A powerful trend in corporate finance in recent years has been a more dynamic approach to FP&A referred to as “Intelligent FP&A” (IFP&A). The key objective of this approach is to enable the fast reaction to constantly developing market conditions via a single unified process, founded upon intelligent insights into large volumes of data. However, fully grasping the concept of IFP&A is no easy task given the typical poor approach finance leaders have in teaching and defining it to their subordinates. For those trying to educate themselves or their company in techniques that could bring real business benefits, then simple labels, no matter how smart, just confuse them and waste time. Worse, the solutions behind the terms may then be overlooked, as potential users can’t see any real differences between them. With that in mind, finance teams should understand the fundamental, unambiguous qualities of “intelligent financial planning & analysis” or IFP&A, the uniqueness of this approach to finance, the tools necessary to carry out the process, and what specific examples of IFP&A look like.
Cracking IFP&A should begin with a simple definition of what “intelligence” means in this specific context. Simply defined this is:
- The ability to acquire and apply knowledge and skills
- The collection of information of military or political value
Given our unique context of IFP&A, we can replace “military or political” with “financial” or “business”.
Financial Planning & Analysis is more difficult to define. But the consensus in corporate finance is that FP&A describes plan creation (mainly financial), analyzing data, and interpreting its meaning with the aim of providing senior management with information to support strategic leadership and operational decision-making.
What’s different about IFP&A?
The terms “FP&A” and “business intelligence” have been around for many years, along with systems that support organizational decision-making. One could argue, then, that IFP&A is nothing new. But that would be missing the point. You can have the world’s best technology system and still use it in a “dumb” way. Similarly, you could develop the best plan with the latest information and still fail in its implementation.
Today, the volume of data is so large and complex that forecasts are often unpredictable: the world is changing faster than managers can anticipate. Managers can no longer rely on traditional monthly reporting of internally generated data to navigate the future.
This is where IFP&A provides its critical benefits, which can be summarized as:
IFP&A can replace the old, time-bound management processes of annual planning, monthly reporting, and quarterly forecasting with a single integrated process triggered by events and exceptions.
A dynamic, continuous approach to analytics that greatly increases the “intelligence” of an organization and its ability to change fast.
Rather than separating financial results from operational activities, IFP&A treats them as a related “cause and effect” model. This approach seeks to make the most of limited resources while linking them to the achievement of long-term goals.
But it’s not only the approach that has changed. To support this wider, connected business world, the newer FP&A solutions have significantly improved capabilities. They can cope with different types of data from a wide variety of sources and yet combine them in innovative ways. This is key to focusing management attention on what is really important, both now and in the future.
Leading the charge is the FP&A team, who must now cultivate new tools and skills to contribute to the organization, understanding that the organization’s needs will perpetually change as the business world continually develops.
Examples of IFP&A in Action
The following are a few examples that incorporate AI techniques to guide managers along the best course of action, which they would not have been able to do using traditional approaches.
This company offered services to consumers that were typically purchased only a few times each year. The market was especially price sensitive which is why it was important to ensure that the price of the services they offered were the best value at any time of the day. Given everything that has come from the Internet, the prices from competing companies could be monitored. They also were knowledgeable of market research, and the kinds of sales results they could expect.
The IFP&A solution took in data at set times of the day by service and region, which included both their own sales and the prices of competitors. If sales went below a set threshold during the day in any location and that price wise they were more expensive, the system automatically recalculated the optimal price for the location and then sent it to the local manager of concern. If accepted by the manager the system would update the online prices for new customers, which could happen multiple times each day. This approach could never have been carried out manually. Additionally, the historic trends generated by the approach helped management device pricing processes that created more accurate profit forecasts.
Enhancing Forecast Accuracy
The company in this example produced goods that were sold to large manufacturers as well as retail stores. The products were mostly seasonal and had sales lead times that only lasted for a few months.
The problem this organization encountered was in creating accurate forecasts for the different product streams in order to maximize the efficiency of production. Like most organizations, sales teams would always forecast the end of year budget figures, as no one wanted to be seen to fail or have targets increased during the year if things went too well.
The FP&A managers’ solution was a combination of trend and correlation information that accessed 2 years of historic data for each product revenue/geography stream. As each sales manager entered their forecast for the remainder of the year, the system would instantly give them a probability rating of how likely that was, along with a suggested revised target. The managers could, in such instances, still submit their original forecast, or a revised one, but with an explanation regarding why they thought the figures submitted were the ‘real’ ones. This IFP&A system was very successful and resulted in such forecasts being submitted by which senior managers could set production or develop actions that exploited or mitigated the business atmospheres they were in.
This example for the project-based planning aspect of IFP&A enabled the management to take a different approach to planning. The organization is capital intensive but whose equipment could be swapped from one type of production to another but at a cost and a delay before output could be restored. They had several production facilities around the world but in order to mitigate transport costs they would try and establish production nearer to customers.
The system would monitor forecasts and compare this with stocks already held in the different production facilities. If one product line was at risk of running out then the model would simulate whether it was cheaper to change the production schedule with its inherent costs and delays, or ship in products from other locations. Alternatively, customers could be offered an incentive to delay deliveries.
Whatever was modelled as being the most profitable for the company was then initiated. What made this example different is that the system wasn’t so much about setting local targets but treating the whole company and its results as being the main objective. By not ‘penalizing’ a local unit that didn’t produce, the company was able to plan and ensure all members felt they were performing as a single entity that together served the overall organization’s interest.