More CFOs are coming to recognize that as post-pandemic volatility mounts, leveraging opportunities to employ next-generation forecasting approaches has become the X factor that is elevating high-performing finance groups, and leading companies, above the pack.
Next-generation capability differs from traditional forecasting in fundamental ways. With a traditional approach, the finance team collects a stable set of fixed costs (including product and service prices), demand projections and other inputs—primarily from internal sources—to generate sales and revenue projections. By comparison, more advanced forecasting methods—what we term next-generation forecasts—are dynamic in nature. They are formulated by automatically accessing real-time data from an evolving collection of internal and external systems. These analyses extend beyond sales projections and take measure of delivery streams by gauging the highly variable factors and costs that ultimately determine if and when the organization will earn revenue, and how much it will earn. Such advanced-level forecasts are also being used to inform approaches to pricing, quantities, delivery centers and timing, among other areas.
Sustained bursts of economic, political, social and environmental upheaval require finance groups to enhance their forecasts to become continuous based on automatic data feeds, equipping internal stakeholders throughout the organization with the real-time financial insights they need to make crucial decisions, often under pressure. The pandemic was just the latest example of a catalyst to such upheaval. Yet, COVID-19-related disruptions struck at a point when many finance groups had achieved enough transformation progress to stress-test newly digital capabilities, determine what worked and quickly recalibrate their forecasting processes. That baptism by fire yielded rich insights.
There are plenty of reasons why CFOs and their finance groups who have yet to modernize their traditional forecasting capabilities should initiate these efforts:
- Delivery streams have become pivotal: Ongoing supply chain disruptions, deficits of raw materials, skills shortages and other disruptive constraints make traditional sales and revenue projections less reliable. Just because a contract or an agreement is in place does not ensure whether and when a company can deliver on that agreement before it expires. Forecasting needs to home in on the factors that enable delivery and determine if and when that revenue is actually earned or if and when expenses are actually incurred.
- What was fixed is now variable: Traditional fixed costs—including but not limited to labor, real estate and raw materials—have become highly variable in the post-pandemic era. The shift to remote work and the premium placed on technological expertise have opened a new front in the talent war while injecting greater variance into salaries, employee productivity, and even manufacturing and service quality. Labor models have become more dependent on the contractual fringe and temporary workers. Forecasting needs to address all elements of this variability. At the same time, next-generation agile forecasting methods must be validated carefully before they are employed and must be audited afterward.
- Understanding and capitalizing on new business opportunities: Government stimulus programs worldwide have created trillions of dollars in opportunities for new business, and the effects of the pandemic have accelerated merger and acquisition possibilities across industries. Forecasts need to reflect these opportunities as they arise, rather than on a quarterly or monthly basis.
- COVID’s staying power: The pace and penetration of COVID infections and vaccinations differ around the world, driving inconsistent post-pandemic business conditions that appear likely to linger. Such conditions and variability can limit the effectiveness of centralized forecasts that aggregate projections from global regions.
Common hallmarks of advanced, next-generation forecasting capabilities include the following:
- Speed, versatility and automation: Cloud-based automation and advanced analytical tools enable planning teams to produce targeted forecasts and reforecasts quickly and as needed, while limiting the need for manual work during a widespread finance and accounting talent crunch. These same advanced analytical tools, especially if elements of machine learning or algorithmic forecasts are utilized, require special risk-mitigation measures—for example, post-event audits to determine whether the forecasting process was effective or significantly off. CFOs should be mindful of potential bias in the underlying data or the algorithms and models used, and root it out if it exists.
- External data reliance: We’re seeing more companies incorporate data sharing stipulations into contracts with suppliers and vendors, a sure sign of the value that finance groups place on obtaining external data to fuel forecasts. More CFOs recognize that the efficacy of their projections and analyses increasingly relies on data outside of their systems and organizations. Keep in mind, though, that from a risk perspective, the use of external data must be monitored carefully to ensure the data is comparable and relevant. Otherwise, incorrect forecasts will result.
- KBIs vs. KPIs: With a next-generation approach, internal and external data sets sourced for planning and forecasting are used to developed more robust and relevant measures that extend beyond days sales outstanding (DSO) and other traditional finance and accounting key performance indicators (KPIs). A broader set of key business indicators, or KBIs, provide deeper visibility into sales, customer relationships, delivery streams and profitability. Whereas DSO KPIs lump B2B customers into buckets based on whether they pay in 30, 45 or 60-plus days, a KBI might distinguish among customers that pay on the fifth of every month versus those that pay in the last week. Other KBIs are being used to go beyond traditional headcount indicators to monitor the productivity, engagement and quality of a workforce, which in turn affects that organization’s ability to deliver goods and services. From a risk perspective, a full scope assessment of inputs and assumptions regarding KBIs should be performed.
- Beyond finance: Forecasting experts and practitioners are finding that the traditional term “financial planning and analysis” no longer suffices, as the creation and use of financial insights reach well beyond finance. Of note, Gartner uses the phrase “extended planning and analysis” to describe how more planning and forecasting capabilities interact with supply chain, talent management, sales and marketing, and other core business operations. Here, collaboration is key. CFOs and finance teams should work closely with these groups, avoiding ingrained tendencies within finance to focus too much on confidentiality and, in turn, sacrificing the ability to generate higher quality analyses.
- Stress-testing different scenarios to enable recalibrations: It’s beneficial to stress-test forecasting for different “what-if” scenarios based on a variety of alternative factors, such as fluctuating interest rates, rising inflation or changes in GDP growth rates. Government actions, potential recovery paths of the pandemic and the related impact on customer behavior should be considered. Recalibrating forecasting is easier when multiple alternative scenarios have been modeled ahead of time.
- More self-service: When the pandemic shut down water-cooler discussions and in-person chats, leading finance groups increased their deployment of self-service technology tools and interfaces that internal customers use to conduct forecasting and planning activities on their own. This flexibility is doubly valuable, bolstering more timely decision-making throughout the organization and freeing up finite finance resources. Here, the ability of line managers to perform rapid scenario forecasts based on changing inputs is key.
In closing, developing a next-generation forecasting capability is a complex endeavor that requires the coordination of numerous processes, tools, data sets and relationships. CFOs must pay close attention to two potentially significant obstacles. One is data quality. If the data being analyzed is not reliable and rich, even the most cutting-edge tools will fail to produce actionable insights. A second impediment is the puzzling and widespread reliance on old-school annual budgeting cycles. The objectives, insights and allocations that materialize during budgeting season frequently become stale within a matter of weeks. This only promises to continue in the post-pandemic world. If forecasting processes are going to make an evolutionary leap, the annual budgeting cycle will need to come along.
This article originally appeared on Forbes CFO Network.
