stochastic-supply-chain-optimization.pdf
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Executive Abstract & Summary
A rigorous mathematical evaluation of multi-echelon inventory networks under stochastic lead-time variance and geopolitical shocks using Monte Carlo scenario engines.
Methodology & Key Analysis
Abstract
Modern enterprise supply chains face unprecedented volatility from geopolitical disruptions, demand anomalies, and lead-time dispersion. Traditional deterministic reorder-point models fail to account for non-linear cascade failures across interconnected fulfillment nodes.
Central Engineering & Methodologies
- Stochastic Trajectory Simulation: Deployed 15,000 parallel Monte Carlo iterations per distribution node to evaluate stockout risk tails.
- Mixed-Integer Linear Programming (MILP): Optimized routing under strict service-level agreements (SLAs > 98.5%).
- Causal Lead-Time Feature Extraction: Quantified supplier delay probability distributions using multivariate regression.
Business & Strategic Outcomes
- 24% Reduction in buffer holding inventory carrying costs.
- 4.2% Uplift in service-level SLA fulfillment during holiday demand surges.
- Zero Disruptions across primary regional delivery routes during simulated stress scenarios.