Scenario
As a business analyst, you are tasked with using Monte Carlo simulations to predict potential outcomes in various decision-making
scenarios. The first part of your task involves simulating random events (dice rolls) to understand the principles of randomness and
probability distribution. In the second part, you will use simulation techniques to predict the future performance of a long-term capital
investment project and calculate its Net Present Value (NPV) under various demand scenarios.
Finally, you will explore real-world applications of Monte Carlo simulations in the aviation industry, providing an example of how these
techniques can enhance decision-making in practice.
You will be using Microsoft Excel and submitting your spreadsheet as a graded deliverable. Review the following directions for this
assignment.
Please read through all sections before proceeding to the next page and refer back whenever necessary.
Expand AllA Collapse Al
• Part 1: Simulating Random Events
– Part 2: Long-Term Capital Investment Simulation
In this section, use the Budget worksheet model from the Chapter 12 Excel file located within the Harnessine Excels Advanced Features
for Business Optimization (PDF/lsaa Gottlich B reading to simulate potential annual demand and calculate the Net Present Value
(NPV) for a capital investment project.
Base Scenario Setup:
• Simulate annual demand for three ycars using the function’-RANDBETWEEN(400000.800000).
• For each scenario, calculate the Net Present Value (NPV) using the provided budget model in Excel.
• Explore how different demand levels impact the profitability of the investment over time.
Please proceed to the Part 3: Real-World Applications section.
Start by simulating random events to explore how probabilities are distributed over time and how these simulations compare to
theoretical expectations.
Dice Rolling Simulation:
• Simulate the roll of a single six-sided die 100 times using the Excel function ‘-RANDBETWEEN(1.6).
• Calculate the relative frequency of each outcome (1 through 6) and compare it with the theoretical probability for each number.
• Visualize your results with a bar chart comparing the simulated and theoretical frequencies.
Pair of Dice Simulation:
• Simulate rolling two six-sided dice and summing their values for 100 trials.
• Create a frequency distribution table for the possible outcomes (2 through 12).
• Plot a histogram comparing your simulated distribution to the theoretical probabilities for each sum.
Please proceed to the Part 2: Long-Term Capital Investment Simulation section.
• Part 3: Real-World Applications
In this final section, explore how Monte Carlo simulations are used in real-world decision-making, particularly in the aviation industry.
Research and Example:
• Research how Monte Carlo simulations are applied to model uncertainty and risk in the aviation industry.
• Provide a real-world example and explain why the simulation is valuable in that instance (e g. fuel cost forecasting, flight delay
predictions).
Reflection:
• Write a brief reflection (200-300 words) on how Monte Carlo simulations can enhance decision-making in the aviation industry.
• Ike wour meearch and pyamole to Flustrate the valun of cimulstions in imorvine onerational effrieney and rick mansorment
As a business analyst, you are tasked with using Monte Carlo simulations to predict potential outcomes in various decision-making scenarios. The first part of your task involves simulating random events (dice rolls)