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What Technology Can Do for Forecasting & Budgeting in FP&A



Among the most important responsibilities of a finance team is to conduct quality forecasting and budgeting. This task helps lay the foundation of any critical business decision. Given the level of importance of budgeting and forecasting, finance teams must know the different methods available to maximize the results of their work in this task, as well as what tools can be used to improve their ability to execute these tasks. They should know the relationship of forecasting and budgeting to appropriate contexts, and what the specific benefits are from new high tech resources.


Forecasting & Budgeting


In corporate finance, the two main prediction methods include forecasting and budgeting. Budgeting is a more traditional and less flexible approach, whereas forecasting involves more improvisation.


1. Static budgeting:


This is the more basic and traditional strategy in financial planning. It often entails a one-year forecast of expenses and revenue down to net income. Static budgets are created through a “bottom up” approach, meaning that individual business units supply their own forecasts for revenue and expenses, then those forecasts are consolidated with corporate overhead, financing and capital allocations to create a complete illustration of the budget.


The static budget is the pen-to-paper filling out of the next year in a company’s strategic plan, usually spelling out a 3-5 year view of where management wants consolidated revenue and net income to be, and which products and services should drive growth and investment over the coming years. In general, the two purposes of a budget are: 1. Provide feedback for strategic decisions 2. Clarify resource allocation.


Although the traditional budgeting process has the appeal of being simple and straightforward, there are a couple important disadvantages to note regarding the use of this method.


1. The traditional budget has the potential to elicit a variety of perverse motives at the business-unit level (sandbagging): A sales manager is incentivized to provide overly conservative sales forecasts if he or she knows the forecasts will be used as a target, as they benefit from under promising, and over delivering. These kinds of biases reduce the accuracy of the forecast, which management needs in order to get an accurate picture of how the business is expected to fare.


2. The traditional budget does not adapt to what actually occurs in the business during the forecast: This process can take up to 6 months at large organizations, which requires business units to guess about their performance and budget requirements up to 18 months in advance. Thus, the budget may be inappropriate to the company’s financial situation almost as soon as it’s ready to be presented, and becomes increasingly irrelevant with each passing month.


2. Forecasting:


The process of using time series data in order to predict and estimate future dynamics in areas such as revenue, demand, and sales for inventory and resources. Specifically, financial forecasting is generally conducted with two methods:


1. Demand Forecasting:


Forecasting demand for resources such as staff and inventory is crucial for ensuring demand is met. An example of a demand forecast for a retail company would be asking the following question: “How should I plan the inventory needed for each location in the next quarter, month or week?” More accurate inventory planning for each location reduces inventory cost, and improves customer experience.


However, for both of these kinds of financial forecasting, traditional statistical modeling techniques like time series forecasting are inappropriate for handling the literal thousands of metrics and KPIs available that businesses are typically up against. You might be able to create a static forecast for the coming year based on a company’s 10 year revenue data with these older techniques, but they lack sufficient accuracy, and are unsuitable for the many more complex forecasting tasks organizations are up against in today’s corporate landscape.


2. Growth Forecasting:

Predictive growth modeling is a critical step to take in order to more accurately conduct corporate planning. This is especially effective with an accurate forecast of future growth, as this enables you to make better budgeting decisions, allocate resources more efficiently, and infer exactly what needs to be done to reach your goals. In order to create a growth forecast, FP&A professionals must ask the question: “What will be the revenue at the end of this quarter?” As you can imagine, revenue forecasting involves analyzing historical data using countless metrics such as geographic inventory, customer withdrawal patterns, etc.


Benefits From New Technologies


Having supporting systems in place is essential to quality budgeting and forecasting in FP&A. The benefits of an FP&A software can be narrowed down to a few specific benefits:

  1. Automated data flows into a forecast in order to eliminate inefficiencies and inaccuracies

  2. Additional analysis and constant updates.

  3. Enterprise grade security, hence reducing the risk of fraud

  4. Unification and accessibility of cross-departmental data

Finance professionals can improve their budgeting and forecasting even more by using software which complements spreadsheet usage.