This paper has presented a simulation-based framework for analyzing supply chain reliability under uncertainty, supported by a comprehensive literature review, theoretical grounding, and a real-world case study. The main contribution lies in demonstrating how simulation modeling — particularly hybrid approaches — can capture the complex dynamics of modern supply chains and provide decision-makers with practical tools for stress-testing, scenario planning, and reliability enhancement.

This study aims to evaluate supply chain reliability under uncertainty by integrating simulation and probabilistic modeling. The purpose is to investigate how disruptions in supply, demand, and logistics affect performance indicators such as service level, recovery time, costs, and lost sales, thereby offering insights for resilience planning. The theoretical foundation builds on contingency theory, complex adaptive systems, the resource-based view, and risk management frameworks to capture the dynamic and interdependent nature of supply chains.

This study aims to develop a comprehensive simulation-based framework for analyzing and enhancing supply chain reliability under conditions of uncertainty. It differs from previous studies in that they addressed individual elements such as supplier disruptions or logistics, while this study considers them collectively, making it more comprehensive. Furthermore, it utilizes simulations of discrete events to measure the impact of interactions and support decision-making.

The research used the analytical method in order to formulate hypotheses and build simulation models to reach the results in order to formulate the practical part. The main reason behind relying on the simulation method in this study is the limited availability of actual data needed to build an integrated statistical or standard model, in addition to the small sample size, which does not allow for accurate and reliable statistical analyses.

The simulation results underscore that supply chain reliability cannot be assured through isolated optimization efforts. Instead, organizations must adopt systems thinking, where risks in supply, demand, and logistics are assessed in an integrated manner. The conceptual framework developed in Section 3 and validated through the case study in Section 5 serves as a replicable methodology for similar assessments in other industries.