A Real-World Financial Forecasting Example That’s Easy to Understand

A Real-World Financial Forecasting Example That’s Easy to Understand

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Without a crystal ball, financial forecasting is your best bet to see into the future of your business. While an imperfect science, forecasting is how companies project things like growth, budgets, new hires, acquisitions, and macroeconomic conditions (just to name a few). 

As you might expect, financial forecasting is complicated—there are numerous data points, assumptions, and considerations that go into many financial models. But whether your model is qualitative or quantitative, highly speculative or intensely data-driven, all financial forecasting models more or less follow the same rules and require the same analytical frameworks.

As is often the case when things are complicated, it’s easiest to understand how and why analysts build forecasting models using a real-world (hypothetical) example. Today, let’s take a look at a medium-sized granola factory called Happy Granola that wants to expand in the new year.

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Financial Forecasting Example: Happy Granola

Last year was an excellent year for our family-owned, locally sourced, environmentally conscious granola factory. Happy grew its market share after earning a contract with one of the top supermarket chains in the United States. They also introduced a new product line (Good Times Granola) with roaring success. 

This was a significant improvement over the previous year, riddled with factory recalls and unfortunate layoffs. But after this bumpy ride, the current fiscal year has been the best one on record. Happy Granola wants to continue the trend next year. That’s why they’re building a financial forecast. The primary goal: move to a bigger production facility.

Right now, Happy’s current factory is operating at full capacity. They can’t make enough granola to meet the growing demand. However, buying and moving into a bigger factory is a considerable expenditure. Several executives at Happy Granola are worried they might be biting off more than they can chew. 

Thankfully, Happy’s has a fantastic CFO and finance team that reassures them that—with a much smaller expenditure and a few weeks to burn the midnight oil—a well-made financial forecast will reveal potential risks and rewards. Here’s how they go about building that forecast:

Step 1: Set a goal or identify a problem that needs solving

All financial forecasting begins with a desired change or foreseeable challenge. For some granola factories, financial forecasting might start with a question like, “What would happen if we lost our biggest client?” or “What would happen if we acquired an oat farm?” 

The Happy finance team begins by setting a specific goal: “We want to move to a bigger factory.” Then they asked, “What will happen to our finances if we do?” 

Step 2: Decide on variables and choose the data set

Now, Happy’s team has to decide which independent variables (inputs) and dependent variables (outputs) are relevant to their forecasting model and most critical to determining whether buying a larger factory is a good investment.

Buying and moving in the most drastic cost to consider is that of the new factory. But with a bigger factory comes new equipment and additional staff. There is also the downtime to consider, transitioning to the new company, moving costs, etc. A bank loan is almost certainly in order.

However, there’s also a strong upside revenue potential. The new factory could triple production while only doubling the staff. That means, if things go well and contracts continue to grow, profits will as well. 

Step 3: Create assumptions 

Now, Happy needs to make some assumptions. Depending on how robust the financial forecasting software the finance team is using, they have to make certain assumptions to narrow down their predictions. The better the software, the fewer assumptions they have to make. 

Thankfully, Happy’s is using flexible financial forecasting software. It allows them to keep their options open and make fewer assumptions. This gives them the opportunity to play around with multiple scenarios and outcomes while looking at different variables like profit margins, factory expenditures, product line successes, economic conditions, and much more.

Step 4: Choosing a financial model

After establishing the pre-existing assumptions, the team has to choose a forecasting model that best represents the available data and the chosen variables to help them decide whether to purchase a new factory.

Deciding what modeling solution to use is critical—while Happy’s analysts have been using Excel, the CFO realizes that multidimensional analysis will be difficult and time-consuming in spreadsheets. However, if your software can implement multivariable analysis with unlimited permutations, you can produce some very sophisticated and nuanced models.

For instance, what happens if Happy’s production line continues to grow, but they lose their biggest client, and the economy goes into recession? Or if there’s a massive recall, but they find more affordable suppliers, and the cost of oats drops 20%? 

Identifying which inputs and outputs matter most and how those variables impact each other is essential to painting a holistic picture of your business’s future financial health, no matter the scenario.

Step 5: Analyzing the data

With the right software, parameters, and assumptions, the team is ready to get started. While there is no such thing as 100% certainty with any financial forecast, Happy’s can evaluate the likelihood of every possible outcome within their set of assumptions. This will help them determine the best course of action. 

For example, let’s say Happy’s decides they do want to move to a larger factory. Using their financial forecasting models, they’ve also discovered that their cash flow will likely peak during the busy season in March. Because of this, the highest likelihood of success would involve delaying the acquisition until at least Q2 of next year, when they have more cash to make the purchase and when things slow down at their current factory.

They’ve also determined that the safest way forward is to minimize their product line offering at the new facility for the first few months and focus on existing products with consistent sales. That means shelving the Good Times product line temporarily, as it requires additional equipment installation and will cause a slight delay.  

Step 6: Verification

Happy finally decides to purchase and move to the new factory. In the months leading up to the acquisition, they line up some interested buyers for their current factory and begin talks to purchase new equipment. After the big move, the finance team immediately begins to validate its assumptions, data, and analysis in real-time.

For instance, the new factory did prove to be a revenue-generating juggernaut, but not without growing pains. While most of their assumptions proved accurate, some did not. For example, after installing all their new equipment, profitability temporarily dropped because they underestimated the success of some of their newer batch offerings, like Good Times Granola. 

Making Your Financial Forecasting a Success

As you can see from the Happy Granola example, financial forecasting can be a powerful tool, but it’s not perfect. The more data you take in and the more scenario analysis you run, the higher your likelihood of success. Especially with complex, multivariable analysis, you can run multiple permutations and discover opportunities and challenges you may not have known otherwise. 

Financial forecasting is inherently iterative, but it’s critical to start with the right framework. You need software capable of understanding your company’s complexity and all the externalities that can affect it, from market forces to seasonal trends. 

We built Synario to handle the most sophisticated financial forecasting challenges. Our platform’s intuitive, streamlined, and flexible data analysis approach allows analysts to rapidly develop and test multiple scenarios. Use on/off switches for variables, levers for adjustable values, and save and share numerous scenarios within a single model, so there’s always one source of truth.

Best of all? Analysts can automatically prepare beautiful presentations for decision-makers, as well as update their models and visualizations mid-presentation. It’s never been easier to achieve confident clarity and consensus.