It is important to note that capital budgeting depends on the forecasted data’s validity and hence is not an objective evaluation process, as many think. Data forecasting involves subjective assumptions, including capital structure assumptions, cash flow growth rates, capital structure assumptions, and the ultimate growth rate (Michelon et al., 2020). These assumptions collectively define the terminal value of the capital budgets, impacting the accuracy of capital budgeting systems, although not significantly (Michelon et al., 2020). When businesses fail to understand that capital budgeting is subjective, they make poor decisions that can negatively affect their long-term investments and goals. For instance, there is a risk of taking action or activity expecting profit and experiencing loss. However, due to their soundness, capital budgeting tools remain important in businesses to determine the viability of investments and projects.
There are several alternatives to capital budgeting that can help avoid the risks associated with capital budgeting. The first one is the computation of the payback period, which calculates the time it takes a company to recover its original investment. An investment with a shorter payment period is always the best option. However, the method has some weaknesses in that it does not consider the future profitability of the capital asset and the time value of money and assumes the same cash flow over the life of the asset. Another reliable approach is the internal return rate (IRR) approach, in which the net present value is computed using a discounted rate. When the net present value is zero, IRR occurs, and the investment with the highest IRR is chosen. While there are other methods like profitability index and net present value computations, I think the two discussed here are the most reliable. They can help make wise decisions, avoiding loss on large projects that require extensive investment.
References
Michelon, P. D. S., Lunkes, R. J., & Bornia, A. C. (2020). Capital budgeting: a systematic review of the literature. Production, 30. Web.