A Scenario is a set of values that Excel saves and can substitute automatically in cells on a worksheet. You can create and save different groups of values on a worksheet and then switch to any of these new scenarios to view different results. For example, suppose you have two budget scenarios: a worst case and a best case. You can use the Scenario Manager to create both scenarios on the same worksheet, and then switch between them. For each scenario, you specify the cells that change and the values to use for that scenario. When you switch between scenarios, the result cell changes to reflect the different changing cell values.  1. Changing cells 2. Result cell 1. Changing cells 2. Result cell If several people have specific information in separate workbooks that you want to use in scenarios, you can collect those workbooks and merge their scenarios. After you have created or gathered all the scenarios that you need, you can create a Scenario Summary Report that incorporates information from those scenarios. A scenario report displays all the scenario information in one table on a new worksheet.
Note: Scenario reports are not automatically recalculated. If you change the values of a scenario, those changes will not show up in an existing summary report. Instead, you must create a new summary report.
Sensitivity analysis determines how different values of an independent variable affect a particular dependent variable under a given set of assumptions. In other words, sensitivity analyses study how various sources of uncertainty in a mathematical model contribute to the model's overall uncertainty. This technique is used within specific boundaries that depend on one or more input variables. Sensitivity analysis is used in the business world and in the field of economics. It is commonly used by financial analysts and economists and is also known as a what-if analysis.
Sensitivity analysis is a financial model that determines how target variables are affected based on changes in other variables known as input variables. This model is also referred to as what-if or simulation analysis. It is a way to predict the outcome of a decision given a certain range of variables. By creating a given set of variables, an analyst can determine how changes in one variable affect the outcome. Both the target and input—or independent and dependent—variables are fully analyzed when sensitivity analysis is conducted. The person doing the analysis looks at how the variables move as well as how the target is affected by the input variable. Sensitivity analysis can be used to help make predictions about the share prices of public companies. Some of the variables that affect stock prices include company earnings, the number of shares outstanding, the debt-to-equity ratios (D/E), and the number of competitors in the industry. The analysis can be refined about future stock prices by making different assumptions or adding different variables. This model can also be used to determine the effect that changes in interest rates have on bond prices. In this case, the interest rates are the independent variable, while bond prices are the dependent variable.
Investors can also use sensitivity analysis to determine the effects different variables have on their investment returns. Sensitivity analysis allows for forecasting using historical, true data. By studying all the variables and the possible outcomes, important decisions can be made about businesses, the economy, and making investments. Assume Sue is a sales manager who wants to understand the impact of customer traffic on total sales. She determines that sales are a function of price and transaction volume. The price of a widget is $1,000, and Sue sold 100 last year for total sales of $100,000. Sue also determines that a 10% increase in customer traffic increases transaction volume by 5%. This allows her to build a financial model and sensitivity analysis around this equation based on what-if statements. It can tell her what happens to sales if customer traffic increases by 10%, 50%, or 100%. Based on 100 transactions today, a 10%, 50%, or 100% increase in customer traffic equates to an increase in transactions by 5%, 25%, or 50% respectively. The sensitivity analysis demonstrates that sales are highly sensitive to changes in customer traffic. In finance, a sensitivity analysis is created to understand the impact a range of variables has on a given outcome. It is important to note that a sensitivity analysis is not the same as a scenario analysis. As an example, assume an equity analyst wants to do a sensitivity analysis and a scenario analysis around the impact of earnings per share (EPS) on a company's relative valuation by using the price-to-earnings (P/E) multiple. The sensitivity analysis is based on the variables that affect valuation, which a financial model can depict using the variables' price and EPS. The sensitivity analysis isolates these variables and then records the range of possible outcomes. On the other hand, for a scenario analysis, the analyst determines a certain scenario such as a stock market crash or change in industry regulation. He then changes the variables within the model to align with that scenario. Put together, the analyst has a comprehensive picture. He now knows the full range of outcomes, given all extremes, and has an understanding of what the outcomes would be, given a specific set of variables defined by real-life scenarios. Conducting sensitivity analysis provides a number of benefits for decision-makers. First, it acts as an in-depth study of all the variables. Because it's more in-depth, the predictions may be far more reliable. Secondly, It allows decision-makers to identify where they can make improvements in the future. Finally, it allows for the ability to make sound decisions about companies, the economy, or their investments. But there are some disadvantages to using a model such as this. The outcomes are all based on assumptions because the variables are all based on historical data. This means it isn't exactly accurate, so there may be room for error when applying the analysis to future predictions.
Marginal analysis is an examination of the additional benefits of an activity compared to the additional costs incurred by that same activity. Companies use marginal analysis as a decision-making tool to help them maximize their potential profits. Marginal refers to the focus on the cost or benefit of the next unit or individual, for example, the cost to produce one more widget or the profit earned by adding one more worker.
Marginal analysis is also widely used in microeconomics when analyzing how a complex system is affected by marginal manipulation of its comprising variables. In this sense, marginal analysis focuses on examining the results of small changes as the effects cascade across the business as a whole. Marginal analysis is an examination of the associated costs and potential benefits of specific business activities or financial decisions. The goal is to determine if the costs associated with the change in activity will result in a benefit that is sufficient enough to offset them. Instead of focusing on business output as a whole, the impact on the cost of producing an individual unit is most often observed as a point of comparison. Marginal analysis can also help in the decision-making process when two potential investments exist, but there are only enough available funds for one. By analyzing the associated costs and estimated benefits, it can be determined if one option will result in higher profits than another. From a microeconomic standpoint, marginal analysis can also relate to observing the effects of small changes within the standard operating procedure or total outputs. For example, a business may attempt to increase output by 1% and analyze the positive and negative effects that occur because of the change, such as changes in overall product quality or how the change impacts the use of resources. If the results of the change are positive, the business may choose to raise production by 1% again, and reexamine the results. These small shifts and the associated changes can help a production facility determine an optimal production rate. Managers should also understand the concept of opportunity cost. Suppose a manager knows that there is room in the budget to hire an additional worker. Marginal analysis tells the manager that an additional factory worker provides net marginal benefit. This does not necessarily make the hire the right decision. Suppose the manager also knows that hiring an additional salesperson yields an even larger net marginal benefit. In this case, hiring a factory worker is the wrong decision because it is sub-optimal.
Because marginal analysis is only interested in the effect of the very next instance, it pays little attention to fixed start-up costs. Including those costs in a marginal analysis is incorrect and produces the so-called 'sunk cost fallacy' When a manufacturer wishes to expand its operations, either by adding new product lines or increasing the volume of goods produced from the current product line, a marginal analysis of the costs and benefits is necessary. Some of the costs to be examined include, but are not limited to, the cost of additional manufacturing equipment, any additional employees needed to support an increase in output, large facilities for manufacturing or storage of completed products, and as the cost of additional raw materials to produce the goods. Once all of the costs are identified and estimated, these amounts are compared to the estimated increase in sales attributed to the additional production. This analysis takes the estimated increase in income and subtracts the estimated increase in costs. If the increase in income outweighs the increase in cost, the expansion may be a wise investment. For example, consider a hat manufacturer. Each hat produced requires seventy-five cents of plastic and fabric. Your hat factory incurs $100 dollars of fixed costs per month. If you make 50 hats per month, then each hat incurs $2 of fixed costs. In this simple example, the total cost per hat, including the plastic and fabric, would be $2.75 ($2.75 = $0.75 + ($100/50)). But, if you cranked up production volume and produced 100 hats per month, then each hat would incur $1 dollar of fixed costs because fixed costs are spread out across units of output. The total cost per hat would then drop to $1.75 ($1.75 = $0.75 + ($100/100)). In this situation, increasing production volume causes marginal costs to go down. A marginal benefit (or marginal product) is an incremental increase in a consumer's benefit in using an additional unit of something. A marginal cost is an incremental increase in the expense a company incurs to produce one additional unit of something. Marginal benefits normally decline as a consumer decides to consume more and more of a single good. For example, imagine a consumer decides that she needs a new piece of jewelry for her right hand, and she heads to the mall to purchase a ring. She spends $100 for the perfect ring, and then she spots another. Since she has no need for two rings, she would be unwilling to spend another $100 on a second one. She might, however, be convinced to purchase that second ring at $50. Therefore, her marginal benefit reduces from $100 to $50 from the first to the second good. If a company has captured economies of scale, the marginal costs decline as the company produces more and more of a good. For example, a company is making fancy widgets that are in high demand. Due to this demand, the company can afford machinery that reduces the average cost to produce each widget; the more they make, the cheaper they become. On average, it costs $5 to produce a single widget, but because of the new machinery, producing the 101st widget only costs $1. Therefore, the marginal cost of producing the 101st widget is $1. Marginal analysis derives from the economic theory of marginalism—the idea that human actors make decisions on the margin. Underlying marginalism is another concept: the subjective theory of value. Marginalism is sometimes criticized as one of the "fuzzier" areas of economics, as much of what is proposed is hard to accurately measure, such as an individual consumers' marginal utility. Also, marginalism relies on the assumption of (near) perfect markets, which do not exist in the practical world. Still, the core ideas of marginalism are generally accepted by most economic schools of thought and are still used by businesses and consumers to make choices and substitute goods. Modern marginalism approaches now include the effects of psychology or those areas that now encompass behavioral economics. Reconciling neoclassic economic principles and marginalism with the evolving body of behavioral economics is one of the exciting emerging areas of contemporary economics. Since marginalism implies subjectivity in valuation, economic actors make marginal decisions based on how valuable they are in the ex-ante sense. This means marginal decisions might later be deemed regrettable or mistaken ex-post. This can be demonstrated in a cost-benefit scenario. A company might make the decision to build a new plant because it anticipates, ex-ante, the future revenues provided by the new plant to exceed the costs of building it. If the company later discovers that the plant operates at a loss, then it mistakenly calculated the cost-benefit analysis.
Economic models tell us that optimal output is where marginal benefit is equal to marginal cost, any other cost is irrelevant. That said, inaccurate calculations reflect inaccuracies in cost-benefit assumptions and measurements. Predictive marginal analysis is limited to human understanding and reason. When marginal analysis is applied reflectively, however, it can be more reliable and accurate. |
What can a manager determine by changing revenue and cost variables in an optimization analysis?
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