Supply Chain Networks
2026-07-20 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors developed a math-based model to study how unpredictable demand and risks spread through complex supply chains. They showed that the Bullwhip effect—a phenomenon where small changes in demand cause big fluctuations in orders—is a natural part of supply chain networks, not just due to noise or poor information. They also included price changes and market feedback to explain how connected issues can worsen quickly when resources are limited. Finally, they applied their model to global oil trade and demonstrated how a disruption in a key route can cause widespread shortages and adjustments across countries over time.
Bullwhip effectNewsvendor modelSupply chain networkStochastic networksSkorokhod reflection problemPrice elasticityMarket-clearing feedbackCapacity bottlenecksCascading failuresGlobal oil trade
Authors
Elioth Sanabria
Abstract
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.