Optimal timing for supplier replacement to mitigate the ripple effect of cruise supply chain disruptions: a novel integrated analytical framework

Existing studies have examined various measures to mitigate the ripple effect of supply chain disruptions, but few have focused on the optimal timing for supplier replacement, particularly in cruise supply chains. This study proposes a novel analytical framework that, for the first time, integrates causal dynamic Bayesian networks, Do-calculus, and mathematical programming to assess supplier replacement timing for controlling disruption propagation. First, a supply chain disruption ripple effect model is constructed: a dynamic Bayesian network captures the ripple effect without supplier replacement, while the causal dynamic Bayesian network and Do-calculus capture the ripple effect under supplier replacement. Building on this model, three models of supply chain risk, service level, and cost are developed. Then, a multi-objective non-convex mixed-integer programming model is formulated to determine the optimal supplier replacement timing, aiming to minimise risk, maximise service level, and minimise cost. Finally, the empirical analysis of cruise supply chain operations shows that risk threshold settings influence the timing and frequency of supplier replacement. This leads to a nonlinear relationship among cost, risk, and service level. Specifically, this relationship manifests as phased improvements, temporary fluctuations due to increased strategic complexity, and diminishing marginal returns as cost inputs rise.