Utilizing simulations offers a comprehensive set of benefits alongside several inherent drawbacks that organizations must carefully weigh before implementation. On the positive side, simulations allow decision-makers to safely test complex scenarios, explore "what-if" outcomes, and optimize operational workflows without risking physical capital, equipment, or human safety. They significantly reduce the costs and time required compared to building physical prototypes or running massive real-world trials. However, simulations also come with notable disadvantages, including the heavy reliance on accurate underlying assumptions and historical data; if the initial parameters are flawed, the output results will be equally inaccurate, a principle known as garbage in, garbage out. Furthermore, developing high-fidelity simulation models often requires specialized expertise, sophisticated programming skills, and expensive software licenses. Interpreting complex outputs can also be prone to human bias or over-reliance, leading stakeholders to mistakenly treat a virtual model as an absolute guarantee of real-world performance rather than an approximation.