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How CFOs & FP&As Use Scenario Planning With AI

CFOs FP&As Scenario Planning With AI

The latest on how finance businesses leverage AI- and ML-Driven scenario planning during uncertainty.

AI-powered scenario planning offers significant benefits to Finance professionals navigating today’s rapidly changing business environment. Despite this demonstrated value, however, many organizations continue to under-utilize scenario planning due to a lack of time and technology to support a process that requires substantial amounts of data for anomaly detection. AI-powered scenario planning process that unleashes the true value of Finance teams by enabling faster decisions, increasing better and more frequent collaboration and increasing forecasting accuracy to drive better strategic decision-making.

For many CFOs and FP&A teams, change is always there. Breakthroughs in artificial intelligence and machine learning can now predict the future with near 100% accuracy.

This article aims to help Finance teams to develop AI- or ML- powered scenario planning processes to enable them to anticipate and plan for a variety of potential scenarios and make better informed operational, financial and strategic decisions.

  • What scenario planning is and why it is important
  • The business challenges to adopting scenario planning
  • How AI-powered scenario planning creates agility in the organization
  • Customer success with AI-powered forecasting

AI-Powered Scenario Planning

In today’s rapidly changing and competitive business environment, Finance must position itself as a key business partner to the organization by anticipating and planning for a variety
of scenarios.

For most CFOs and Financial Planning and Analysis (FP&A) teams, change has become an ever-present constant. Yet despite the most advanced technologies available thanks to breakthroughs in artificial intelligence (AI) and machine learning (ML) capabilities, predicting the future with 100% accuracy is still impossible.

​Anyone who has prepared the annual budget can attest that, even in the most stable financial conditions, changes will occur after the forecasting process ends that impact the financials. As a result, Finance must be prepared for what may come even – and especially – if it doesn’t align with Plan A or Plan B.

​Scenario planning enables organizations to anticipate and plan for a variety of potential scenarios and make better informed operational, financial and strategic decisions (see Figure 1). And with the explosive growth in data availability combined with packaged AutoAI capabilities, Finance teams have a massive opportunity to build better planning strategies, make smarter decisions, and execute more effectively.

​AI-powered scenario planning enables organizations to identify opportunities faster and understand how to capitalize on them to outperform their competition by a wide margin.

Why Scenario Planning is Important

Organizations that make the time for scenario planning see measurable improvements in budgeting, planning and forecasting results, according to the FSN survey. In fact, 77% of organizations that utilize scenario planning can reforecast earnings within a week. Nearly double the number of scenario planners (31%) can forecast a year ahead compared to their non-scenario planning counterparts (16%). The value of incorporating scenario planning generates improvements across many areas of the forecasting process.

​Scenario planners can make changes to the budget, roll those changes out to data entry templates, reflect those changes in reporting to stakeholders and make changes to hierarchy structures within half a day — and do it all in higher numbers across the board compared to those who don’t use scenario planning.

​Unfortunately, however, despite these huge benefits to financial outlooks and results, the adoption of scenario planning remains low. But why? Well, performing scenario planning with outdated technologies, manual processes and manual data gathering is inaccurate and inefficient. In other words, planning under such circumstances simply takes too much time to provide valuable insight.

STRESS TESTS

Scenario planners are twice as likely to be able to forecast a year ahead…

  • Are you able to reforecast earnings in under a week?
  • Are you able to forecast a year ahead with confidence?
  • Are you able to forecast revenue to within +/- 5%?
  • Are you able to forecast earnings to within +/- 5%?
  • Are you able to make a minor change to the budget within half a day?
  • Are you able to roll out a minor change to budget holder’s templates in half a day?
  • Are you able to reflect a minor change in all reports within half a day?
  • Are you able to make a simple change to a hierarchy in half a day?

Scenario Planning Challenges

Why are so many organizations slow to adopt scenario planning despite its demonstrated value? When the process relies on too much manual intervention, Finance lacks control and repeatability in the process. These shortfalls lead to too much time wasted wrangling (see Figure 3) and analyzing data and leave too little time to draw meaningful insights that lead to strategic decision making. Relying on fragmented silos of spreadsheets and legacy corporate performance management (CPM) software for planning needs ultimately results in disjointed processes around technology and inhibits key collaboration and business agility required to drive performance in an environment where speed is king.

As a result, the overwhelming majority of organizations cannot find sufficient time and manpower to engage in effective scenario planning. Why? A major contributor is a lack of maturity in technological enablers. But those organizations currently leveraging scenario planning are doing so with better technology and better processes. For CFOs and FP&A teams seeking to enable their business partners with effective scenario planning, automating and streamlining the process with AI will improve accuracy and eliminate painful, time-consuming manual processes. Finance leaders who successfully do so can confidently lead their organizations through unprecedented levels of uncertainty.

Be Agile with AI Scenario Planning

AutoAI solutions can break down the barriers that have traditionally held back Finance and operations teams by powering scenario planning with time-series ML forecasting across hundreds or even thousands for targets — and do so at scale within a seamless user experience and single solution.

The Benefits of AI-Powered Scenario Planning Include the Following:

  • Enhanced Speed and Accuracy – AI-powered scenario planning automates many manual tasks involved in traditional scenario planning, ultimately reducing the time to generate and share the results. In addition, in today’s digital economy, companies have access to more data than ever. AI can process this ever growing data and consider additional business intuition — such as events, pricing, competitive information and weather — to produce more accurate scenario predictions. AI-enabled scenario modeling can automate a lot of the repetitive, menial tasks required in scenario planning and provide more time for analysts to develop strategic recommendations for the organization based on the results of AI-generated scenarios.
  • Improve Risk Management – Finance can identify and react to potential risks easier and quicker, as well as more effortlessly capitalize on opportunities by leveraging AI-powered scenario planning. For instance, AI can incorporate substantially more drivers, both internal and external, to automatically identify correlations, patterns and anomalies across thousands of products and locations, which can then be used to make better-informed decisions on managing risk and preparing for potential scenarios. This efficiency will dramatically cut down the amount of time needed to complete various “what-if” analyses to better plan for the rapidly changing environment. Now, planners can focus more on leveraging the potential scenarios rather than producing them.
  • Cross-functional Collaboration – Machine learning has the capability to forecast at very granular and frequent levels to support processes such as demand planning and S&OP processes where planning by product and/or region is required. Such forecasting
  • can, with ML, be done on a daily or weekly basis. These processes drive cross-collaboration between operational and financial scenario planning. In essence, effective scenario planning looks across the organization for various scenarios that might impact financials, negatively or positively, and that process requires cross-functional collaboration outside the Finance department and across various lines of business. Engaging in scenario planning creates a process by which scenario planners broaden their awareness of external influences (Figure 5) and appreciate the importance of including a wide range of inputs in planning, budgeting and forecasting.
  • Better Strategic Decisions – Ultimately, all the benefits gained through AI-powered scenario planning result in better strategic decision-making. By analyzing more data faster and with better accuracy, analysts can then use their time to take a more comprehensive approach to various possibilities and how they would impact the company’s financial health. This approach gives Finance leaders visibility into how various scenarios would impact cost of goods sold (COGS), gross margin, EBITDA, cash flow and other important financial metrics that are critical KPIs to understand when navigating uncertainty. All leaders want timely and accurate insights to increase performance efficiently and effectively, and AI-powered scenario planning will help leaders do that.

Example of Success with AI Scenario Planning

Autoliv, Inc. is the worldwide leader in automotive safety systems. Autoliv develops, manufactures and markets protective systems, such as airbags, seatbelts, and steering wheels for all major automotive manufacturers in the world. As well as mobility safety solutions, such as pedestrian protection, connected safety services and safety solutions for riders of powered two wheelers.

Used by major car manufacturers worldwide, Autoliv’s products save over 35,000 lives every year. Through organic growth, acquisition, and a merger, Autoliv has become a market leader with over 65,000 employees across 27 countries. On a mission to deliver forecasting and reporting through the entire value chain, Autoliv selected OneStream for their actuals and unified planning processes. By having a single platform for unified business planning and reporting, Autoliv has transformed financial and operational planning into a collaborative, supported approach.

CHALLENGES

M&A ACTIVITY AND ORGANIC GROWTH CREATED A COMPLEX, MULTI-DIVISIONAL OPERATIONAL STRUCTURE.

NEED TO DELIVER COMPREHENSIVE SUPPORT FOR SALES, OPERATIONS AND FINANCE.

DATA WAS DELIVERED WITH LIMITED GRANULARITY AND INSUFFICIENT ACCURACY REQUIRED.

UNABLE TO GAIN INSIGHTS WITH DATA SPREAD ACROSS 40 OPERATIONAL SYSTEMS, INCLUDING ERPS, HYPERION PLANNING, ESSBASE AND HFM.

IMMEDIATE NEED TO REPLACE HFM BEFORE BEING FORCED INTO ANOTHER UPGRADE.

NEEDED TO REACH A COMPLETE GLOBAL VIEW OF VERTICAL PROFITABILITY.

BENEFITS

ONE SOURCE OF TRUTH, IMPACTING DECISIONS DAILY.

CREATED A GLOBAL VIEW OF PROFITABILITY ALLOWING TEAMS TO PLAN ON COSTS DOWN TO SKU.

VIEW OF PROFITABILITY AND P&L ACROSS PRODUCTS, PROJECTS AND PROCESSES.

FINANCE FUNCTION HAS TRANSFORMED INTO STRATEGIC BUSINESS PARTNERS FOR PLANNING, ESSBASE AND HFM.

REDUCED FLUCTUATIONS IN LABOR PLANNING AND OVERTIME EXPENDITURES.

INCREASED ACCURACY OF DEMAND FORECASTS RESULTING IN REDUCED EXCESS INVENTORY COSTS.

AI FORECAST PRODUCES GRANULAR FORECASTS DAILY FOR 1,200 PARTS BY CUSTOMER.

INCREASED AGILITY WITH NEW DRIVERS REPRESENTATIVE OF THE CURRENT ENVIRONMENT.

AI Forecast Unifies Demand Planning with Financial Goals

Autoliv is exploring ML-enabled forecasting to improve operational forecasting and interpret customer demand planning. By unifying granular demand and supply forecasts with financial plans, SensibleAI Forecast would enable Autoliv to proactively respond to changes in the market, as information moves along the value chain, and impact what they produce. Here are a few additional benefits of the approach:

  • Recognizing underlying demand trends
  • Reducing volatility
  • Improving operational stability and thus reducing premium freight
  • Reducing fluctuations in labor planning resulting in reduction of overtime expenditures
  • Increasing accuracy of demand forecasts resulting in reduced excess inventory costs

By leveraging SensibleAI Forecast’s capabilities to produce detailed, granular forecasts at a daily level, Autoliv was able to create forecasts to match the granularity of their demand planning. In addition, SensibleAI Forecast can produce more accurate and more frequent forecasts at scale and at a fraction of the time and cost (Figure 6). This would allow Autoliv to quickly pivot from demand forecasting to supply-oriented forecasting due to Covid disruptions of the market conditions. Using SensibleAI Forecast’s Auto AI feature, Autoliv’s team could automatically bring in new drivers representative of the current business environment and therefore save time and increase agility.

MEAN SQUARED ERROR IMPROVEMENT OVER EDI: 35%

MEAN SQUARED ERROR IMPROVEMENT OVER THE HUMAN-ADJUSTED EDI: 10%

AI-powered scenario planning offers significant benefits to Finance professionals navigating today’s rapidly changing business environment. Despite this demonstrated value, however, many organizations continue to under-utilize scenario planning due to a lack of time and technology to support a process that requires substantial amounts of data for anomaly detection. AI-powered scenario planning process that unleashes the true value of Finance teams by enabling faster decisions, increasing better and more frequent collaboration and increasing forecasting accuracy to drive better strategic decision-making.