The crystal ball of forecasting
Updated: Sep 11, 2019
It’s not a crystal ball, but if you want to get better visibility over your budget and gain the agility you need to rapidly respond to changing market conditions, it’s time to look at replacing outdated annual budgets with rolling forecasts, flexible budgets and event-driven planning.
Static data: the problem with fixed term budget cycles
The major shortcoming of the traditional fixed budgeting cycle – whether that is annual, quarterly or some other time period – is that while the data you’re working with is current at the time you complete your budgets, it becomes progressively more outdated (and therefore inaccurate) as the time period you’ve forecasted unrolls. The worst case with a fixed annual budgeting cycle is that you are working with data that is 11 months out of date, by the time you reach the last month of a 12 month cycle.
The power of the rolling forecast is that it’s updated frequently, so that you retain data currency through the forecast/budgeted period. Working with more accurate, timely information underpins stronger decision-making and allows you to change strategies or targets in line with changing market conditions.
Heading for the horizon
It’s tempting to jump on the rolling forecast bandwagon and replace annual budget cycles with a 12 month rolling forecast. After all, it’s the best way to make sure you’re not using 11 months out-of-date data, right? It turns out that, actually, 12 months may not be the best timeframe for rolling forecasting. The potential trap with a 12 month rolling forecast is that organisations tend only to feed it financial performance data. A forecast based only on past performance does not accommodate other influences – it is only a prediction of future performance based on past performance if all variables remain the same. Forecasting expert David Axson, author of Best Practices in Planning and Performance Management: From Data to Deicisionsrecommends a 90-day rolling forecast for most organisations. It’s a mistake, he says, to forecast all future periods at the same level of detail. 90 days is a timeframe in which there can be a level of confidence in the accuracy of forecast figures, and which provides a long enough view of data to support decision-making based on detailed trend analysis, not just knee-jerk reaction to current or immediate past performance.
Case study: Combining historical data with future models
A manufacturing company uses a monthly, driver-based forecast. Each month, actual data from the previous month is loaded to the forecast, building a data set that looks ahead not from a static annual budget figure, but from an evolving figure that demonstrates actual performance. The company performs more detailed analysis quarterly, and once a year takes a ‘snapshot’ of the forecast to provide as budget figures to its parent company. The frequently updated forecast is the key management tool that allows the company to react quickly to changes in market conditions.
Pass the driver
According to Axson, rolling forecasts offer greatest value when they link key operational drivers to future expected financial performance. “The objective should be to develop a driver-based rolling forecast that projects both the key operational variables of the business and the resulting financial results. For example, an effective rolling forecast should include projections for items such as inventory turns, customer conversion, promotion response rates as well as sales, gross margin, and operating expenses.” A key to identifying the business drivers is to identify those particularly volatile areas that affect the bottom line. Capturing these in forecasts will allow you to see the effects of short term volatility, and correct course as required. Forecasting in this way drives collaboration between different business units, and deepens the organisation’s understanding of the relationships between key drivers and financial results. Sales forecasts become more than simply a guesstimate of future sales, but take into account competitor pricing, planned new product introductions and marketing initiatives, the productivity of the sales force and more. Typically, involving more organisational functions in the forecasting exercise improves data accuracy and increases buy-in. The forecast becomes more than a set of numbers from the finance department – it becomes a tool by which every area of the business can measure its contribution to the organisation’s success.
Case study: Five quarter rolling look ahead
An energy company employs a quarterly rolling forecast model, with look-ahead across five quarters. It has found that quarterly, rather than its former annual, review leads to more robust forecasts. It performs its reviews typically in September, January and April. A snapshot of the April forecast is used as a budget figure for reporting purposes to the end of June, after which the budget figure is replaced by the Jul-Sep quarter’s forecast. In September, the budget figure against which performance is reported becomes the Oct-Dec quarter’s forecast. This method ensures reporting occurs against more current data than simply an annual Jul-Jun budget.
“The 2013 annual Aberdeen Group Financial Planning, Budgeting, and Forecasting survey reported that organisations that have adopted rolling forecasts were able to drive an 8% increase in operating margins over the past two years.”1
Find out how it can work for you
The Conoscenti experts can talk you through some more of the background to rolling forecasts, help you identify if they are a tool that will contribute to your organisation’s success and help you uncover the key drivers to include in your rolling forecast. We can help you with processes for initial forecasting, review and update, and analysis and evaluation of the success of your forecasting model. We can’t predict the unpredictable, but we can give you the tools to better perceive what is ahead and act on it quicker than the competition. Discover what fascinating insights can be uncovered from your current budgets and forecasts with a rolling forecast workshop.