Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Abigail Butcher, Junior
Lead Presenter's Name
Abigail Butcher
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Dr. Bryan Watson
Abstract
Understanding stability in complex supply chains remains a critical challenge due to nonlinear feedback, delayed responses, and sensitivity to parameter changes. This project presents a novel framework that applies bifurcation analysis to evaluate system stability, using the Oregonator autocatalytic chemical reaction model as an analog for supply chain dynamics. A parameter sweep of key model variables, particularly the stoichiometric factor f and the reaction rate constants k, is used to identify transitions between stable and oscillatory regimes. These transitions provide insight into how variations in feedback strength can drive instability in real-world systems. The framework will then be extended to real-world applications, with emphasis on industries such as steel manufacturing, where demand variability and delayed response times create conditions analogous to reaction-rate imbalances in the Oregonator model. By mapping chemical reaction dynamics to supply chain behavior, this work provides a new perspective on identifying critical thresholds, informing policy decisions, and estimating safety stock requirements. This research demonstrates a preliminary view of how nonlinear dynamical models offer a powerful tool for understanding and improving supply chain resilience, particularly in systems prone to oscillations and abrupt transitions.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Dynamic Systems Commons, Operations and Supply Chain Management Commons, Systems Science Commons
Supply Chain Analysis: The Oregonator Autocatalytic Case Study
Understanding stability in complex supply chains remains a critical challenge due to nonlinear feedback, delayed responses, and sensitivity to parameter changes. This project presents a novel framework that applies bifurcation analysis to evaluate system stability, using the Oregonator autocatalytic chemical reaction model as an analog for supply chain dynamics. A parameter sweep of key model variables, particularly the stoichiometric factor f and the reaction rate constants k, is used to identify transitions between stable and oscillatory regimes. These transitions provide insight into how variations in feedback strength can drive instability in real-world systems. The framework will then be extended to real-world applications, with emphasis on industries such as steel manufacturing, where demand variability and delayed response times create conditions analogous to reaction-rate imbalances in the Oregonator model. By mapping chemical reaction dynamics to supply chain behavior, this work provides a new perspective on identifying critical thresholds, informing policy decisions, and estimating safety stock requirements. This research demonstrates a preliminary view of how nonlinear dynamical models offer a powerful tool for understanding and improving supply chain resilience, particularly in systems prone to oscillations and abrupt transitions.