study for defense supply chains. It compares two MRE menus—that we will refer to as Menu A and Menu B—each supported by a multi-tier supply chain that includes both meal ingredients and packaging components. Results from the MRE supply chain datasets show notable improvements over the current U.S. government supplier configurations. On an extended Menu A dataset, the proposed stochastic model achieved approximately 10.6% lower supplier risk and 1% lower transportation risk, with only a 0.1% increase in cost. In comparison, on the Menu B dataset, the stochastic model achieved roughly 33% lower supplier risk, with just a 0.2% cost increase. In addition, the stochastic model demonstrated robust performance across 1,000 disruption scenarios, including natural disasters, pandemics, and other supply chain shocks. This work demonstrates that significant risk reductions can be achieved through transparent, data-driven, multi-tier strategies, providing supply chain managers with an effective tool for optimizing decisions in multi-tier networks.</p>