Certified in Logistics, Transportation and Distribution (CLTD) 2025 – 400 Free Practice Questions to Pass the Exam

The process that uses probability distributions to anticipate performance outcomes is Monte Carlo Simulation. This technique employs random sampling and statistical modeling to estimate the likelihood of different outcomes in a process that cannot easily be predicted due to the intervention of random variables.

Monte Carlo Simulation assists decision-makers by providing a range of possible outcomes and the probabilities associated with those outcomes. It allows for the assessment of risk and uncertainty in forecasting and decision-making processes. By simulating a model multiple times, each with randomly selected variables based on specified probability distributions, it generates a comprehensive view of potential performance scenarios.

In contrast, cost-volume analysis focuses on the relationship between costs, production volume, and profitability, typically using fixed and variable cost structures rather than random variables. Optimization modeling seeks to find the best solution from a set of feasible options under given constraints but does not inherently incorporate randomness as in Monte Carlo Simulation. Crossover point analysis evaluates the point at which two different cost structures converge, providing insights into profitability scenarios without the probabilistic flair characteristic of Monte Carlo methods.