The cost volume profit analysis, commonly referred to as CVP, is a planning process that management uses to predict the future volume of activity, costs incurred, sales made, and profits received. In other words, it’s a mathematical equation that computes how changes in costs and sales will affect income in future periods (Peavler, 2019). CVP analysis provides managers with the advantage of being able to answer specific questions needed in business analysis. Such as, what is the company's breakeven point? When a manager knows the breakeven point, he can adjust spending and increase production efforts to increase profitability. Because CVP analysis is based on statistical models, decisions can be broken down into probabilities that help with the decision-making process. It would be ideal if a business knew the exact number of customers that would enter a business, and what exactly they would be purchasing, as this would ensure the business would have the exact number of employees and products available; however, as we know this is not reality; everything has to be estimated based upon previous years and projections. The CVP analysis looks primarily at the effects of differing levels of activity on the financial results of a business. The reason for the focus on sales volume is because, in the short-run, sales price, and the cost of materials and labor, are usually known with a degree of accuracy. Sales volume, however, is not usually so predictable and therefore, in the short-run, profitability often hinges upon it (CVP, 2019). Cost-volume-profit analysis is invaluable in demonstrating the effect on an organization that changes in volume (in particular), costs and selling prices, have on profit. However, its use is limited because it is based on the following assumptions: Either a single product is being sold or, if there are multiple products, these are sold in a constant mix. We have considered this above in Figure 3 and seen that if the constance mix assumption changes, so does the break-even point (Henry, Robinson, & Hendrik, 2011).