The construction of mathematical models includes a variety of strategies for closing the gap between the model and real-time data. In this study, a data mining technique is used to expand mathematical model construction to include simulation optimization. At the same time, this procedure is used to verify the mathematical model. In addition to dynamic property validation, the paper suggests mathematical programming as a static property validation method. The validated mathematical model is compared and examined with real-time data, and the results show that the mathematical model is applicable to real-time systems. The mathematical model has a low error margin estimate and can be used to forecast. The paper's main contribution is to investigate simulation optimization, mathematical programming, and mathematical models to handle increasing complexity of variables in order to improve prediction capabilities and lay a solid foundation for tackling factory planning issues.