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Tiffany Ma | Jun 20, 2023

Sensible ML for EPM – Customer Success Stories

At OneStream, our mission statement – every customer must be a reference and a success – drives everything we do.  And customers are our focus in this final post of our 4-part series titled Sensible Machine Learning for EPM – Future Finance at your Fingertips.  Specifically, we’ll delve into inspiring customer stories that highlight the real-world applications and benefits of Sensible Machine Learning (ML) in the Enterprise Performance Management (EPM) landscape.  Previous posts in this series discussed the path toward ML-powered intelligent planning.  In this post, we’ll show you how Sensible ML has revolutionized EPM, paving the way for better decision-making and improved financial performance.

AI’s Increasing Role in Financial Planning and Analysis (FP&A)

In today’s rapidly evolving business landscape, artificial intelligence (AI) is playing an increasingly crucial role in the realm of FP&A.  Organizations are recognizing the immense potential of AI-powered solutions to optimize FP&A processes, drive better decision-making and unlock valuable insights from complex financial data.  OneStream’s Sensible ML for EPM is at the forefront of this AI revolution, offering a powerful and practical approach to harnessing the benefits of AI in FP&A through a unified Auto ML and EPM approach, something unique in the market (see Figure 1).

Figure 1:  Sensible ML Forecast Workflow

According to Gartner, by 2028, 50% of organizations will have replaced time-consuming bottom-up forecasting approaches with AI.  That shift will result in autonomous operational, demand and other types of planning.  And our customers who have gotten a head start in automating their FP&A are seeing promising results.

Delivering 100% Customer Success

Below are a few examples of customers we have worked with and the benefits the organizations achieved with OneStream.

Polaris, a global leader in powersports, and Autoliv, a leading car safety manufacturer, both leverage Sensible ML to increase planning efficiency and forecast accuracy.  Both also gained insights into the drivers that influence their forecasting and remained agile amid shifting trends during the COVID-19 pandemic.  Leading up to the pandemic, both Polaris and Autoliv leveraged demand-based forecasts to run the business.  Once COVID-19 hit, however, the businesses shifted from demand to supply-oriented planning.

With Sensible ML’s, Polaris and Autoliv were able to quickly make that shift by easily feeding new supply-chain-oriented features into Sensible ML.  This ability to quickly adapt the planning process using Sensible ML enabled both companies to not only survive COVID-19 market impacts but also thrive in amid rapidly changing conditions.  In fact, both Polaris and Autoliv’s revenue increased over a 3-year period.

Customer success stories like that extend beyond manufacturing and into other industries as well.  Our Sensible ML customers include Financial Services, Professional Services, Retail – CPG and Grocery.  Across all customers, Sensible ML has improved accuracy by double digits over each customer’s human-generated forecasts – with significant accuracy improvements in a substantial range.

Sensible Use Cases Foster Success

Beyond providing efficient and accurate detailed weekly demand planning, Sensible ML also enables organizations to more quickly and accurately foster success through top-down quarterly strategic planning processes over 3- or 5-year (or longer) periods, monthly Annual Operating Plans, workforce planning and much more.  More granular, bottom-up type forecasting by customer, product by location and/or S&OP allows organizations to share hundreds of data points per target.  Sensible ML can create weekly or daily forecasts that even account for specific intuition from the business analysis on impacts such as holidays, weather, pricing changes, competitive impacts or any time-based intuition (see Figure 2).

Figure 2:  Multiple Use Cases Addressed by Time Series Forecasting

Planning for Downstream Processes

Studies have shown that, when collaborating with machine learning algorithms, humans can leverage their domain knowledge and intuition to refine and improve the outcomes produced by the algorithms.  In that sense, the value of having Sensible ML embedded in OneStream’s platform is the ability to easily adjust the ML forecast since no data movement, mapping or reconciliation are required between multiple systems.  Sensible ML’s forecast is immediately available in OneStream’s dashboards and reports for business planners to analyze and act upon.

This functionality also creates a seamless flow for downstream processes, such as labor or production planning.  For example, a retail customer can take Sensible ML’s forecast for sales of product A in store location XYZ and subsequently plan for the inventory to keep on hand, which in turn minimizes rush orders and maximizes revenue.  The retail customer can now also plan for staffing at each location to minimize overtime costs and satisfy customers with sufficient staff available.

Uncovering Insights

Sensible ML also has Feature Transparency dashboards surfacing insights that previously may not have been known to the business.  These dashboards display how impactful each driver (feature) was to the forecast.  By measuring the degree of impact for each driver, businesses can proactively plan for drivers or events and be better prepared when events occur (see Figure 3).

Feature Transparency dashboard within Sensible ML

Figure 3:  Feature Transparency Dashboard

Feature Transparency dashboards showing driver impact could then be used in scenario modeling to see whether offering a promotion would affect sales vs. not offering the promotion.  Or planners can create various models offering the promotion at various times of the year.

A Tug of War dashboard also shows how all the various drivers, events and macroeconomic data are affecting a forecast on an individual basis.  Customers can see both the positive and negative impact to the dollar amount for the forecasted item on each day.  Accordingly, this dashboard gives users actual evidence to substantiate the Sensible ML forecasts.  If more labor and inventory are required at store XYZ for a particular day, for example, the Tug of War dashboard gives the reasoning with an exact dollar amount to justify the spike in required resources (see Figure 4).

Tug-of-war dashboard within Sensible ML

Figure 4:  Tug of War Dashboard


OneStream’s Sensible ML for EPM has emerged as a groundbreaking solution, revolutionizing the way organizations approach planning, budgeting and forecasting.  Through the power of ML algorithms and real-world customer stories, Sensible ML has proven its ability to drive better decision-making, enhance accuracy, adapt quickly to changing business dynamics and unlock valuable insights for businesses of all sizes and industries.  From streamlining budgeting processes to improving forecasting accuracy, organizations that have embraced Sensible ML have gained a competitive edge in the market. And organizations using Sensible ML can harness the power of ML in EPM without the complexity and technical expertise typically associated with ML implementations.

Learn More

To learn more about how FP&A teams are moving beyond the AI hype, download our white paper here.

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