Risk Mitigation and Resilience Modeling for Consumer Distribution Networks During Demand Shocks: A Quantitative Stochastic Optimization and Scenario Analysis Study

Abstract

This study investigated risk mitigation and resilience modeling for consumer distribution networks operating under demand shock conditions using a quantitative stochastic optimization and scenario-based analysis framework. A multi-echelon distribution network was evaluated under a structured set of baseline and stress demand scenarios to examine how mitigation policies and resilience-oriented capabilities influenced operational performance. The analytical dataset comprised 1,520 scenario-level network observations, balanced across four service regions and essential and non-essential product segments. Descriptive results indicated a mean service continuity index of 0.81 with observable right-skewness, while the unmet demand ratio averaged 0.18, reaching a maximum of 0.61 under severe stress scenarios. Cost escalation exhibited the greatest variability, with a mean increase of 22.4% and a maximum observed value of 78.6%, confirming strong tail sensitivity in economic outcomes. Reliability analysis of composite constructs demonstrated acceptable to excellent internal consistency, with Cronbach’s alpha values ranging from 0.85 to 0.93, supporting measurement validity prior to inferential testing. Regression analysis showed that resilience capability (β = 0.421, p < 0.001) and robustness stability (β = 0.276, p < 0.001) were statistically significant predictors of performance outcomes after controlling for demand volatility, utilization intensity, and network scale. Inclusion of resilience constructs improved explanatory power, increasing adjusted R² from 0.241 in the baseline model to 0.318 in the extended specification. Segment-level analysis revealed that essential goods achieved higher service continuity (0.86) and lower unmet demand (0.13) than non-essential goods, albeit with higher average cost escalation (26.9% versus 17.1%). Robustness testing using out-of-sample and segmented evaluations confirmed stability of coefficient direction and significance across alternative scenario partitions. Overall, the findings provided quantitative evidence that resilience and robustness functioned as measurable attributes that systematically shaped distribution network performance under demand shock conditions.