This study aims to investigate the relationship between market disruptions and supply chain resilience by employing a Vector Autoregression (VAR) model to analyse the dynamic relationships between various economic indicators and forecast the magnitudes of the impact of stochastic disruptions on a supply chains ability to stay resilience . Market disruptions in this case are measured by the disruption of supply on the upscale nods of a supply chain and and demand on the retail side of operations , while supply chain resilience is measured by economic indicators such as, new orders, unfulfilled orders, inventory and production levels. The study utilizes monthly data from 1993 to 2022 for the United States automotive industry. The findings of this study will attempt to test if there is a negative relationship between market uncertainty and supply chain resilience, indicating that as random market disruptions increase, supply chain resilience decreases. Furthermore, the results found with the Impulse response Function (IRF) will attempt to measure the impact of these stochastic disruptions on supply chain resilience in the short run than in the long run. The findings of this study provide valuable insights for businesses and policymakers, highlighting the need for effective risk management strategies to mitigate the negative impact of market uncertainty on supply chain resilience.


Luri, Moses


Economics; Global and International Studies


Operations and Supply Chain Management


supply chain, resilience, vector autoregression, automotive industry, EOQ model, impulse response function

Publication Date


Degree Granted

Bachelor of Arts

Document Type

Senior Independent Study Thesis


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