Unit 4 Assignment Expected Payoff Table Outcomes Addressed

Unit 4 Assignment Expected Payoff Tableoutcomes Addressed In This Act

Scenario: You have been approached by a hotel owner who would like you to forecast expected payoffs for him. The hotel has 100 rooms, and they would like to maximize their revenue by selling as many as possible at full price, and selling the rest to travel brokers, such as Travelocity and Expedia, at a discounted price. The market rate for hotel rooms in their area is $279 per night. They feel that they can sell rooms for as little as $229 per night and still profit. Selling rooms for less than that would cause a financial loss. Every room left empty for a night reduces revenue, so they would rather discount some rooms than have them empty, but their preference is to sell as many rooms as they can at full price. Your task is to help the hotel owner find the best possible combination of full and discounted rooms in order to maximize hotel revenue. You will do this in Excel. Download the Expected Payoff Table.xlsx file from Course Documents.

Tables for Expected Payoff and Opportunity Loss have been prepared for your use in that file. Complete the following steps: Fill in the Pricing Assumptions Table with the correct values from the scenario above. Create formulas in the Expected Payoff Table to calculate the amount of revenue the hotel owner will expect to earn for each combination of full and discounted rooms. Ensure that you never calculate a revenue amount that would represent more than the sale of 100 hotel rooms, as he only has 100 rooms to sell each night. Use a color to highlight the cell in each column that represents the maximum amount of revenue for each combination of full price and discounted rooms.

Using the Likelihood percentages (row 8 in the spreadsheet), create formulas to calculate the Expected Payoff Calculations. Use a color to highlight the best expected payoff. Calculate the Price of Perfect Information. This is the difference between the revenue generated by selling all rooms at full price (there is only a 2% likelihood of that), and the best expected payoff calculated in step 3 above. Create formulas to complete the Opportunity Loss table. Opportunity loss is the difference between the best possible revenue and the actual revenue at each combination of full price and discounted rooms. Use a color to highlight the best (smallest) opportunity loss for each combination of full and discounted rooms. Using the Likelihood percentages (row 8 in the spreadsheet), create formulas to calculate the Expected Opportunity Loss calculations. Use a color to highlight the best (smallest) expected opportunity loss. In the conclusion area (cells R8 through U37 are merged for this), write an explanation of how your Expected Payoff Table will help the hotel owner.

Paper For Above instruction

The hotel industry operates within a complex environment where maximizing revenue involves strategic decisions about room pricing and allocation. The core objective for the hotel owner in this scenario is to balance between selling rooms at full price and leveraging discounted rates to ensure occupancy, while minimizing losses and optimizing revenue. The development of an Expected Payoff Table in Excel serves as a critical analytical tool that informs these strategic decisions by quantifying potential outcomes based on various occupancy and pricing scenarios.

The initial step involves accurately inputting the pricing assumptions into the designated table, including the full price ($279), discounted price ($229), and the minimum profitable price ($229). These assumptions establish the foundation for subsequent calculations. The next phase involves creating formulas within the Expected Payoff Table to compute potential revenues for each combination of full and discounted room sales. As the hotel has only 100 rooms, the formulas must cap sales at this maximum, ensuring realistic revenue projections.

For each scenario, the revenue is calculated by considering how many rooms are sold at full price and how many are discounted. Whenever the total demand exceeds 100 rooms, the actual number of rooms sold is limited to 100, with the remaining demand unfulfilled. By applying these formulas, the table generates expected revenues across different configurations of room sales.

Color coding is essential to visually identify the most lucrative strategies. Specifically, cells in each column representing the maximum expected revenue are highlighted, guiding the hotel owner toward the most profitable mix of discounted and full-price rooms. To assess the probabilistic nature of customer demand, likelihood percentages are incorporated into the calculations, resulting in Expected Payoff figures for each scenario. These figures reflect the weighted average revenue, considering the probabilities of different demand levels occurring.

The calculation of the Price of Perfect Information (PPI) provides further insight by quantifying the value of knowing future demand scenarios with certainty. PPI is computed as the difference between revenue when all rooms are sold at full price (with a mere 2% likelihood) and the highest expected payoff derived from probabilistic analysis. This metric informs the potential benefits of demand forecasting accuracy.

Complementing the Expected Payoff Table, the Opportunity Loss table measures the potential revenue foregone when actual outcomes fall short of the best possible revenue scenario. By calculating the opportunity loss for each demand and pricing combination, and identifying the smallest loss options, the hotel owner gains perspective on the risks associated with different strategies. The expected opportunity loss, then, is a probabilistic average of these potential foregone revenues, which can highlight strategies that minimize risk.

The conclusion area synthesizes these analyses by explaining how the expected payoff data guides decision-making. The hotel owner can leverage this financial modeling to determine optimal room pricing strategies, balancing occupancy goals with revenue maximization. They can also understand the value of demand information, and how it influences profitability under uncertainty.

While these analytical tools provide valuable insights, they also present ethical considerations. One positive ethical aspect involves transparency; the hotel owner can use data-driven strategies to make fair pricing decisions, avoiding arbitrary or discriminatory practices. Conversely, a negative ethical issue may arise if the owner manipulates data to justify unfair pricing policies, or if they withhold full information from customers, thus misleading them about room availability and rates.

Overall, this Expected Payoff Table plays a vital role in strategic hotel management, enabling informed decision-making based on comprehensive risk assessment and probabilistic analysis. Such tools foster ethical, transparent, and economically sound practices that align with broader industry standards and customer expectations.

References

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