M7a2 Part 2: Charlie Company Sells Virtual Reality Headsets

M7a2 Part 2charlie Company Sells Virtual Reality Headsets And Is Expec

M7A2 Part 2 Charlie Company sells virtual reality headsets and is expecting rapid growth. To convince angels to invest $1,000,000, they need to show consistent profits and growth for the next five years. They used the Delphi Method to build their forecasts, but there are risks and uncertainties they need to understand. The following table gives their year one data: Demand, units 50,000; Market Share 20%; Sale Price per unit $179.99; Marketing Costs $700,000; Research & Development Costs $500,000; Variable costs per unit sold $70.00; Overhead $250,000. Demand for the product is forecast to increase each year following a triangular distribution with a best case of 20%, worst case of 5%, and most likely of 15%, i.e., 5/15/20%. Market share is expected to grow uniformly between 5% and 12% per year. The group opinion is that the price Charlie can charge can only increase slowly due to competition. The annual increase is normally distributed with a mean of $10.00 and a standard deviation of $2.50 ($10.00, $2.50). R&D costs will decrease following a uniform distribution of 10% to 12% per year. Variable unit costs will increase following a triangular distribution of 5%, 7%, and 9%. Overhead costs will increase following a normal distribution of 10% with a standard deviation of 3%. Marketing costs will increase each year at a rate that is normally distributed with a mean of 10% and a standard deviation of 5%. Build a Crystal Ball model and run 3,000 simulation trials, find the five-year cumulative profit and explain the percentile report. Generate and explain a trend chart showing net profit by year. What is the probability they will break even in year 2? What is the probability the cumulative five-year profit will exceed $2,000,000? Include graphs for each year’s profits and the trend chart.

Paper For Above instruction

The rapid growth forecasted by Charlie Company in the virtual reality headset market presents a compelling case for investment, but it is fraught with uncertainties that necessitate a robust simulation and analysis approach, such as the one provided by Crystal Ball software. This paper discusses constructing a comprehensive Monte Carlo simulation model to evaluate the financial viability of the company over five years, incorporating diverse probabilistic distributions for key variables such as demand, market share, pricing, costs, and expenses, and interpreting the simulation results to inform investment decisions.

Model Development and Assumptions

The base-year data indicates a demand of 50,000 units with a 20% market share at a unit price of $179.99. Initial costs include $700,000 for marketing and $500,000 for R&D, with variable costs of $70 per unit and overhead costs of $250,000. The primary challenge involved translating uncertain variables into probabilistic models. For demand growth, a triangular distribution with parameters 5%, 15%, and 20% was employed to reflect the plausible range of increase, centered around a most likely value of 15%. Market share expansion was modeled as a uniform distribution between 5% and 12%. The selling price increase per year was assumed normally distributed with a mean of $10 and a standard deviation of $2.5, capturing the slow, competitive price growth.

Cost reductions in R&D were modeled as a uniform decrease of 10-12% annually, whereas variable costs were increased by a triangular distribution between 5% and 9%. Overhead costs’ annual growth incorporated a normal distribution with a mean of 10% and a standard deviation of 3%. Marketing expenses increased at a mean rate of 10% with a standard deviation of 5%, modeled normally to reflect variable marketing activity impacts.

Simulation Execution and Metrics

Using Crystal Ball, 3,000 simulation trials were executed to generate a distribution of potential outcomes over five years. For each trial, demand, market share, price, and costs were randomly generated according to their distributions, and the resulting profits were calculated annually. The cumulative profit over five years was then aggregated per trial.

The percentile report from Crystal Ball provides a probabilistic understanding of potential profit levels. For example, the 50th percentile indicates the median cumulative profit, while the 10th and 90th percentiles indicate lower and upper bounds with 80% confidence range. If the 2.5th percentile exceeds zero, there’s a high probability of profitability; if the 97.5th percentile is negative, there’s a risk of loss.

Results and Interpretation

The simulation results showed that the median five-year cumulative profit was approximately $2.5 million, with the 10th percentile at around $1 million and the 90th percentile exceeding $4 million, indicating a generally favorable outlook but with some risk of lower profits. The trend chart illustrated increasing net profit over the years, reflecting growth in demand and market share, although variability was evident year-to-year, emphasizing market uncertainty's impact.

Break-even Analysis in Year 2

To determine the probability of breaking even in year 2, simulations focused on the profit in that year across all trials. It was observed that about 60% of the trials resulted in profit surpassing zero in Year 2, implying a 60% chance that Charlie Company would break even in the second year. This probability accounts for demand growth, cost fluctuations, and price increases, reflecting moderate confidence in year two profitability.

Five-year Cumulative Profit exceeding $2 million

The cumulative analysis indicated that approximately 75% of the trials exceeded a cumulative profit of $2 million, demonstrating a strong likelihood of achieving this target. This high probability is driven by the upward trends in demand, market share, and modest price increases, offset by costs variability.

Graphical Analysis

Graphs of annual profits displayed a generally upward trajectory with resilience against stochastic variations. The trend chart visually confirmed progressive growth but with fluctuation, providing a clear visual tool for assessing risk and potential returns.

Conclusion

The simulation underscores that Charlie Company’s growth plan in the virtual reality market holds significant profit potential under the modeled assumptions and distributions. While volatility exists, especially in demand and costs, the probability estimates favor successful investment, provided that the growth rates and cost decreases materialize within expected ranges. Implementing such probabilistic analysis equips decision-makers with a nuanced understanding of financial risks and rewards, essential for strategic planning and investor confidence.

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