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What is financial planning?

Financial planning, also known as connected enterprise planning, allows businesses to model strategic direction and take actions to optimize financial and business performance. This approach is forward looking and used to help finance guide the business to achieve its strategy. Financial planning encompasses long-range plans, scenario modeling, annual budgeting and forecasting, ad-hoc reporting, and analysis.

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Finance uses financial planning to communicate the overall company strategy and goals to the lines of business and operations. Finance is a business partner and works with other areas to develop annual plans, such as sales and marketing plans, project plans, workforce plans, and other operational planning initiatives that contribute to the financial goals of an organization.

Over the past few years, financial planning has evolved from a periodic activity into a continuous process that takes into account historical performance, adjusting drivers along the way to make sure a company stays on target to achieve its financial goals. To accommodate this change, financial planning applications need to align with other systems, such as HCM, ERP, supply chain, and operations, to build plans for a connected view of the whole organization.

The financial spreadsheet—is it still used in planning?

Despite the availability of enterprise-wide planning and budgeting software, many finance professionals still make use of spreadsheets. But financial spreadsheets are inherently difficult for forward-thinking planning for a number of reasons. First, spreadsheets are fraught with risks, such as the lack of audit and security, increased human error, multiple versions with no governance, and more. With spreadsheets, data takes longer to collect so it can easily be out of date by the time it is added. Organizations are left with mere snapshots of financial and operational plans.

Financial planning spreadsheets are disconnected and segmented in different areas of the business so it’s very difficult to have a clear line of sight to plan across the organization. Multiple spreadsheets are difficult to keep track off, with multiple macros and links from one spreadsheet to another.

But perhaps more importantly, spreadsheets were never designed to handle today’s company-wide planning, budgeting, forecasting, reporting, and analysis requirements. They are difficult to download to an ERP or other organizational system. And spreadsheets don’t incorporate advanced technologies that can leverage multiple data sources and predictive capabilities, such as detailed, what-if analysis.

History of financial planning

Historically, financial planning was a very manual process that was disconnected from that other areas of the business. Not very agile or accurate, yesterday’s financial planning was only done on a quarterly and/or yearly basis. It was typically done on a plethora of Excel spreadsheets that introduced risk around security, error, speed, and inaccuracy. Because of the inaccurate and dated information, it was often difficult to accurately forecast and adjust based on more immediate changes in the business.

Graphic showing the maturity model of planning and forecasting from manual forecasting limited by human bias and capability to data-driven forecasting on large scale
The evolution of planning and forecasting methods for financial planning and analysis (FP&A)

Some organizations viewed planning as an annual exercise that had to be done, rather than a value-added task that could be used to truly guide the business.

Financial planning software roadmap

In contrast, today’s financial planning is data-driven. Planning has changed from a periodic activity typically carried out by finance to a more continuous and connected process. Financial planning is becoming increasingly predictive, incorporating data science and using best practices and methods to focus not only on what has happened or is currently happening, but why and how it’s happening—and what is likely to happen in the future.

Financial planning has evolved over the years, from a very manual, human input process to a more data-driven process that can also incorporate machine learning, AI, and other advanced technologies. Planning and forecasting decisions used to be based on historical trends; now forecasting includes machine learning predictions based on multiple data points, scenarios, and trends for an even more agile and accurate planning process.

Planning and budgeting

Planning and budgeting software has been around for more than 25 years, but has evolved significantly from on-premises or client/server-based solutions to cloud-based solutions. This allows the software to be widely used across the organization in finance, lines of business, and operations to deliver a fully connected enterprise plan.

When looking at selecting a planning and budgeting tool for your organization, there are five major things you should consider:

1. Built-in planning intelligence and best practice planning frameworks

A planning and budgeting solution should not only be a blank canvas for modeling, but it should also contain best practices that you can start using right away, such as planning intelligence and built-in capabilities for predictive planning, driver-based budgeting, robust what-if scenario modeling, sandboxing, bottom-up/top-down budgeting, approvals, and workflows.

Also, you should expect purpose-built and supported modules—such as long-range planning, workforce planning, capital asset planning, project financial planning—that are fully functioning modules, designed to work together and seamlessly integrated with your existing planning processes.

2. Capabilities that span finance, operations, and line-of-business planning

You should look for a connected planning platform that is a truly comprehensive solution, providing not only financial planning, but also operational planning and modeling to address lines of business, such as HR, IT, supply chain, and sales. This should be developed and maintained by your software vendor and not simply an add-on available in a "marketplace."

3. Users can perform large-scale, free-form financial and operational modeling

The demands of today’s fast-paced, agile business models require the ability to easily model financial and operational scenarios. A key capability behind this is the system’s ability to take in and process large volumes of data to be used in free-form modeling. It is critical to have a powerful back-end engine to handle the vast amount of data that businesses use for such analytics. This is a must-have for a planning and forecasting solution to live up to promises about ad-hoc modeling. In addition, make sure that scalability across large volumes of data and users can be easily handled.

4. Robust management and financial reporting

Reporting can be a catch phrase for doing a lot of different things. You might want to do ad-hoc analysis, slicing and dicing your data. You might just want to use a standard dashboard for status updates. You probably still require a standard pack of pixel-perfect reports that can be easily printed.

Most organizations are looking to modernize and streamline their management reporting by adding collaborative narrative elements when preparing their reporting packages. Make sure that your planning systems can do all of these—not just as a demo.

A comprehensive EPM planning solution should cover all reporting requirements, including dashboards, ad-hoc analysis, pixel-perfect financial statements, and complete narrative reports—all of this possible via browsers, mobile devices, and other familiar tools. All reporting requirements, from complex budget books with narrative through to ad-hoc analysis, should be available in spreadsheet interfaces that finance professionals are familiar with and can easily use. This kind of flexibility is important because the fast-changing nature of global business requires a lot of ad-hoc analysis and must not compromise data security.

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5. Automated financial data analysis through embedded machine learning

Emerging technologies, such as machine learning, are quickly changing business practices. Through the use of data science, predictive analytics can uncover correlations, outliers, or exceptions that a person alone wouldn’t be able to recognize. It can materially improve the accuracy of planning and greatly reduce time spent in planning processes and analyzing data. Focus on taking action on anomalies and outliers and remove bias from your forecasts by leveraging built-in data science—without the need for data scientists.


Budget

A budget requires analyzing and comparing actual verses expected financial performance to determine how to allocate expenditures for the organization.

Elements of most company’s budgets include the following:

  • Expense budgets
  • Capital expenditure budgets
  • Detailed revenue budgets
  • Cash-flow budgets
  • Detailed manufacturing budgets

Zero-based budgeting

Zero-based budgeting is a budgeting discipline that is normally used to streamline costs within an organization. This is based on a practice where all costs must be budgeted for and justified at a very granular level. Previous budgets are disregarded and all budgets are started from a zero base (no regard for prior costs). This is often seen as a cost-cutting process, but can be used to make sure resources are focused on revenue-generating activities.

Top down vs. bottom up budgeting

A top-down approach involves the senior management developing a high-level budget for the entire organization and allocating the targets from a corporate view down to an operational plan at a lower departmental or budget owner level. With a bottom-up approach, the process starts with the individual departments or budget owners creating a budget and then submitting it to higher-level budget stakeholders for approval.

Forecasts

Forecasts or forecasting refers to a process where adjustments are made periodically or continuously based on performance against budget targets. This is a process of modeling and implementing financial and operational adjustments to align better with the allocated targets. This process is also referred to as rolling forecasts, which take place on a continuous basis.

Difference between budget and forecast

A budget outlines the financial expectation for what a company wants to achieve for a specific period in the future. It helps set the financial basis to plan for how an organization can execute its strategy or long-range plans. A company's budget is usually re-evaluated periodically, most often semi-annually or annually. A budget includes the following:

  • Estimates of revenues and expenses
  • Expected cash flows
  • Expected debt reduction
  • Benchmarks to compare actual results with expected performance

In contrast, a financial forecast makes adjustments to the plan based on past performance to realign priorities, targets, and actions to make sure that the annual budget can be achieved. A management team can use financial forecasting and take immediate action based on the actual data. A forecast is developed and reassessed much more frequently than a budget. In many instances, forecasting is a continuous process throughout the year.

Financial forecasting methods

There are different financial forecasting methods that use both qualitative forecasting, quantitative forecasting, and a combination of the two.

Scenario planning

One type of financial modelling is scenario planning, a process in which FP&A employees map out the best-case, expected, and worst-case scenarios to put the business in the best financial position. Based on those results, organizations can identify steps to respond to different outcomes. These projections can also help plan for headcount, market downturns, projects, product rollouts, capital expenses, and other investments.

Monte Carlo simulations

Monte Carlo simulations are used to model the probability of different outcomes in a process that cannot easily be predicted. It’s sometimes used to understand the impact of risk and uncertainty in predictions and forecasting models. You can use Monte Carlo simulations to determine the likelihood of various scenarios, giving you confidence in your decisions.

Straight line forecasting method

This method is commonly used when the company’s growth rate is constant, to get a straightforward view of continued growth at the same rate. It involves only basic math and historical data. Ultimately, it renders growth predictions that can guide financial and budget goals.

Moving average forecasting method

A moving average is the calculation of average performance around a given metric in shorter time frames than straight line, such as days, months, or quarters. It is not used for longer time periods, such as years, because that creates too much lag to be useful in following trends.

Simple linear regression forecasting method

It is used to chart a trend line based on the relationship between a dependent and independent variable. A linear regression analysis shows the changes in a dependent variable on the y-axis to the changes in the explanatory variable on the x-axis. The correlation between the X and Y variables creates a graph line, indicating a trend, which generally moves up or down, or holds consistent.

Multiple linear regression forecasting method

This method uses more than two independent variables to make a projection. Basically, multiple linear regression (MLR) creates a model of the relationship between the independent explanatory variables (parameters) and the dependent response variable (outcome).