Supply Chain Networks

This study provides a quantitative framework for analysis of systemic demand uncertainty and risk propagation cascades across general supply chain networks.

By leveraging properties derived from stochastic networks embedded within a Newsvendor paradigm, we model multi-echelon networks under equilibrium and transient operational regimes.

We mathematically validate that the systemic volatility behavior commonly referred to as the Bullwhip effect persists entirely as an unavoidable, inherent topological property of coordinated logistics networks, independent of traditional operational noise or information visibility constraints.

Extending this paradigm to transient environments, we model inventory drawdown horizons as a multi-dimensional Skorokhod reflection problem.

Crucially, we endogenize market-clearing feedback loops by incorporating non-linear price elasticity mechanisms and dynamic trade relation rebalancing, demonstrating how decentralized rational actions co-evolve with physical capacity bottlenecks to accelerate systemic network degradation.

Finally, we operationalize the framework through a data-driven numerical experiment mapping global oil trade dynamics, showing how localized chokepoint disruptions, such as a capacity shock in the Strait of Hormuz, trigger non-linear cascading stockouts and systemic reallocation across sovereign buffers over time.