Congestion at major container ports can cause delivery delays, increasing the risk of grocery stockouts further downstream. This paper examines how congestion at the Port of Savannah affects the reliability of shipments on the Savannah–Atlanta replenishment corridor. To study this, a stress-conditioned Monte Carlo inventory model that links a public container throughput proxy (TEU volume) to discrete congestion regimes R∈{−1,0,1,2}, representing low through extreme stress was built. Each stress level alters the distribution of shipment delays, including the probability of rare but severe disruptions.

The model simulates how stochastic lead times translate into inventory depletion and estimates the stockout probability P(stockout∣R,B,s) and the expected shortage across inventory buffer levels B∈{2,3,4,5,6} days of coverage and policy scenarios s∈{Baseline, Safety Stock (+1 day), Tail Mitigation}. Results show that congestion shocks significantly increase the risk of stockouts when buffers are small. Under Extreme stress (R=2) with a low buffer (B=2), baseline stockout risk reaches 12.51%, compared with 1.83% under a one-day safety stock increase and 9.31% under tail-focused mitigation. Shortage severity follows the same pattern: expected shortage falls from 6.370 units in the baseline scenario to 0.849 units with safety stock. When buffers increase to B=3, baseline stockout risk drops to 1.76%, and further to 0.24% with a safety stock.

Overall, the results suggest that increasing buffer coverage is the strongest lever for reducing both the frequency and magnitude of stockouts, while mitigation strategies that reduce extreme delays become more effective once buffer levels are no longer critically low. The framework shows how upstream port congestion indicators can be translated into downstream inventory risk estimates to support practical supply chain planning decisions.