Adaptive Storage Scaling for Disruption-Ready Multi-Echelon Networks: A Comprehensive Simulation-Based Framework

Abstract

This paper presents a comprehensive framework for enhancing supply chain resilience through adaptive storage scal-ing in multi-echelon networks facing location-specific disruptions. We investigate the complex interplay between dynamic ordering policies, warehouse expansion strategies, and disruption recovery mechanisms across four-echelon supply chain structures with as-sembly operations. Our sophisticated simulation-based analysis, incorporating real-world data from electronics manufacturing, reveals that downstream disruptions cause significantly longer service degradation periods (280 days vs 120 days for upstream) and persistent inventory imbalances exceeding two years. The research demonstrates that reactive expediting strategies, while commonly employed in industry, amplify system variability and increase total costs by 18-32% compared to optimized dynamic ordering approaches. We propose an integrated resilience play-book combining adaptive safety stock policies, staged distribution center capacity expansion, and cross-echelon parameter opti-mization using metaheuristic techniques. Experimental results show that genetic algorithm-based global optimization achieves 16.3-30.2% cost savings over conventional local search methods, though with higher computational requirements. The framework enables 45% faster post-shock stabilization with 28% lower total costs compared to traditional expediting-led responses, providing actionable insights for supply chain resilience planning in volatile environments.