Project Info

Group 8: Sarah Long, Toan Nguyen, Shriya Pawar, Sujay Subramanyam

Class: BUS 125 – Simulation For Business

Professor: Long Gao

Date: August 31, 2025

University of California, Riverside

A. Gary Anderson School of Business Management

Table of Contents

1. Introduction
2. Commence
   • 2.1 Storyline
   • 2.2 Objectives
3. Construction & Simulation
   • 3.1 Aviation Simulation Revenue Model
   • 3.2 Formulas
4. Conclusion & Analysis
   • 4.1 Analysis
   • 4.2 Conclusion
5. Limitation & Solution
6. References

Introduction

Tourism between Japan and the United States has grown in recent years, with international travel playing a major role. Full-service airlines like All Nippon Airways (ANA) take part in this growth by not only transporting passengers but also offering a full in-flight experience. ANA, based in Minato, Tokyo, is known for its attention to detail, providing meals, snacks, entertainment, and other amenities that build customer loyalty and set it apart from competitors.

One of ANA’s most important routes is the Airbus A380-800 flight from Los Angeles (LAX) to Narita (NRT). This long-haul trip carries hundreds of passengers across different seating classes and requires careful financial planning. Ticket sales bring in most of the revenue, but profitability also depends on costs like catering, staffing, fuel, and airport fees. Because these flights are expensive to run, even small changes in demand or costs can affect profit.

This project focuses on these challenges by creating a simulation model for ANA’s Los Angeles to Narita route. The model provides a simple way to analyze how ticket pricing and operating costs influence profit under different scenarios. By testing realistic, data-based outcomes, it helps show how ANA can make better pricing and budgeting decisions, especially during off-season periods when demand is harder to predict.

Commence

Storyline

Our project begins with the story of a single long-haul flight: ANA’s Airbus A380-800 from Los Angeles (LAX) to Narita (NRT). The plane has 439 seats, with 56 in Business Class and 383 in Economy. Each passenger contributes to revenue through ticket purchases, while also generating costs associated with in-flight services such as meals, beverages, and entertainment.

For ANA, the economics of this flight are complex. While the plane is able to carry many passengers, profit depends on how many tickets are sold and how costs are managed. Expenses such as catering, crew salaries, fuel, and airport fees stay high whether the flight is full or not. This means that a flight selling close to full capacity can create strong returns, while a flight with weaker demand may struggle to cover fixed costs.

To examine this thoroughly, we built a simple simulation model in Excel. It includes ticket prices, the number of tickets sold during the offseason of October, variable costs such as catering per passenger, and fixed costs like base fuel and staffing. The model does not include extras such as duty-free sales or upgrades, but it covers the core financial factors. By running different scenarios, we can see how small changes in demand or costs affect overall profit.

Objective

The main goal of this project is to simulate expected profit for ANA’s Los Angeles to Narita Airbus A380-800 flight using a scenario-based approach. The model combines ticket revenue, in-flight service costs, and both fixed and variable expenses. By running different simulations, we can test how ticket pricing in Business and Economy Class affects outcomes, and how changes in costs influence net profit. The purpose of our model is to provide ANA with a clear and simple tool for understanding how pricing and cost decisions impact profitability, especially during off-season flights when demand is more unpredictable.

Construction & Simulation

Aviation Model

Table 1: All Nippon Airway seat capacities, costs, and pricing

Airline Revenue Model

Fixed Costs

Variable Fuel Costs

Seating Capacity

Inflight Amenities Cost Based on Class

Ticket Price and Demand Assumptions

Ticket demand is modeled using a normal distribution, with mean and standard deviation values based on ticket price. For example, at a Business Class price of $5,500, the expected demand is 42 passengers with a standard deviation of 3.36. At an Economy Class price of $700, the expected demand is 247 passengers with a standard deviation of 40.4. Each ticket price option affects both demand and revenue outcomes in the simulation.

In reality, in the world of an airline company, there obviously would be a variable regression function that utilizes standard error instead of standard deviation, but for the sake of simplicity, we choose to stick to these, modeling as if we were to take a few points out of a regression.

Variable costs per passenger include fuel and in-flight amenities (food, bedding, etc.). Business Class catering costs $3,790 per person, and Economy Class costs $250. The reason we show such variation is that passengers in economy have a simple pillow and blanket, while business class passengers get a mattress, pajamas, and a meal with champagne. Fuel use is 288.288 units per passenger, because the added weight increases the fuel needed for the flight.

Revenue is calculated by multiplying ticket sales by the ticket price. Variable cost is calculated by multiplying the number of passengers by the combined fuel and catering cost. Contribution margin is the difference between revenue and variable cost, and therefore, something we must maximize for the completion of this problem.

Fixed costs include base fuel ($18,720), crew salaries ($340,000), airport fees ($13,430), and maintenance ($88,000). The final profit per flight is calculated as total contribution minus fixed costs. All simulations are performed using Excel with formulas such as NORM.INV, IF, and RAND to reflect uncertainty in demand and passenger behavior. The values we chose were random and speculative, though in a real-world scenario, the airline would have lots of data to model this information.

Formulas

Airlines operate in a highly competitive and uncertain environment, where demand fluctuates depending on pricing, seasonality, competition, and global factors. To make informed decisions, managers need tools that can simulate different scenarios of sales, revenues, costs, and profitability. Excel provides an effective way to model these factors by combining statistical functions with cost and revenue calculations.

This report explains how key formulas can be used to estimate ticket sales, compute revenue, capture variable costs, and evaluate contribution margins, using the example of economy class tickets priced at $770. We also ran the same simulation with economy class tickets priced at $700 and $630, as well as business class tickets priced at $4,500, $5,500, and $5,850.

Table 2: Variables of the Aviation Simulation

Estimating Ticket Sales

The number of tickets sold is one of the most uncertain variables in airline operations. To capture demand uncertainty, the following formula is applied:

text=INT(IF(NORM.INV(RAND(),C39,D39)>C19,C19,NORM.INV(RAND(),C39,D39)))

In this formula, RAND() generates a random probability between 0 and 1. The NORM.INV function transforms this probability into a simulated demand value based on a normal distribution, where C39 represents the mean demand and D39 represents the standard deviation of demand at a given ticket price.

Since airlines cannot sell more tickets than available seats, the formula compares simulated demand against capacity (C19) and uses the smaller value. Finally, INT ensures the result is a whole number. This approach allows the model to run multiple simulations, each producing a different sales outcome. Over many iterations, managers can evaluate not just expected sales, but also the risk of underutilized seats or over-demanded flights.

Revenue from Ticket Sales

Once ticket sales are determined, revenue is straightforward to compute:

text=G19*L19

Here, G19 is the number of tickets sold, and L19 is the price per ticket. Multiplying the two values gives the total sales revenue. For example, if 350 tickets are sold at $770 each, total revenue is $269,500. This formula provides the foundation for assessing financial performance, as it reflects the inflow of funds from passengers.

Variable Costs per Flight

While revenue reflects inflows, airlines must account for costs tied directly to passengers. The variable cost formula is:

text=G19*B13+G19*C27

This expression combines two major categories of per-passenger expenses. The first component, G19*B13, estimates the total fuel cost by multiplying sales units by the fuel cost per passenger. The second component, G19*C27, estimates the cost of in-flight amenities, such as meals, beverages, and comfort items. Together, these costs scale directly with passenger numbers, making them variable costs.

Contribution Margin in Dollars

After subtracting variable costs from revenue, airlines can measure contribution in dollars:

text=H19-I19

In this formula, H19 is sales revenue, and I19 is variable costs. The result represents how much money is left over to cover fixed costs, such as aircraft leasing, crew salaries, and airport fees. Contribution is a key measure because it bridges the gap between revenue and profit, highlighting the portion of revenue that can support long-term financial sustainability.

Contribution as a Proportion

Managers often need to understand not just the dollar contribution, but how it relates to fixed costs. The following formula provides that perspective:

text=J19/SUM(C7:C10)

Here, J19 is the contribution in dollars, while C7:C10 represents the sum of fixed costs. Dividing the contribution by fixed costs indicates what percentage of fixed expenses are covered by the revenue remaining after variable costs. A ratio above 100% means fixed costs are fully covered, and the airline is profitable. A ratio below 100% signals a shortfall that must be addressed through pricing strategies, cost reductions, or additional demand stimulation.

Ticket Price Reference

Finally, the model references ticket price directly through:

text=B39

With B39 storing the economy class ticket price, this setup allows managers to adjust prices easily. Changing the value in B39 automatically flows through to revenue, variable cost scaling, and contribution calculations. This flexibility supports sensitivity analysis, where different price points can be tested to identify the most profitable balance between demand and pricing.

Taken together, these formulas provide a comprehensive framework for analyzing airline profitability. By combining random demand simulation with straightforward revenue and cost calculations, managers can test scenarios that reflect real-world uncertainty. The model can be expanded to include multiple cabin classes, fixed costs, and profit calculations. It also enables “what-if” analysis, such as testing how changes in ticket price or amenities costs affect profitability.

For decision-makers, this framework supports strategic choices around pricing, capacity allocation, and cost management. It highlights the trade-off between filling seats at a lower price versus maximizing revenue per passenger. Most importantly, it provides a structured, quantitative way to anticipate outcomes before making costly operational decisions.

Conclusion & Analysis

Analysis

Our simulation results highlight how sensitive ANA’s Los Angeles to Narita Airbus A380-800 route is to pricing decisions, especially during the off-season. The model shows that even small adjustments in ticket price can have large effects on revenue, contribution margins, and overall profitability. While operating costs such as crew salaries, maintenance, and airport fees remain fixed, the financial outcome depends heavily on selecting the right ticket prices to maximize contribution.

Economy Results

In Economy Class, the results suggest that a ticket price of $700 produces the strongest contribution. At this level, total contribution is higher than both the lower option of $630 and the higher option of $770. This indicates that moderate pricing is the most effective strategy for capturing revenue while maintaining stronger margins.

Business Results

Business Class shows a similar outcome. The analysis reveals that $5,500 is the optimal price, generating the highest contribution margin and total revenue among the scenarios tested. A higher price of $5,850 lowers the contribution substantially, while a lower price of $4,500 increases revenue less efficiently. Careful pricing in Business Class, therefore, plays a critical role in maximizing the profitability of the route.

Conclusion

When both seating classes are priced optimally at $700 for Economy and $5,500 for Business, the airline achieves its highest simulated profit of $191,577. This outcome represents the best balance between revenue per seat and overall contribution to cover the high fixed costs associated with a long-haul flight.

In contrast, other pricing strategies, like raising Business Class fares to $5,850 or setting Economy fares at $770, result in big declines in profitability. The worst-case combinations push the route into heavy losses, with deficits going over $250,000 per flight, which is a big financial burden for the airline.

These results show the thin margins airlines often face when operating long-haul international routes. Because fixed expenses like crew salaries, airport fees, and maintenance remain constant, profitability relies on selecting pricing structures that maximize contribution without increasing risk. The findings show that even small mistakes in pricing strategy can lead to major swings in financial performance, turning a profitable flight into a loss.

Ultimately, this shows the importance of data-driven pricing decisions, especially during the off-season months when profitability is harder to sustain. The application of a simulation model provides ANA with a structured way to test pricing outcomes in advance and better understand how different scenarios affect the bottom line. By using this approach, ANA can make better decisions not only for this specific Los Angeles to Narita route but also for other long-haul services across the network. This betters the airline’s ability to minimize financial risk, capture revenue more effectively.

Limitation & Solution

In our model, passenger demand and costs are simplified through basic assumptions and randomized distributions, and our model in no way is qualified to reflect real-world airline dynamics. By using mean and standard deviation assumptions for three ticket classes instead of an econometric regression model, we ended up underestimating the complexity of real-world demand, which is shaped by competitor actions, marketing campaigns, and macroeconomic events such as fuel price fluctuations.

To better reflect reality, future work could incorporate historical booking data, regression-based forecasting, and other techniques to better understand how demand changes with price. Our prices and demand values were 3 values that we came up with to just make a bare prototype of a model that could help airlines make better decisions, and therefore, we have not allocated for the many different price points that will need to be looked at in the case of a real simulation.

Our current model assumes ticket prices remain constant, but in reality, airlines use dynamic pricing strategies, adjusting fares based on a multitude of different variables. Excluding these mechanisms would leave the simulation to overstate or understate potential profits, as well as the volatility of profits. On top of this, customer behavior was simplified to price-driven decisions, and we never accounted for the amount lost on frequent flyers who would be getting free upgrades. These limitations, in general, make it tough for us to be able to give an accurate indication of the actual impact of these decisions.

On top of this, the route we simulated in some ways is not fully accurate, as ANA currently does not use its A380 for its Los Angeles flight, so in this case, there is a lot of what-if that goes into our project. Being able to get accurate data on operating costs is something we hoped we could get with 100 percent accuracy, but generally was also generally estimated using a conversation with Perplexity AI, an AI model that is built to search the internet and make approximations.

Overall, our model is a prototype of a simulation that could be implemented by airlines on multiple routes, allowing them to make better decisions on pricing their tickets by understanding price and demand. Even if airlines today use pricing decisions made by algorithms, the data of a simulation like this would be crucial in aiding the models.

Appendix

• Table 1, All Nippon Airway seat capacities, costs, and pricing, Aviation Simulation Revenue Model, Group 8
• Table 2, Variables of the Aviation Simulation, Aviation Simulation Revenue Model, Group 8
• Traveloka. (2025). Flight prices from Los Angeles (LAX) to Narita (NRT), October 1–31, 2025. Traveloka. http://www.traveloka.com
• Arba, A. (2025, May 23). Load factor of passenger operations of All Nippon Airways Co., Ltd. from the fiscal year 2015 to 2024, by type of flight. Statista. https://www.statista.com
• Perplexity. (2025). Perplexity.ai (AI chatbot) [Large language model]. https://www.perplexity.ai/
• Perplexity AI. (2025, August 31). What is the approximate change in gallons needed for an 11-hour flight on an… [AI-generated conversation]. Perplexity AI. Retrieved August 31, 2025, from https://www.perplexity.ai/search/what-is-the-approximate-change-mrcpfhRkRhGuv7Y0X5nZzg