Mathematical Economic Model in Financial System in Manufacturing Industry- An Application

Abstract

This paper provides a comprehensive analysis of the application of mathematical economic models to address critical financial challenges within the manufacturing industry. The work demonstrates that these models are not merely theoretical constructs but essential tools for enhancing precision, mitigating risk, and optimizing operational and strategic decisions. The methodology focuses on two distinct, yet complementary, classes of models: linear programming for optimizing short-term operational functions, such as production and inventory management, and stochastic modelling, particularly Monte Carlo simulation, for evaluating long-term, capital-intensive projects with significant uncertainty. A detailed case study illustrates the practical application of both modelling approaches to a hypothetical manufacturing scenario, moving from model formulation to the interpretation of results. The analysis highlights the power of these models to move beyond simplistic, single point estimates by providing a full distribution of potential outcomes, quantifying risk, and identifying the most critical sources of variability. The paper concludes that the successful implementation of these models requires a fundamental shift toward data integration and cross-functional collaboration, ultimately providing a robust, data-driven framework for modern manufacturing financial management.