{"id":16262,"date":"2025-09-25T14:57:45","date_gmt":"2025-09-25T14:57:45","guid":{"rendered":"https:\/\/www.simtrade.fr\/blog_simtrade\/?p=16262"},"modified":"2026-01-20T21:51:09","modified_gmt":"2026-01-20T21:51:09","slug":"forecasting-airline-route-profitability-with-monte-carlo-simulation","status":"publish","type":"post","link":"https:\/\/www.simtrade.fr\/blog_simtrade\/forecasting-airline-route-profitability-with-monte-carlo-simulation\/","title":{"rendered":"Forecasting Airline Route Profitability with Monte Carlo Simulation"},"content":{"rendered":"\n\n\"Samuel\n<\/a>\n\n

In this article, Samuel BRAL<\/a> (ESSEC Business School, Global BBA \u2013 Exchange at NUS, 2025) explains how he applied Monte Carlo simulations to support Emirates Airlines in evaluating the profitability of launching a new long-haul route under conditions of uncertainty.<\/p>\n\n\n

Context of the project<\/h2>\n\n

This project was part of the course \u201cDecision Analytics using Spreadsheets\u201d at the National University of Singapore (NUS). I was asked to provide a quantitative recommendation to Emirates Airlines on selecting a new international route from Dubai. The available destination options included Buenos Aires, Tokyo, Cape Town, and Cairo.<\/p>\n\n

Due to the complexity of airline operations and the uncertainty surrounding factors such as demand, ticket prices, no-show rates, and operating costs, a traditional static financial model would not be sufficient. Instead, I built a Monte Carlo simulation model to capture the dynamic range of possible outcomes and assess the risk-return profile of each destination.<\/p>\n\n\n

What is a Monte Carlo simulation?<\/h2>\n\n

A Monte Carlo simulation is a mathematical technique used to estimate the probability distribution of outcomes when there is uncertainty in the input variables. By running thousands of simulations using random values generated from defined probability distributions, the method provides insights into the range, likelihood, and volatility of potential results.<\/p>\n\n

This approach is commonly used in financial modeling, risk analysis, and engineering. For example, investment banks use Monte Carlo models to simulate portfolio returns and Value at Risk (VaR), while oil and gas companies apply them to forecast drilling success and production volumes.<\/p>\n\n\n

Simulation approach and methodology<\/h2>\n\n

I built a simulation model in Excel that executed 2,000 trials per route. Each trial simulated a potential outcome based on randomly generated values for key variables. The profit was calculated using the following formula:<\/p>\n\n

Profit = (Tickets Sold \u00d7 Ticket Price) \u2013 Operating Costs \u2013 Compensation Costs<\/code><\/p>\n\n

Here is how each component was modeled:<\/p>\n