• Marginal analysis is the comparison of the actual benefits gained vs the extra costs incurred from any given business activity.
• Companies and investors can use marginal analysis to help inform profit maximization decisions.
• Performing a marginal analysis can also reveal what a company needs to do to maintain production costs in the face of anticipated changes.
A useful decision-making tool, marginal analysis affords the opportunity to compare benefits derived from an investment or other business activity to the extra cost of the activity. In this way, the potential profits to be gained can be measured by weighing the costs against the benefits. Generally employed by companies seeking to maximize efficiency and improve decision-making, performing a marginal analysis is a necessary aspect of planning any investment or expansion of business operations.
Underlying most business decisions is the question of whether the costs incurred will justify the anticipated benefit. Marginal analysis does so and considers the causal relationship between variables.
With marginal analysis, it is possible to examine the potential ramifications to the company as a whole of certain conditional changes. The resulting data can inform pricing as well as production decisions. Performing a marginal analysis can also reveal what a company needs to do to maintain production costs in the face of anticipated changes.
For example, this tool can help a company determine whether it is worthwhile to ramp up production. In other words, will it make sense to continue the investment until the marginal revenue derived from the additional production equals the marginal costs of production?
For investors considering two or more investments, performing a marginal analysis can help determine the costs and benefits of each to ascertain which of them is likely to offer the greatest income potential.
The two primary uses of marginal analysis are the observation of changes and determining opportunity costs.
As an observational tool, marginal analysis can be used to survey changes resulting from the application of specific variables in controlled experiments, such as evaluating the consequences of a set percentage of production increase on costs and revenues. Should there be an observed reduction in marginal cost, or a sufficient increase in revenue to realize more profit, the production increase could be deemed worthwhile.
Managers and investors often find themselves in situations in which they must decide between two or more opportunity cost related choices. Say for example, a company has a budget to add one additional employment position and the choices are between creating a marketing position or an administrative one.
Should the results of a marginal analysis reveal there is more benefit to be derived from investing in a marketing person than an administrator, the logical decision would be to create the marketing position. The cost of doing the opposite could be a lost opportunity to expand the business.
Consider a scenario involving a baseball cap manufacturer. The materials to produce each cap cost $1.50. The facility in which they are produced has fixed costs of $200 dollars monthly. That means that the fixed cost of producing 100 hats per month is $2 each. Therefore, the total monthly cost per hat, including the materials, would be $3.50 ($3.50 = $1.50 + ($200/100).
Ramping volume up to 200 ball caps monthly would lower the fixed cost to $1 per unit. This, in turn, would drop total per unit cost to $2.50 ($2.50 = $1.50 + ($200/200)). Thus, increasing production volume in this instance would reduce marginal costs.
In so doing, the company will have captured an economy of scale, in that the marginal cost will decline with the production increase. However, it is important to remember that the key to additional profitability lies in whether or not market demand is sufficient to absorb the 100 additional units each month.
It is important to recognize the fact that marginal data, by and large, is hypothetical. In other words, a multitude of “ifs” must be aligned in order for the results of the analysis to manifest. Therefore, the marginal analyst doesn’t get a true picture of marginal cost and output. There are only estimates, assuming all things will be equal. For this reason, decisions based solely upon average data of this nature can sometimes fall short.
As an example, in observing an uptick in demand, a bakery owner may consider the purchase of a second oven to increase production. Projecting growth based upon the trajectory of the uptick, the baker may decide the potential for additional revenue will outweigh the cost of the new oven and make the purchase. The decision will likely prove to be a sound one if the uptick in business continues unabated.
However, if the uptick was due to an unaccounted for anomaly, say a one-time large-scale local celebratory activity, the purchase decision will have been based upon an erroneous assumption in the marginal analysis. In other words, marginal analysts must consider all factors that may be pertinent to the analysis to ensure that the result is as accurate as possible.
In our baseball cap manufacturing example above, it was determined that the marginal cost of producing 200 caps is lower than producing 100. However, as we observed, a lower marginal cost does not guarantee a higher marginal benefit, because we did not consider whether the marketplace could absorb 100 more ball caps each month.
Therefore, we cannot accurately assess the marginal benefit until we have done so.
Yes, producing the additional caps will lower costs, but the benefit will only be derived if all 200 of the caps actually sell. Thus, it is necessary to analyze market demand in addition to production costs. Moreover, the costs of shipping 200 units will be higher than those associated with shipping 100. All such factors must be considered to ensure an accurate assessment.
Consider a situation in which an investor finds two seemingly worthy investments, but only has the resources to take a worthwhile position in one of them. Applying marginal analysis, the investor can compare the costs and benefits of each investment to the other one to figure out which offers the highest income potential.
This can be accomplished by reviewing the price-to-book (P/B) ratio, the price-to-earnings (P/E) ratio, the price-to-earnings (P/E) growth ratio, earnings per share (EPS), and dividend yield of each investment. Having done so, weighing those factors against what it would cost to take a position in each stock will help the investor determine whether a stock is trading at a fair price, or whether it is over- or undervalued.
The offering with the best price, when measured against the potential benefits, would be the logical one in which to buy. Assuming, of course, that it also fit into the investor’s overall strategy and portfolio diversification requirements.
Toward the subject of diversification, alternative investments can be potentially useful tools for portfolio diversification. Traditional portfolio asset allocation envisages a 60% public stock and 40% fixed income allocation. However, a more balanced 60/20/20 or 50/30/20 split, incorporating alternative assets, may make a portfolio less sensitive to public market short-term swings.
Real estate, private equity, venture capital, digital assets, and collectibles are among asset classes deemed “alternative investments.” Broadly speaking, such investments tend to be less connected to public equity, and thus offer potential for diversification. Of course, like traditional investments, it is important to remember alternatives also entail a degree of risk. In some cases, this risk can be greater than that of traditional investments.
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Marginal analysis is typically employed as a tool for determining the potential to extract the greatest amount of value from available resources. It can be applied to manufacturing as well as investing for the purposes of maximizing profits through comparisons of the costs and benefits of any investment activity.
All investments involve risk, including the possible loss of capital. There can be no assurance that any product or strategy described herein will achieve any targets or that there will be any return of capital. Past performance is not a guarantee or reliable indicator of future results. Current performance may be lower or higher than the past performance data quoted. Any historical returns, expected or target returns are hypothetical in nature and may not reflect actual future performance. All performance and/or targets contained herein are subject to revision by Yieldstreet and are provided solely as a guide to current expectations.
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