Sensitivity analysis in risk management PDF

Sensitivity analysis aims to eliminate uncertainty about the future by modeling financial risks and decisions. Also called what-if analysis, this type of analysis examines how changes in inputs affect outputs. The process helps with long-term decision-making. 

Sensitivity analysis is a vital part of any risk management strategy. When used correctly, it can unveil risks, identify lucrative opportunities, and enhance future planning. By illuminating the best path forward, sensitivity analysis serves as a valuable strategic tool. 

Almost every field utilizes sensitivity analysis, including geography, engineering, education, and finance. Organizations and businesses can use sensitivity analysis in a variety of ways, from analyzing how customer traffic impacts sales to seeing how capital investments impact revenues. Even individual investors can use sensitivity analysis to make better price predictions.

No matter how or why you use sensitivity analysis, it’s crucial that you do so the right way. Here, we’ll discuss how sensitivity analysis should work, as well as all the sensitivity and risk analysis techniques you should know.

Sensitivity and risk analysis: your organization’s GPS

Sensitivity analysis functions like a GPS: it maps the best way forward while helping you avoid major pitfalls, like traffic, bad weather, or worse. 

If you hope to future-proof your organization, you’ll need to incorporate sensitivity analysis into your risk management strategy. This type of analysis helps you identify the best direction for your organization by looking at potential real-world situations. Events in the real world are multi-dimensional, so your risk analysis should be, too. 

Advanced sensitivity analysis can perform limitless “what-if” analyses without ever changing the underlying model. This lets analysts quickly test the impact of independent variables on dependent variables and uncover key drivers that have the greatest impact on the business.

By enabling you to rapidly stress-test different decisions, sensitivity analysis gives you a broad view of the relationship between independent and dependent variables, as well as their financial consequences.

This holistic, unified view makes it easier for analysts and stakeholders to work together, understand all possible outcomes, and make effective, well-informed decisions.

Risk management and sensitivity analysis steps

As an analytical framework for handling uncertainty, risk analysis not only aims to decrease the likelihood that your organization takes on bad projects, but also to increase the likelihood that you discover worthwhile ones. 

Given the benefits of sensitivity analysis to risk management, all organizations should know how to meticulously perform this type of analysis. Below, we’ve outlined a step-by-step process for conducting sensitivity analysis as part of your risk management.

1. Establish a base case

In simple terms, financial models help decision-makers determine what the three most common scenarios look like:

• The best case, or one extreme
• The worst case, or the other extreme
• The base case, or the most likely outcome

The base case is the outcome most people expect to happen. Establishing a base case is the first—and, perhaps, most important—step in performing sensitivity and risk analysis.

Analysts use historical data as well as predictable assumptions (such as growth trends) to establish a base case. For instance, if your company saw 10% revenue growth in the past year, your base case for the following year may have revenue projections that are 10% higher.

It’s crucial that you identify and acknowledge the most expected outcome of each decision. This often means the most conservative outcome, one in which nothing disastrous or amazing occurs.

With this type of control scenario accounted for, you can use sensitivity analysis to rigorously test how even small changes in variables and assumptions could impact your business. 

2. Determine your variables

The next step is to determine the input and output variables that are important to your organization. What particular variables you test will ultimately depend on your project, company, and/or industry. 

For instance, if you run a retail business and are considering whether or not to expand a store, your financial analysts may perform sensitivity analysis to analyze the following input variables: 

• Cost of goods sold
• Construction costs
• Financing costs
• Employee wages
• Manager wages
• Customer traffic
• Cost of utilities

As for output variables, an analyst may look at outputs like internal rate of return (IRR), net present value (NPV), discounted payback period, net profits, and share price. It all depends on which output you’d like to sensitize. 

For example, net present value is the output of choice for most analysts when it comes to determining whether a particular project will be profitable, according to the Harvard Business Review.

That’s because NPV analysis—a method of calculating return on investment—accounts for the time value of money. It takes into account the initial investment, the acceptable rate of return, and the stream of cash flows from the investment. 

3. Test the variables

In order to successfully test these variables, you need to build out a financial model that does the following: 

• Organizes all assumptions in one place.
• Easily helps viewers identify inputs and outputs.
• Utilizes charts and graphs for data visualization.
• Identifies linear/nonlinear relationships between independent and dependent variables.
• Makes testing variables as easy as changing numbers in a spreadsheet cell.

Most analysts use spreadsheets to build these models, using ‘what-if’ analysis to determine the impact of each variable on every outcome.

Returning to the example of the retail store expansion, you may be able to answer questions such as: 

• What if foot traffic increases by 10%?
• What if labor costs increase by 5%?
• What if the cost of goods sold decreased by 5%?
• What if the construction loan had a 1% higher interest rate?

By calculating how changes in these independent input variables affect your business outputs, you can determine just how important each variable is to your financial model and projection.

With each unique variable, analysts examine how changes in those variables affect the company’s outputs, such as profit margins and IRR. By exploring a wide set of variables, stakeholders can better visualize future outcomes for their decisions. This simplifies and streamlines decision-making around capital budgeting and business strategy. 

Let’s use net present value as the output for the retail store expansion, just as an example. In order to determine whether the expansion will yield the desired returns, analysts may use the following formula for NPV:

• NPV = (Cash Flow / (1 + Required Return)))t – Initial Investment

If the result of the NPV calculation is positive, the investment in the store expansion will yield the desired returns. If it’s negative, it won’t. 

For instance, lower-than-expected foot traffic could lead to less cash flow, potentially making the project unprofitable. A lower interest rate on the construction loan, on the other hand, may improve the rate of return, increasing the project’s profitability.

Best practices and techniques for sensitivity and risk analysis

It’s vital that your organization use the best sensitivity and risk analysis techniques possible. Otherwise, you won’t get a clear overview of all the future possibilities. That means missing out on identifying key risks and capitalizing on profitable opportunities. 

When performing sensitivity and risk analysis, you’ll want to pay attention to the following techniques and best practices:

Utilize data tables and tornado charts

Your team must go through three steps when performing sensitivity analysis:

1. Model
2. Analyze
3. Communicate

This last step is vital: if you don’t communicate your findings, all the hard work of modeling and analyzing the sensitivity of outputs could be wasted.

You need a way to effectively communicate just how important each input is to the business. The best way to do that is through data visualization using tables, charts, and graphs. 

Specifically, you should use: 

• Data tables that list the impact of each variable, organized by highest to least impact
• Tornado charts that sort the variables from most to least impactful 

Understand direct versus indirect methods

Direct analysis methods account for cash flow by adding up operating activities (cash receipts and payments). Conversely, indirect methods account for cash flow by reconciling from net income.

Understanding how these two accounting practices differ is key to proper analysis as they can directly affect the data in your financial models in unexpected ways. 

For example, if you use the direct method, poor tracking of cash inflows can lead to inaccurate data in your sensitivity and risk analysis. While most organizations find the indirect method easier to employ, it’s hard to gain accuracy in real time as adjustments are being made. 

The method you choose depends on your personal preference, as well as the nature of your business or organization. Just be sure you maintain strong records—accurate data is vital to effective sensitivity and risk analysis. 

Use a capable financial model

As mentioned above, most analysts use spreadsheets when performing sensitivity and risk analysis. Unfortunately, this isn’t the best sensitivity and risk analysis technique.

That’s because spreadsheets aren’t really designed to run financial models. In fact, they have a whole lot of drawbacks. Spreadsheets tend to be:

• Too manual: Even advanced spreadsheet users can waste nine hours per week on repetitive manual entry and adjustments. 
• Two-dimensional: Testing one variable at a time is slow and inefficient. You have no way of getting “the big picture” without multiple spreadsheets and models, none of which are easy to update.
• Static and undynamic: Things change daily, but spreadsheets don’t change at all unless you make them change. This isn’t ideal for any fast-moving business.
• Vulnerable to human error: Almost 90% of spreadsheets contain an error. And any single error could compromise the integrity of an entire analysis, rendering it useless.

What you need is a more capable financial modeling solution—one that can update in real-time and test multiple variables at once without needing to change the underlying model. This way, your sensitivity analysis can broaden your view of the future and provide deepened insight into risk exposure. 

A better sensitivity analysis tool for risk management

When you utilize the best sensitivity and risk analysis techniques, you gain greater insight into risk exposure and can more effectively identify new opportunities. This helps your organization find the best path forward. 

But the reality is that you can’t do that with just a spreadsheet. You need agile, intelligent software made specifically for financial modeling. It should let you rapidly stress-test multiple scenarios and accelerate and improve your decision-making. 

Synario’s financial modeling software offers: 

• A simple toggle feature for changing inputs without touching the underlying model
• Patented layering technology that enables testings of multiple variables at once
• Automated object orientation and financial statements to limit errors and increase efficiency
• An out-of-the-box solution that can be quickly customized to run limitless ‘what-if’ analyses

All these features make your sensitivity and risk analysis more efficient and more accurate, meaning you can enjoy greater clarity when it comes to making important decisions for your organization.

Isn’t it time you put yourself in a better position to succeed?