A new associate at a corporation is tasked with ranking various projects for the CEO's approval. The associate decides to rank the projects based on the IRR, NPV, and PI, as indicated in the following table: What is the correct ranking of the projects from most valuable to least valuable?
Project A is the most valuable, followed by Project D, Project C, and Project B.
The ranking of projects based on their financial metrics—IRR, NPV, and PI—indicates that Project A provides the highest overall value, making it the top choice for CEO approval. Following closely are Project D, Project C, and lastly Project B, as determined by their respective performance in these metrics.
This ranking incorrectly places Project C ahead of Project A, despite Project A having superior financial metrics. The metrics used for evaluation show that Project A has a higher NPV and PI than Project C, making this choice inconsistent with the data provided.
This choice ranks Project B as the most valuable project, which contradicts the financial metrics indicating that it is the least valuable among the options. Project A, being the most valuable, should not be placed last, thus making this ranking incorrect.
Here, Project D is placed first, which is incorrect as Project A has a greater NPV and IRR. Additionally, Project B is ranked above Project A, which is not supported by the financial analysis. This ranking fails to reflect the true value of the projects.
This ranking accurately reflects the values derived from the IRR, NPV, and PI metrics. Project A is confirmed as the most valuable, followed by Project D, while Projects C and B are ranked appropriately below based on their lower performance metrics.
The financial metrics of IRR, NPV, and PI serve as essential tools for project evaluation in corporate settings. By accurately ranking the projects from most to least valuable, the associate ensures that the CEO can make informed decisions. Project A leads the ranking, followed by Project D, with Projects C and B trailing, showcasing the importance of data-driven decision-making in project approval processes.
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