Monte Carlo Simulation is a quantitative technique used in Operations Research to analyze complex systems involving uncertainty and randomness. It uses repeated random sampling and probability distributions to estimate possible outcomes of a decision problem. Unlike mathematical optimization techniques, which seek the best possible solution under defined conditions, Monte Carlo Simulation focuses on evaluating the behaviour of a system under uncertain conditions and measuring the likelihood of different outcomes. Monte Carlo Simulation is preferred over mathematical optimization techniques in situations where the system is highly complex and cannot be easily represented through mathematical equations. Many real-world problems involve multiple uncertain variables, random events, and dynamic interactions that make traditional optimization models difficult to develop. In such cases, simulation provides a practical approach for understanding system performance. One important situation where Monte Carlo Simulation is preferred is when input variables are uncertain or probabilistic. Mathematical optimization methods generally require fixed values for parameters, whereas many business and industrial problems involve uncertainty in demand, costs, market conditions, production times, or resource availability. Monte Carlo Simulation can incorporate probability distributions for these uncertain factors and provide