This paper proposes a methodology for modeling and evaluating supply chains based on generalized stochastic Petri net (GSPN) components. The proposed modeling process is based on a bottom-up approach, which assures desirable model properties that start from a set of predefined modules for typical supply chain entities. A compositional algebra that formally defines GSPN compositions is presented. The proposed methodology is supported by an implementation of the methodology in the stochastic logistics optimizer tool (SLOT). An industrial case study has been conducted to show the benefits of the approach.