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Predictive planning has become the gold standard of planning. Adding a predictive element to planning and forecasting practices offers finance an opportunity to pivot and re-plan faster, produce more accurate forecasts, and understand the impact of more scenarios on the bottom line. Top-down, bottom-up, side to side, whichever way you plan, predictive planning can simulate the entire up and downstream effects. While implementing a robust predictive planning program might seem like a giant technology project, you don’t have to go from analog to AI overnight. The FP&A Trends report, Maximizing Potential: Unleashing the Power of Predictive Planning & Forecasting within xP&A, provides finance teams with a helpful guide to advancing predictive abilities via a staged approach. This article will summarize FP&A Trends’ research and break down:
FP&A Trends developed its predictive planning and forecasting maturity model from a series of interviews with organizations that are actively investing in PPF capabilities. The model outlines the ideal PPF implementation journey based on a staged approach. The stages are:
And, the PPF maturity model is based on three dimensions:
Implementing a mature PPF program can't be done overnight, but companies can move towards maturity by following the practical steps detailed in this model.
FP&A Trends has established that companies will pass through these five stages on their way to PPF maturity.
In the most elementary phase, Basic PPF teams are confined by the limits of the spreadsheet and the human imagination. Spreadsheets allow for only the most basic forecasting. The process is almost entirely manual.
Companies in the Developing PPF stage have slightly more advanced predictive ability than the basic stage — but importantly, they recognize the need for and benefits of high PPF maturity. Companies at this stage use separate spreadsheet models for financial planning and forecasting, but they're starting to use detailed modeling and statistical packages in some areas.
Another stage, another level up. This stage demonstrates a mid-maturity PPF process where we're seeing some predictive power coming into play — and the results that go along with it. At this stage, teams can get more detailed predictions, like by store or by product, and they're using rolling forecasts instead of fixed periods, all of which are powered by machine learning algorithms. They're also dipping their toes into scenario analysis.
Now we're getting into a PPF program that reaps the many benefits of predictive and is just shy of full maturity. At this advanced stage, predictive planning is more detailed, like by product and Line of Business (LoB). Automation and integration are the names of the game, and human intervention is minimized. For example, predictive analytics identify critical drivers, not humans. One of the defining characteristics of this stage is a three-way view between the P&L, balance sheet, and cash flow.
As FP&A Trends states, "An organization might have a modern technology platform and use ML to uncover drivers (Stage four), but if they aren't producing a three-way view of the business, they remain at Stage three."
FP&A Trends identified five strategies to help advance your PPF maturity: 1. Get senior management support: Senior management must see and understand that your current processes and techniques are insufficient for meeting your organization's needs but also recognize the potential of PPF to provide material impacts via its agile, facts-based approach to planning. 2. Work with a small, dedicated, skilled team to implement PPF: According to Wojciech Porebski, VP of Finance, Grids & Power Quality Solutions at Hitachi ABB: "The (PPF) team must be well-versed in the business landscape, understanding the intricate connections between its various components. Only then can they transform raw numbers into a compelling story that drives informed decision-making." 3. Begin with your goal in mind: What is your desired outcome? And how will you get there? Before you can map out your PPF vision, you need to understand the requirements of business partners, processes, reporting systems, and functional needs. You also need to know how existing technologies, like your ERP, BI, and closing software, will interact with new PPF software. Essentially, to be able to determine your destination, you need to know where you are now and how you’re going to measure success in the future. 4. Identify key business drivers: Fabrizio Tocchini, Head of Innovation at CCH Tagetik of Wolters Kluwer, suggested three ways to identify key business drivers: "First, choose a measure with significant organizational impact, such as revenue, and gather at least two years of detailed, consistent, and reliable historical data. Then, employ a predictive machine learning regression model to determine the accuracy of the correlations that best explain the outcomes. Finally, discuss the key drivers with those most familiar with the predicted measures." You can read more about these steps in detail in the report. 5. Take it one step at a time: Start with small projects, like improving forecasting in one specific area, and take on a learn-test-improve mentality to manage expectations. There will be stumbling blocks along the way, and you don't have to eat the entire elephant of PPF at once. What's more, even when you've implemented a PPF platform, it will take time for machine learning forecasts to surpass the accuracy of human-driven forecasts. Multiple interviewees in the study suggested that the best path for organizations new to predictive analytics is to run both human-created and predictive forecasts simultaneously and gradually phase out the manual process over time.
No matter where you land on the PPF maturity model, you can always learn from the experience of those who came before you. Read the 2023 global research report, Maximizing Potential: Unleashing the Power of Predictive Planning & Forecasting within xP&A, by FP&A Trends, to learn how senior executives from leading organizations transformed their planning and forecasting processes by combining predictive analytics with xP&A.
A 20+ year veteran of B2B and B2C Marketing and eCommerce transplant to the Financial sector, Jonathan has held key marketing and leadership positions with multiple Manufacturers, Agencies, SaaS, Finance, and Technology organizations globally.
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