The tools and technologies now available to FP&A allow organizations to spend far more time on value-added activities like strategic business partnering and forecasting.
In March of 2019, APQC and AFP interviewed the finance lead at a large, global technology company that offers a wide range of products and services. Unifying the finance function and streamlining its processes with investments in cutting-edge tools and technologies has allowed FP&A to drastically reduce cycle times for key processes like forecasting, cut labor costs, reduce outsourcing, and focus more on strong business partnering.
“Things have never been better for FP&A at our company,” the finance lead said. “I started my career in FP&A, and I was always frustrated that the tools weren’t in place to help drive the business forward. I spent 80 or 90% of my time just getting the data and doing manually-intensive processes.”
On a broad level, FP&A is engaged in four key types of processes and practices:
- “Rhythm of the business” processes like accounting, which primarily involve backward-looking analyses of financial performance.
- Forward-looking projections like forecasting, budgeting and planning.
- Risk management activities, including control and compliance.
- Business partnering.
The tools leveraged by FP&A can be organized into three broad categories: financial analysis and reporting tools; strategy and forecasting tools; and intelligent automation. These tools and capabilities have resulted in more efficient processes that have saved significant amounts of time and money for the global tech company.
FINANCIAL ANALYSIS AND PLANNING TOOLS
Tools for financial analysis and reporting have evolved significantly over the years, said the finance lead. “Five or six years ago, we spent a lot of time making sure that the data was centralized and had the highest fidelity possible.” With 350 different finance systems and tools, however, “it was messy trying to consolidate the numbers.” The company made substantive investments in this area and now uses one global ERP. The organization also leveraged Sequel to build a data warehouse that helps streamline statutory and tax reporting with integrated intelligence capabilities from Power BI, and uses a data lake for management reporting that spans finance as well as marketing, sales and operations. These tools are what have allowed the company to drastically reduce its need for manual reporting processes.
STRATEGY AND FORECASTING TOOLS
Machine learning plays a central role in the company’s forecasting processes. The organization uses a technology platform that allows FP&A professionals to build algorithms directly into the machine learning engine and train machines for a variety of purposes. Finance leverages supervised machine learning (where the machine receives external training input) in two specific areas: regression analysis, which helps calculate figures for activities like forecasting, budgeting and headcounts; and classification, which teaches the machine to look for patterns. For example, machine learning can help the organization find new customers for a product based on existing customer profiles and can be trained for risk management by learning the characteristics of fraudulent transactions.
For revenue forecasting, for example, the organization takes a “top down” as well as “bottom up” approach, which is triangulated by the central finance team. Machine learning has allowed the company to significantly streamline forecasting while making it more accurate. “Five or six years ago, the bottom-up forecast was conducted by 600 to 700 people and took three weeks to complete. Our variance rate was around three percent, and we were always shooting to do better.” With machine learning tools, cycle times for forecasting have been reduced from weeks to hours, the variance rate has been cut by half, and labor has been reduced by 90,000 hours. While these forecasts are performed at least quarterly, machine learning capabilities mean that they can be done on a weekly or even daily basis as needed.
FP&A also leverages chat bots and virtual agents to pull important data from the cloud and push it to the right analysts in real time. For example, chat bots can generate and send an invoice to an analyst with the option to approve or reject the invoice. The organization’s IA capabilities have been extremely beneficial for FP&A and the organization more broadly: “Through the use of intelligent agents, we’re seeing huge time savings across our organization,” noted the finance lead.
Check out the full suite of resources associated with Preparing for the Next Level of Financial Planning & Analysis, including the summary guide, qualitative research and interactive industry breakdowns.