Evaluation and Optimization of Manufacturing Supply Chain Resilience
Abstract
Digital transformation can improve supply chain sensing, but recovery depends on whether reliable information is converted into coordinated operational action. This study develops a simulation-based case study that integrates data-quality-adjusted indicator fusion, a six-dimensional resilience index, system dynamics, and phase-based constrained policy search. The case represents a component-intensive discrete-manufacturing network with 28 tier-1 suppliers, nine qualified alternatives, seven logistics nodes, and five product families. A reproducible 36-month supplier-product panel (5040 unit-month records) is generated from a fixed seed and an explicit machine-readable configuration; it is not presented as confidential company data. Completeness, timeliness, cross-source consistency, and out-of-sample predictive contribution are defined explicitly, and an event-preserving gate prevents reliability shrinkage from attenuating logged disruption signals. Twelve indicators measure robustness, redundancy, agility, visibility, collaboration, and adaptive recovery; time-to-recovery is reserved as an outcome rather than included in the input index. The dynamic model is solved at a 0.25-month step over a 24-month policy horizon. Under a compound supplier-capacity, demand, and logistics shock, the balanced phase-based portfolio increases minimum resilience from 0.490 to 0.680 and reduces time-to-recovery from 8.4 to 3.5 months relative to the efficiency baseline. At an equal 6.9% incremental-cost budget, the integrated portfolio retains a 0.029–0.071 advantage in minimum resilience over single-mechanism alternatives. Holdout replay, alternative weighting schemes, event-gate tests, parameter perturbations, and unseen shock combinations establish numerical robustness but do not constitute external empirical validation. The findings indicate, for this specified model and case, that visibility creates resilience value when coupled with response authority, supplier coordination, flexible capacity, and targeted buffers.