In the literature review we explain the modeling process before discussing the different simulation and modeling methods and interpreting the methods in enterprise-level modeling.I. Modeling Process: The powerful technique that allows researchers with diverse backgrounds to analyze and study complex phenomena is modeling. In general the model is "A (small) finite description of an infinitely complex reality, constructed for the purpose of answering particular questions" (Kuipers 1994). Although in each scientific discipline the steps adopted in the modeling process differ, the general modeling method follows is: Step 1: Model identification – this step aims to identify the objectives of the study and the best method to model a particular event. This phase also involves defining the boundaries of the model, i.e. identifying the key variables and requirements of the model's scope, time frame and reference mode. Step 2: Model Building: Aims to represent real-world influences between variables of interest in an appropriate layout. This can be applied using a quantitative approach such as defining a system of simultaneous ordinary differential equations (ODE) or linear programming or a qualitative approach such as representing structural dependencies using causal diagrams. There are other hybrid methods that can be adopted, which combine quantitative and qualitative techniques. Phase 3: Analysis and interpretation of the model – which provides the derivation of results for the mathematical equations and/or the simulation of the relationships between the variables, which in turn provides the solution for particular research questions or criteria that have been established at start of the process. Additional steps may be needed such as template…half of the document…when template variables are not explicitly defined and therefore may not be easy to parse.3. Other hybrid approaches: Various hybrid approaches are the macroeconomic model (IMEM) of Chen and Sun (2000) which combines quantitative and qualitative reasoning methods to model the economy, fuzzy cognitive maps (a combination of fuzzy logic and neural networks) of Stylios and Groumpos (1998) which explained the method of modeling a two-level production control system and the explanation of the application of mixed qualitative/quantitative modeling approaches combining principal component analysis, together with grouped fuzzy diagrams and modeling reasoning for a continuous stirred tank reactor and distillation column by Yadegar and Pishvaie (2005). A utility cost function is also suggested, which can be used to estimate the correctness of alternative modeling techniques.