Three signs of an inefficient forecast
Forecasting is a practice as old as time. Well, almost as old as time—its roots can be traced back to 650 B.C., to be exact, when Babylonians attempted to forecast the weather based on atmospheric conditions.
In our times, forecasting has evolved into a wide range of applications that support diverse business objectives. Hotels, for example, often operate with three different kinds of forecasts—operational, demand, and financial—to help manage staff allocation, business performance, and fiscal outcomes.
For a corporate FP&A team, financial forecasting helps the organization prepare for market conditions and trends, and to execute strategic action plans. Increasing market volatility has resulted in a need for timelier business forecasts that can adapt to evolving business conditions.
Traditional forecasting methods that rely on legacy planning tools and manual processes often prevent organizations from achieving an agile forecasting approach. These methods can instead reduce efficiency, cause users to struggle with large volumes of data, and produce unreliable results that undermine business performance.
However, an organization can identify and overcome an inefficient forecasting process by evaluating three warning signs.
Three indications of a flat-lining financial forecast
Reducing the time taken to forecast while improving its accuracy is a top priority for many organizations. However, some FP&A teams are hampered by the slow processing of a current planning solution or they struggle with the level of detail and data needed within a manual environment. This can result in reduced accuracy and limited reliability.
The following areas can also indicate that an organization relies on an ineffective forecasting process.
- The exception became the rule. Business uncertainty can cause a forecast to occasionally perform outside of expectations; if the forecast continually trends outside tolerance levels, this could be a flag for concern. In this scenario, re-evaluate the following considerations:
- What should the acceptable level of variance be?
- What are the assumptions that should be used?
- What are the appropriate time horizons?
- Which targets will best measure accuracy?
- The process is continuous, but not in a good way. If the forecasting process has to undergo too many cycles and requires an excessive amount of adjustments, FP&A productivity becomes handcuffed. The need for too many adjustments can also challenge the credibility of its output and increase the risk for human errors.
- A lack of real-time visibility contributes to a lack of confidence. Real-time visibility into business performance and forecasts can help executive leadership peer around the corner to make strategic business decisions. When this visibility and data is absent, it could compromise strategy and efficiency, and its absence limits analytical abilities such as “what-if” analysis that instill confidence for better decision-making.
Best practices for a better financial forecast
Best practices, such as driver-based and rolling forecast approaches, can help finance teams become more adaptive in steering business performance. These methodologies incorporate financial modeling to uncover business drivers with the most significant impact on performance and consistently evaluate operating results against the forecast.
By automating processes with a cloud-based planning platform, organizations can improve forecasting performance: speeding up forecasting cycles, adapting more quickly to changes in the market, and reducing exposure to manual errors.
Best practices are better supported through technology rather than spreadsheet environments that can barely accommodate the complexity necessary for accuracy. With the right approach and tools, financial forecasts today can go far deeper than manual processes to yield the insights needed to steer better business performance.
That’s where a connected planning platform like Anaplan comes into play. The Anaplan platform can be used to take lengthy and bewildering arrays of siloed information and sort it into easily adaptable and readily available, real-time views—giving organizations the data transparency and visibility needed to forecast efficiently and make confident business decisions.