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With the increasing frequency and magnitude of economic volatility, FP&A teams have been under more pressure than ever.  Traditional processes for quarterly or annual budgets are no longer relevant as the pace of change quickly renders these static budgets stale.  Additionally, increasing data sources and exponential growth in data requires data transfer, reconciliation and consolidation before insights can be factored into budgets, forecasts or plans.  Fortunately, business planners are in luck.  Why?  AI transforms enterprise Finance.

That transformation occurs through faster processing of data, increased forecast accuracy and deeper insights.  Rapidly becoming an essential tool for enterprises of all sizes, AI streamlines processes, unlocks valuable insights and drives strategic decision-making.  Soon, AI will be embedded across all enterprise Finance processes.  This blog post therefore explores how AI transforms Finance, setting the stage for a deeper dive into OneStream’s innovative Sensible ML solution.

How AI Transforms Enterprise Finance

Enterprise Finance is transformed through AI by enabling a variety of capabilities. Specifically, AI enables the following:

Predictive Analytics and Forecasting

AI-powered predictive analytics is revolutionizing financial forecasting for enterprises.  By analyzing historical data and market trends, AI algorithms can accurately predict future financial outcomes, enabling organizations to make informed decisions and mitigate risks effectively.  These predictive models provide valuable insights into cash flow management, revenue projections and expenditure planning.  As a result, Finance professionals are empowered to allocate resources efficiently and optimize financial performance.

Process Automation and Efficiency

Automation is a cornerstone of modern Finance operations, enabling enterprises to streamline repetitive tasks, reduce manual errors and enhance operational efficiency.  With purpose-built AI solutions, Finance can automate mundane financial processes (e.g., data entry, forecast creation and reconciliation).  Finance teams then have more time to focus on value-added activities.  By integrating AI-driven automation tools into workflows, enterprises can thus accelerate decision-making processes, improve data accuracy and achieve cost savings.

Risk Management and Anomaly Detection                    

Enterprise Finance involves inherent risks, including compliance violations, human error and market fluctuations.  To detect anomalies and identify suspicious activities in real-time, AI-driven risk management solutions leverage advanced algorithms.  How?  AI’s ability to process vast amounts of data allows for the identification of patterns and anomalous data points.

This ability facilitates exception handling for Finance processes such as planning, data quality and reporting.  Thus, Finance professionals can streamline operations and focus on exceptions rather than sift through entire datasets.  This approach ultimately saves valuable time and enhances the efficiency of financial processes.

hand on ipad looking at dashboard

As Finance continues to embrace AI, a practical and sensible ML approach – one that balances automation with transparency and human insight – has become increasingly important.  Effective planning is, after all, critical for businesses to remain competitive and adapt to changing market conditions.

At OneStream, we call this Sensible ML.

Introducing Sensible ML

OneStream’s Sensible ML (see Figure 1) is a paradigm shift in leveraging AI for Finance professionals.  By seamlessly unifying AI within an enterprise Finance platform, Sensible ML – which is purpose-built for FP&A – creates  thousands of forecasts and insights, which were previously impossible to do with manual processes.

Figure 1:  Sensible ML Process Flow

Purpose-Built AI for FP&A in a Unified Platform

By integrating AI, Finance teams can seamlessly leverage AI capabilities without having separate tools, systems or teams.  No longer are the days of having data scientists create a forecast without understanding the business value.  Also gone are the days the Finance team receives the output with no understanding of where the numbers originated.

Instead, with purpose-built AI for Finance and Operations, business planners are independently creating ML-backed forecasts.  And these planners are doing so for the entire ML-forecasting process – from data ingestion and quality to model building, all the way to utilization and consumption.  Finance professionals can now explain their accurate forecasts with confidence and at scale across hundreds or thousands of forecasts.

Sensible ML also incorporates external factors (e.g., weather, macroeconomic factors) to create highly accurate forecasts and utilizes a unique, groundbreaking concept:  the Model Arena.

The Model Arena offers tailored precision by automatically selecting the most performant model for each forecasted line item.  In contrast, the one-size-fits-all approach applies a single model for all forecasted line items, failing to account for the characteristics of each product-location combination.  The Model Arena approach instead produces a much higher level of accuracy by accounting for the nuances of different forecasted products by locations.

Polaris, a global leader in powersports whose products have vastly different characteristics, offers a good example of the power of Sensible ML.  Specifically, Polaris can now forecast for specific products and locations with distinct models across the business.  Only a unique ML model tailored for Polaris snowmobiles or off-road vehicles can create an accurate sales forecast.  In turn, Polaris can then optimize for downstream processes such as resource allocation or maximize the contribution margin.

Polaris products of snowmobile and off-road vehicle

Sensible ML’s Model Arena automatically selects the most accurate ML model for every product-location combination within differing business units.  Ultimately, then, Sensible ML arms Finance professionals with deeper insights into future financial scenarios, enabling better decision-making and strategic planning.

Conclusion

As AI continues to evolve, the impact on enterprise Finance will only intensify.  AI will continue revolutionizing traditional practices and unlocking new opportunities for growth and innovation.  From predictive analytics to process automation and risk management, AI empowers enterprises to navigate complex financial challenges with confidence and agility.  Finance professionals who use OneStream’s Sensible ML solution can thus unlock the full potential of AI to drive sustainable business success in the digital era.

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Want to learn more about how AI transforms enterprise Finance for FP&A teams?  Stay tuned for additional posts from our Sensible ML blog series, or download our white paper here.

Download the White Paper

In recent years, the integration of artificial intelligence (AI) into various sectors has transformed operations – improving efficiency, accuracy and decision-making processes.  One area that holds significant promise is AI for higher education Finance.  With the ever-increasing complexities and challenges universities and colleges face, the responsible use of AI presents an opportunity to streamline operations, empower data-driven insights and enhance budgeting and planning.

Finance leaders approach the idea of integrating AI into higher education with a mix of excitement and cautious optimism.  For example, over half of Finance leaders already envision AI becoming a core component of financial processes; a staggering 79% of Finance decision-makers also believe AI will increase productivity by increasing efficiency and improving accuracy in business processes.  These potential benefits in higher education could reshape financial management practices and unlock innovation opportunities.

How Does AI for Higher Education Finance Add Value?

AI in Finance is not about replacing human intelligence but about enhancing it.  As a result of AI automating repetitive and time-consuming tasks, Finance teams can focus on more strategic activities.  For instance, AI-powered tools can automate planning, data entry and routine financial reporting – freeing up valuable time for analysis and decision-making.

Let’s dive into three ways that AI for higher education Finance offers significant value:

  1. Streamlining financial operations
  2. Empowering data-driven insights
  3. Enhancing budgeting and planning

1. Streamlining Financial Operations

An increasing concern for many institutions is simplifying processes and reducing costs.  In fact, EDUCAUSE highlights the need for institutions to take significant steps forward to reinvent university operations and greatly lower costs as a top area of focus.  So how can institutions make substantial improvements?

AI is one impactful way to simplify and standardize processes, data and technologies.

Gone are the days of manual data entry, cumbersome consolidation processes and tedious spreadsheet analysis.  Instead, AI’s ability to automate routine financial tasks and process vast amounts of data allows for the identification of patterns and anomalous data points.  This ability facilitates exception handling for Finance processes, such as planning, maintaining data quality and reporting.

As a result, Finance teams can streamline operations and focus on exceptions – rather than sifting through entire datasets.  The approach ultimately saves valuable time and enhances the efficiency of financial processes.

2. Empowering Data-Driven Insights

Another top priority for higher education leaders is improving data-driven insights and analytics throughout the institution.  According to a Chronicle of Higher Education survey, 97% of college administrators believe that higher education needs to better use data and analytics to make strategic decisions.  

Colleges and universities generate vast amounts of information – including Finance, HR and Student data – but struggle to leverage it.  By helping institutions leverage that information, AI empowers Finance teams (see Figure 1).  An AI platform can bring together data and help teams gain new insights into Finance and Student outlooks.

Figure 1:  OneStream AI-Powered Dashboard

AI algorithms can analyze an institution’s data in real time, uncovering valuable insights and trends that inform strategic decision-making.  With predictive analytics that forecast future financial scenarios, Finance teams can proactively plan and allocate resources effectively to create optimized student outcomes.  

Harnessing the power of data-driven insights thus empowers Finance teams to make informed decisions that optimize financial performance and support institutional goals.

3. Enhancing Budgeting and Planning

AI can also provide significant enhancements to the tedious planning processes at universities and colleges.  Traditionally, Finance planning methods often rely on historical data and assumptions, leading to inaccuracies and limited predictive capabilities.

AI instead enables Finance teams to move beyond historical reporting and embrace machine learning (ML).  By leveraging ML algorithms, Finance can analyze historical financial data alongside external factors, such as market trends, student behavior and economic indicators.

These algorithms can identify hidden patterns, uncover non-linear relationships and generate more accurate forecasts.  In essence, those trends then help institutions make informed financial decisions (see Figure 2).

For example, AI can analyze market demand, retention rates, student preferences and academic performance to model recommended tuition and fee pricing adjustments.  Those capabilities maximize revenue while maintaining affordability and competitiveness. As a result, Finance can better support student needs, optimize resource allocation and mitigate financial risks.

Figure 2:  OneStream Higher Education Tuition and Enrollment Dashboard

How Should Higher-Ed Finance Leaders Look to Incorporate AI?

With operational changes on the horizon, higher education leaders must strategically plan their next steps for AI integration.  The successful implementation of AI for higher education Finance requires careful planning and investment in infrastructure and training.  But when Finance employs the right practical AI strategy and commitment to innovation, AI has the potential to revolutionize financial operations in higher education.

Ultimately, the right strategy paves the way for more sustainable, efficient and student-centered financial management processes.

Conclusion

AI represents an exciting shift for higher education Finance teams, offering unparalleled opportunities to streamline operations, unlock insights and enhance strategic decision-making.  By embracing AI technologies, Finance teams can navigate the complexities of financial management with agility, resilience and innovation.

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Learn more about how OneStream’s Finance AI uniquely empowers higher education Finance teams to enhance planning, gain new insights and streamline Finance processes.

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Artificial intelligence (AI) is one of the most revolutionary technologies of our time and will clearly be a future focus for government agencies.  In fact, the AI in Government Act was created to promote the responsible use of AI technology.  The act will significantly impact agency operations through new demands on the workforce, information and infrastructure.  With this act, Finance leaders can improve their back-office operations and create high-value outcomes.

What is the AI in Government Act of 2020?

The AI in Government Act was established to support federal agencies to ensure they responsibly approach AI technology.  The act has three primary objectives:

Further, the Office of Management and Budget has proposed a policy (currently undergoing the review of public comment) to create new agency requirements and guidance for AI governance, innovation and risk management.  AI will be an important future endeavor that agency leaders will need to weave into future operational plans and that will impact resources, information and infrastructure.  Leaders can thus expect to make operational changes.

What does the AI in Government Act mean for agencies today?

With operational changes on the horizon, agency leaders must strategically plan their next steps for AI integration.  The act and proposed policy outline key new roles, responsibilities, reporting and requirements that agencies will need to consider when adopting AI business processes.  Agencies should consider taking the following actions when approaching AI:

What are practical AI opportunities for Finance teams?

Finance teams can take advantage of AI technology in a variety of valuable, sensible ways to create better outcomes.

One way is by incorporating AI and machine learning (ML) into back-office operations, which is a growing trend for Finance teams.  Why?  Well, ML can help Finance teams make more accurate and reliable forecasts, automate repetitive tasks, and identify patterns and trends that might otherwise go unnoticed in traditional analysis.  How?

ML makes forecasting easy for Finance teams in three ways: 

1. Enhancing budgeting and planning

Traditional Finance planning methods often rely on historical data and assumptions, leading to inaccuracies and limited predictive capabilities.  ML has transformed this process by incorporating multiple variables and complex data relationships, empowering Finance to make more accurate predictions and projections (see Figure 1).

Figure 1:  Sensible ML Enhanced Financial Forecasting and Planning Dashboard

By leveraging ML algorithms, Finance can analyze historical financial data alongside external factors such as market trends, customer behavior and economic indicators.  These algorithms can identify hidden patterns, uncover non-linear relationships and generate more accurate forecasts.  As a result, Finance can make data-driven decisions, optimize resource allocation and mitigate financial risks.  The biggest benefit?  Being able to better plan for different services, such as taxes, fees, and shipping volumes.

2. Employing scenario modeling and sensitivity analysis

Scenario modeling and strategic simulations have become crucial tools that help agencies understand the potential outcomes of different scenarios and decisions.  Using such evaluations, Finance teams can make strategic choices and develop contingency plans to mitigate risks and best serve the agency mission.

By incorporating AI and ML forecasting into scenario planning, agencies can create more realistic and useful scenarios and identify the best course of action (see Figure 2).

Figure 2:  Scenario Planning Process

3. Improving operational efficiency

ML enhances operational efficiency by automating repetitive tasks, minimizing errors and identifying areas for improvement.  Specifically, Finance can leverage ML algorithms to streamline financial processes such as budget formulation, variance reporting and financial reporting.

For example, ML algorithms can remove the need for tedious, manual processes and allow Finance to easily analyze large volumes of financial data to identify anomalies and flag potential risks in real time.  By automating these processes, Finance can save time, enhance accuracy and focus on value-added activities.

Conclusion

Finance teams today need to make significant gains and improve back-office operations.  With the AI in Government Act and the recent executive order from President Biden on the responsible use of AI, an urgency exists for agencies to leverage AI to be competitive during these dynamic times.  Now, agency leaders have a framework to implement AI technology and improve mission effectiveness with practical AI technology.

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Learn more about how OneStream’s Finance AI uniquely empowers agencies to plan with confidence and best serve their missions at https://www.onestream.com/.

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The Finance function stands as the backbone of strategic decision-making and organizational success.  But with more information being generated than ever before, Finance teams must find ways of unlocking the hidden insights within this deluge of data.  How?  Artificial intelligence (AI) has emerged as a transformative force, offering Finance professionals a powerful tool to streamline processes, identify patterns and unlock valuable insights.  This blog explores AI basics in Finance, setting the stage for a deeper dive into OneStream’s innovative Sensible ML solution.

Understanding AI Basics in Finance

Efficiency Through Automation

AI in Finance is not about replacing human intelligence but enhancing it.  The automation of repetitive and time-consuming tasks allows Finance professionals to focus on more strategic activities.  For instance, AI-powered tools can automate planning, data entry and routine financial reporting – freeing up valuable time for analysis and decision-making.

Machine Learning for Predictive Analytics

AI enables Finance teams to move beyond historical reporting and embrace ML-backed predictive analytics. By analyzing historical data patterns, AI algorithms can more accurately forecast future trends.  Those trends then help organizations make informed financial decisions.  For example, AI can analyze customer behavior, purchase history and market trends to predict the ideal price point for each product or service.  This personalized approach maximizes both revenue and customer satisfaction, paving the way for sustainable growth.

Exception Handling and Anomaly Detection for Streamlined Processes

AI’s ability to process vast amounts of data allows for the identification of patterns and anomalous data points.  This ability facilitates exception handling for Finance processes such as planning, data quality and reporting.  Thus, Finance professionals can streamline operations and focus on exceptions, rather than sifting through entire datasets.  The approach ultimately saves valuable time and enhances the efficiency of financial processes.

hands on a laptop keyboard with Ai and Financial words displayed

As the Office of Finance continues to embrace AI, adopting a practical sensible approach to ML – one that balances automation with transparency and human insight – has become increasingly important.  After all, effective planning is critical for businesses to remain competitive and adapt to changing market conditions.

At OneStream, we call this Sensible ML.

Introducing Sensible ML

OneStream’s Sensible ML (see Figure 1) is a paradigm shift in leveraging AI for Finance professionals.  By seamlessly unifying AI within an enterprise Finance platform, Sensible ML’s purpose-built for FP&A creates forecasts and insights that were previously impossible to do.

Figure 1:  Sensible ML Process Flow

Purpose-Built AI for FP&A in a Unified Platform

By integrating AI, Finance teams can seamlessly leverage AI capabilities without the need for separate tools, systems or teams.  No longer are the days of having data scientists create a forecast without understanding the business value and of the Finance team receiving the output with no understanding of where the numbers originated.

Instead, with purpose-built AI for Finance, FP&A teams are creating ML-backed forecasts on their own and for the entire process – from data ingestion and quality to model building, all the way to utilization and consumption.  Finance professionals can now explain their accurate forecasts with confidence and do so at scale across hundreds or thousands of forecasts. 

Sensible ML also incorporates external factors such as weather or macroeconomic factors to create highly accurate forecasts and utilizes a unique and groundbreaking concept, the Model Arena.  

The Model Arena offers tailored precision by automatically selecting the most performant model for each forecasted line item.  Contrast this approach to the one-size-fits-all approach that apply a single model for all forecasted line items, failing to account for the characteristics of each product-location combination.  Comparatively, the Model Arena approach produces a much higher level of accuracy by accounting for the nuances of different forecasted products by locations.

For example, Polaris, a global leader in powersports whose products have vastly different characteristics, uses Sensible ML to forecasts for specific products and locations with distinct models across the business. Only a unique ML model tailored for their snowmobiles or off-road vehicles can create an accurate sales forecast – optimizing for downstream processes like allocation of resources or maximizing contribution margin.

Polaris products of snowmobile and off-road vehicle

Sensible ML’s Model Arena automatically selects the most accurate ML model for every product-location combination within differing business units.  Ultimately, then, Sensible ML arms Finance professionals with deeper insights into future financial scenarios, enabling better decision-making and strategic planning.

Conclusion

As the Finance function continues to evolve, understanding the basics of AI is crucial for staying competitive and driving organizational success.  OneStream’s Sensible ML empowers Finance professionals with purpose-built AI capabilities, making it a valuable tool in the quest for efficiency, accuracy and strategic decision-making.  By embracing AI, Finance departments can position themselves at the forefront of innovation and contribute to the overall growth and success of their organizations.

Learn More

To learn more about how FP&A teams can learn AI basics in Finance, stay tuned for additional posts from our Sensible ML blog series or download our white paper here.

Download the White Paper

In the dynamic machine learning (ML) landscape, precision is paramount.  However, selecting the right forecasting model can be difficult and time-consuming, often resembling a maze of complex algorithms and intricate performance metrics.  As FP&A teams strive for more accurate data models to enable informed decision-making and optimize strategies, Sensible ML introduces a groundbreaking concept – the Model Arena.  How?  Much like in a sports bracket, models compete head to head, vying to be the most accurate predictor for specific product-location combinations.  In this third post of our AI for FP&A series, let’s delve into this analogy to uncover the inner workings of Sensible ML’s Model Arena.

Miss the first two posts in the series?  Catch up now with post #1 (AI for FP&A Starts with Data Quality) and post #2 (The Secret Ingredients of Machine Learning for FP&A: Features).

The Analogy: Forecasting Models as Sports Teams

Sensible ML orchestrates model competitions that mirror the intensity of a sports bracket.  The objective?  To pinpoint the most accurate models tailored to each individual product-location combination for which Sensible ML is creating a forecast.  This tailoring sets the stage for a groundbreaking approach to ML optimization.

Much like a sports bracket, Sensible ML divides models into a structured competition format.  Each model is a contender, entering the arena with the aim to outperform other models.  The criteria for success?  Producing the most accurate prediction for each target or product-location combination line-item.

Why Is the Model Arena Unique?

While some Corporate Performance Management (CPM) competitors opt for the simplicity of applying a single model across all targets, Sensible ML handpicks individual models for each target, ensuring tailored precision that goes beyond a one-size-fits-all approach. 

This is the real power of AI For FP&A.  To actually do all the hard work so FP&A teams don’t have to.

Polaris, a global leader in powersports whose products have vastly different characteristics, uses Sensible ML to forecasts for specific products and locations with distinct models across the business.

person driving a Polaris snowmobile in snow with "Case Study" as heading

Contrast this with a  one-size-fits-all approach, where reliance on a single  forecasting model would fail to account for significant differences between their boating and snowmobile product categories for example.  Below are some of the differences that various models consider for Polaris:

A single ML model would simply fail to capture the above nuanced factors in the Polaris product categories, leading to inaccurate forecasts, missed opportunities and poor decision-making.  And if that’s the case, that’s a missed opportunity for AI and ML. 

With OneStream’s Sensible ML, the Model Arena is built right into the solution.  That means, with a single click of a button, business planners can automatically get the most accurate model selected for each forecasted line-item.  AI for FP&A at scale! 

Accelerate Model Train Times and Experimentation in Sensible ML’s Model Arena

Model monitoring is also a challenge for traditional ML tools, as model performance begins to degrade once placed into production.  Manual efforts also only further hamper model performance.  For these reasons alone, effective and scalable ML solutions must automatically compare models and contain the following capabilities to create speed to value for Finance and Business analysts:

In other words, users can continuously monitor model health scores and performance over time and auto-retrain in Sensible ML’s Model Arena.  All the model variations in the arena compete against each other in a sports-bracket structure to identify the best-performing models and determine which ones to deploy.  Below, Figure 1 depicts Sensible ML’s Model Arena selecting the most accurate model to predict the sales of menu items for a fictitious organization.

Sensible ML Model Arena

Figure 1:  Sensible ML Model Arena Call Target

In Figure 1, model A (CatBoost) won the sports bracket.  Why?  The model received the lowest error score compared to the rest of the models. Therefore, model A was used to create the forecast number for the call product in the Rochester location. When a different target or product location combination is selected, however, a different ML model wins (see Figure 2).

Figure 2:  Sensible ML Model Arena Pints Target

As shown in Figure 2, when the Pints product for the Rochester location is selected, model B (XGBoost) beat the others to win the sports bracket. Model B was the most accurate model and was therefore used to create the forecast number for Pints in the Rochester location.

Conclusion

In sum, OneStream’s Sensible ML’s Model Arena automates a targeted, personalized approach for FP&A teams that surpasses the limitations of generalized, one-size-fits-all approach. Bottom line – this helps FP&A create value from AI, unlock the true potential of their data and create competitive edge through unparalleled forecasting accuracy and tailored insights.

The future of accurate, personalized AI for FP&A is here – be a part of it with Sensible ML.

Learn More

To learn more about Sensible ML,  stay tuned for additional posts from our Sensible ML blog series.  You can also download our white paper here.

Download the White Paper

The need for software tools that enable finance and business leaders to navigate today’s challenging economic landscape and vast array of enterprise-level risks with predictive insights and agility couldn’t be greater.  This is driving demand for modern, cloud-based financial planning software applications that can replace spreadsheets and legacy planning applications and enable more confident decision-making.  

So it’s timely that the 2023 Gartner® Magic Quadrant for Financial Planning Software1 was recently published, in which OneStream was recognized as a leader for the second year in a row.  Read on to hear the highlights of the report and why OneStream was named a leader.

Assessing the Planning Software Vendor Landscape

In the 2023 MQ for Financial Planning Software, the Gartner analyst team evaluated 16 software vendors based on their ability to execute and completeness of vision, including market understanding, offering strategy, innovation and geographic strategy.  Based on their evaluation, OneStream was recognized as a Leader for the 2nd year in a row in this report. This comes on the heels of OneStream recently being recognized as a Leader in the Gartner Magic Quadrant for Financial Close and Consolidation Solutions2.

Why was OneStream recognized as a Leader in the report?  I suggest reading the entire report to get the full story, but a few strengths highlighted in the report are:

We believe this recognition underscores our continued momentum in the market, innovation we are driving with our AI/ML strategy, and the value that OneStream’s unified platform delivers to customers, enabling agile financial and operational planning and reporting processes. 

Organizations that have adopted OneStream for FP&A are streamlining their budgeting, planning and forecasting processes by an average of 58%, aligning financial and operational planning across the enterprise, and are using our built-in Analytic Services to report and analyze daily and weekly financial and operational signals and trends by integrating large volumes of transactional data.  A growing number of customers are leveraging our Sensible ML solution to deliver faster and more accurate demand and revenue forecasts to support more confident decision-making. Here are a few examples:

Polaris – leveraging Sensible ML, Polaris is reducing forecasting cycle times and improving the accuracy of their demand forecasts. The Finance team also has more insights into the key forecast drivers, helping drive more informed decision-making.

Autoliv – leveraging Sensible ML’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, Sensible ML can produce more accurate and more frequent forecasts at scale and at a fraction of the time and cost.

AI-Powered Planning Goes Mainstream

In the report, Gartner highlighted several key market trends that are driving rapid growth in the FP&A software market.  This includes the following:

And as mentioned in prior reports, the transition from on-premises to cloud-based financial planning software reflects a broader trend toward SaaS offerings, facilitating faster implementation, improved ease of use and reduced dependence on IT staff for management. Additionally, these solutions provide adaptability and collaboration for tighter operational and financial performance feedback loops, extending their adoption across the enterprise.

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To learn more about the key market trends, and how OneStream compares to the other vendors in the financial planning software market, download the Gartner MQ report and contact OneStream if your organization is ready to reduce reliance on spreadsheets and legacy applications and maximize business impact!  

Download the Report

Sources:

1Gartner Magic Quadrant for Financial Planning Software, Regina Crowder, Matthew Mowrey, Vaughan Archer 5 December 2023

2Gartner Magic Quadrant for Financial Close and Consolidation Solutions, Nisha Bhandare, Permjeet Gale, Jeffrin Francis, Renata Viana, 27 November 2023

GARTNER is a registered trademark and service mark of Gartner and Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Introduction

Have you ever wondered what makes a cake delicious?  At the core, that deliciousness comes from the combination of ingredients and the process of baking.  Now think about the world of machine learning (ML) where the “ingredients” are characterized as “features.”  To better understand this notion, let’s delve into our kitchen (or the world of ML) with a simple analogy for FP&A (Financial Planning & Analysis) teams to consider.

Imagine you’re crafting a cake.  The recipe (or model in ML) you’re following demands certain ingredients:  flour, sugar, eggs and maybe some cocoa for a chocolate spin.  Each of these ingredients, with their unique taste and texture, plays a role in determining the final taste, appearance and consistency of the cake.

cake with macarons on top

The Cake:  The outcome or prediction.

The Recipe:  The machine learning model.

The Ingredients:  The features.

The absence of sugar might result in a bland cake.  In the same way, missing or poorly chosen ML features can weaken a FP&A) model’s predictive accuracy.  The right features ensure that won’t happen, and in this second post in our AI for FP&A series, we cover the key ML features that can be your secret ingredient to achieving effective planning and analysis.

Key Ingredients: Features

Features are the building blocks of ML. These individual properties or characteristics are extracted from data and used as input in ML.  In the context of time series forecasting, the term “feature” usually refers to attributes derived from time series data that can be used to make future predictions.  Well-chosen features can help machine learning models do the following:

Time series data is often complex and noisy, and machine learning models can sometimes struggle to learn from such data without the right features.

Sensible ML: Taking Baking to the Next Level with Easy-to-Follow Reminders

Let’s be honest:  Baking isn’t always easy.  Sometimes we might forget an ingredient, miscalculate the proportions or simply not know which ingredients will work best for a particular type of cake.  Similarly, in ML, selecting and generating the right features is often a cumbersome, time-consuming process.

But it doesn’t have to be.  Keep the following reminders top of mind to take your baking (i.e., ML modeling) to the next level.

Reminder #1: Automate with Sensible ML’s groundbreaking Auto AI capabilities.

Sensible ML understands the challenges associated with features in the ML process and offers auto ML  to simplify the process.

Sensible ML is a machine learning solution that automates many of the tasks involved in building and deploying ML models.  One of the key features of Sensible ML is its automated feature generation and selection capabilities.

In fact, Sensible ML can automatically generate thousands of features from your data, even if your data is complex or unstructured.  For example, assume US housing starts or housing permit activity is a “feature” to help forecast a manufacturer’s door sales.  Sensible ML will automatically calculate whether a 1-month, 2-month or longer lead or lag helps improve forecast (door sales) accuracy.  

After generating features from your data, Sensible ML can automatically select the most important features for your machine learning model.  The benefit?  Both the accuracy and efficiency of your model are improved.

Reminder #2: Accelerate insights with the Low-Code/No-Code (LC/NC) Sensible ML Feature Library

Without Sensible ML’s LC/NC Feature Library, Finance teams and data scientists would be forced to identify external data sources on their own, manage each integration and spend time cleaning data.  And all that would have to be done before doing all the hard work to gauge whether the contributed external data is useful for forecasting.

Alternatively, Sensible ML accelerates time to value, reduces technical overhead and increases productivity with few, if any, programming skills required.  This functionality/feature has saved our customers from needing 2 weeks of a dedicated data scientist’s time.

With Sensible ML’s built-in LC/NC capabilities, Finance teams and analysts can quickly build time-series ML models ready for consumption across the organization – and do it in a way everyone can understand and use. 

Sensible ML’s Feature Library thus enables Finance and Operations teams to enrich data using predefined external sources such as the Consumer Price Index, weather or gas prices – without lengthy complicated code only data scientists would understand (see Figure 1).  Sensible ML will then do all the “hard work” to identify which of these external sources/variables are relevant to contribute to forecast model performance – and to what extent.

OneStream's Sensible ML solution on the Feature Library step.

Figure 1:  Sensible ML Feature Library

Reminder #3: Use dashboards to visualize forecasts and assess feature impact.

Automatically bringing in features for a more accurate prediction is great. But if there’s no transparency into how the ML model got to the numbers, then what’s the point?  Business analysts want to take a forecasted number and explain how they arrived at that prediction, so the organization can develop a strategy going forward.  For example, if a sales promotion at a particular time of the year had a large positive impact on sales, this information is valuable for the business.

To that end, Sensible ML comes with several dashboards highlighting feature influence across the portfolio or on individual predictions for each product-location combination (see Figure 2).  The Feature Impact dashboard below shows that the “Week of the year” feature has a relative importance of 11.63% to the prediction amount, which is relatively high compared to the rest of the features.

Figure 2:  Sensible ML Feature Impact

To take transparency a step further, Sensible ML also shows for any given forecast datapoint what features are driving the forecast higher or lower (see Figure 3).

Figure 3:  Sensible ML Prediction Explanations (Tug of War)

Forecast accuracy is the de facto outcome of Sensible ML, although deep insights are the much richer benefit our customers are enjoying.  Customers can now better understand the specific impacts that external and promotional factors had on product demand.  In turn, this deep insight enables more strategic decision-making that results in material impacts to an organization’s financials.

Conclusion

In essence, ML is a lot like baking in that both are a combination of art and science.  The right features (or ingredients) can make all the difference.  And with Sensible ML’s Auto AI capabilities, the intricate process of building an ML forecast (or baking) is streamlined, ensuring your “cake” is always the talk of the town!

Learn More

To learn more about the end-to-end flow of data and how FP&A interacts with data, stay tuned for additional posts from our Sensible ML for FP&A blog series.  You can also download our white paper here.

Download The White Paper

Introduction

In the bustling world of artificial intelligence (AI) one saying perfectly encapsulates the essence of the work – “garbage in, garbage out.”  Why?  This mantra underscores a truth often underplayed amid the excitement of emerging technologies and algorithms used across FP&A teams:  data quality is a decisive factor for the success or failure of any machine learning (ML) project.

Big data, often referred to as the “new oil,” fuels the sophisticated ML engines that drive decision-making across industries and processes.  But just as a real engine cannot run effectively on substandard fuel, ML models trained on poor-quality data will undoubtedly produce inferior results.  In other words, for AI in FP&A processes to prove successful, data quality is essential.  Read on for more in our first post from our AI for FP&A series.    

Understanding the Importance of Data Quality

Data scientists spend 50%-80% of their time collecting, cleaning and preparing data before it can be used to create valuable insights.  This time investment is a testament to why “garbage in, garbage out” isn’t a warning but a rule to live by in the realm of AI and ML.  Particularly for FP&A, that rule demonstrates why skimping on data quality can lead to inaccurate forecasts, biased results and a loss of trust in ground-breaking AI and ML systems.

Unleashing Data with Sensible ML

In the current digital age, businesses have unprecedented access to vast quantities of data.  The data lays a crucial groundwork for making significant business decisions.  But to ensure the data available to employees is reliable, visible, secure and scalable, companies must make substantial investments in data management solutions.  Why?  Substandard data can trigger catastrophic outcomes that could cost millions. If data used to train ML models is incomplete or inaccurate, this could lead to inaccurate predictions with consequential business decisions that lead to loss of revenue. For example, if bad data causes incorrect forecasts about demand, this could lead to the company producing too little or too much of the product, resulting in lost sales, excess inventory costs, or rush shipments and overtime labor.

Contrary to “most” predictive analytics forecasting methods, which generate forecasts based on historical results and statistics, OneStream’s Sensible ML incorporates true business insights.  These insights include factors such as events, pricing, competitive data and weather – all of which contribute to more accurate and robust forecasting (see Figure 1).

Sensible ML Data Pipeline Diagram

Figure 1:  Sensible ML Process Flow

Managing Data End-to-End

FP&A and operational data plays a pivotal role in the success of any machine learning forecasting scenario.  However, to create purpose-built data flows that can efficiently scale and provide exceptional user experiences, advanced solutions – such as Sensible ML – are necessary.  Sensible ML can expedite and automate crucial decisions throughout the data lifecycle, from the data source to consumption (see Figure 2). 

Advanced data flow capabilities encourage the following:

Figure 2:  Sensible ML Pipeline

Sensible ML leverages OneStream’s built-in data management capabilities to ingest source data and business intuition.  How?  Built-in connectors automatically retrieve external data such as weather, interest rates and other macroeconomic indicators that can be used in the model-building process.  While Sensible ML then automatically tests the external data sources without any user intervention, users ultimately decide which data to use.

Using Sensible ML’s Built-in Data Quality

Sensible ML can bring in detailed operational data from any source, including point-of-sale (POS) systems, data warehouses (DW), Enterprise Resource Planning (ERP) systems and multi-dimensional cube data.

The data quality capabilities in Sensible ML provide some of the most robust capabilities available in the FP&A market.  Those capabilities include pre-and post-data loading validations and confirmations, full audit, and full flexibility in data manipulation.  In addition, Sensible ML’s data management monitors the way data curation typically behaves and then sends alerts when anomalies occur (see figure 3).  Here are a few examples:

Sensible ML Data Quality Page

Figure 3:  Sensible ML’s Auto ML data cleansing

In Sensible ML, an intuitive interface offers drop downs with the most performant and effective data cleansing methods, allowing business users of any skillset to run the entire data pipeline from start to finish.

Conclusion

Ensuring data quality is not just a box to be checked in the machine learning journey.  Rather, data quality is the foundation upon which the entire edifice stands.  As the boundaries of what’s possible with ML are continually pushed, the age-old “garbage in, garbage out” adage will still apply.  Businesses must thus strive to give data quality the attention it rightfully deserves.  After all, the future of machine learning is not just about more complex algorithms or faster computation.  Producing accurate, fair and reliable models – ones built based on high-quality data – are also essential to effective ML.

Learn More

To learn more about the end-to-end flow of data and how FP&A interacts with data, stay tuned for additional posts from our Sensible ML blog series.  You can also download our white paper here.

Download the White Paper

In today’s digital era, Financial Planning & Analysis (FP&A) teams are inundated with vast amounts of data.  This data holds invaluable insights that, if harnessed effectively, drive significant improvements in organizational performance.  In that sense, machine learning (ML)-enabled analytics is an emerging powerful tool that helps organizations make sense of data, identify patterns and make informed decisions to steer performance in the right direction.  This blog post explores the key benefits of ML-enabled analytics and how it’s revolutionizing organizational performance management.

Specifically, we explore the transformative potential of ML-enabled analytics and how FP&A teams can harness its power to drive their organizations’ financial success.

The Power of ML-Enabled Analytics

Incorporating ML-enabled analytics into the FP&A toolkit is no longer a luxury but a necessity in today’s data-driven world.  By leveraging ML algorithms, FP&A teams can enhance financial forecasting, improve operational efficiency, optimize pricing strategies and mitigate financial risks.  The ability to leverage the resulting data-driven insights empowers CFOs to make informed decisions, drive financial performance and deliver sustainable growth.

Those benefits emphasize how machine learning and advanced analytics have emerged as powerful tools for FP&A teams, offering deeper insights into financial data and enabling predictive and prescriptive analytics.  By leveraging ML algorithms, FP&A teams can analyze vast amounts of data to uncover patterns, detect anomalies and generate accurate forecasts. ML-enabled analytics ultimately helps FP&A teams in the following five ways.

1. Enhancing Financial Forecasting and Planning

One of the primary responsibilities of FP&A is to develop robust financial forecasts and plans.  Traditional forecasting methods employed by FP&A often rely on historical data and assumptions, leading to inaccuracies and limited predictive capabilities.  ML-enabled analytics revolutionize this process by incorporating multiple variables and complex data relationships, empowering FP&A to make accurate predictions and projections (see Figure 1).

Figure 1:  Sensible ML Enhanced Financial Forecasting and Planning Overview

By leveraging ML algorithms, FP&A can analyze historical financial data alongside external factors such as market trends, customer behavior and economic indicators.  These algorithms can identify hidden patterns, uncover non-linear relationships and generate more accurate forecasts.  As a result, FP&A can make data-driven decisions, optimize resource allocation and mitigate financial risks more effectively.

2. Employing Scenario Modeling and Sensitivity Analysis

ML-enabled analytics can generate scenario models and perform sensitivity analysis, allowing FP&A to evaluate how various business decisions and external factors can impact financial performance.  Using such evaluations, FP&A teams can make strategic choices and develop contingency plans to mitigate risks and capitalize on opportunities.

Advances in AI and ML have especially enhanced scenario planning by allowing Finance to make more accurate and reliable forecasts.  With AI and ML, FP&A teams can analyze vast amounts of data and identify complex patterns and relationships between different factors.  Such analysis can enable organizations to develop more sophisticated and accurate forecasts that reflect current market conditions and emerging trends.

By incorporating AI and ML forecasting into scenario planning, businesses can therefore create more realistic and useful scenarios, helping organizations make better-informed decisions and stay ahead of the curve (see Figure 2).

Figure 2:  Scenario Planning Process

3. Improving Operational Efficiency

ML-enabled analytics can significantly enhance operational efficiency via automating repetitive tasks, minimizing errors and identifying areas for improvement. More specifically, FP&A can leverage ML algorithms to streamline financial processes such as budgeting, variance analysis and financial reporting.

For example, ML algorithms can analyze large volumes of financial data to identify anomalies, detect fraud and flag potential risks in real time.  By automating these processes, FP&A can save valuable time, enhance accuracy and focus on value-added activities (e.g., strategic planning and analysis).

4. Optimizing Pricing and Revenue Management

Pricing and revenue management are critical aspects of financial performance, especially for businesses operating in highly competitive markets.  ML-enabled analytics can help FP&A optimize pricing strategies and revenue generation.

By analyzing market dynamics, customer behavior, competitor pricing and historical sales data, ML algorithms can identify optimal pricing levels, demand patterns and customer segments.  FP&A can then leverage these insights to develop dynamic pricing models, implement personalized pricing strategies and maximize revenue – all while ensuring competitiveness.

5. Mitigating Financial Risks

In an uncertain business landscape, FP&A must proactively identify and mitigate financial risks.  ML-enabled analytics provide powerful risk management tools, empowering FP&A teams to identify potential risks, predict outcomes and take preventive measures (see Figure 3).

Figure 3:  Sensible ML Workspace to Mitigate Risks to Performance

By analyzing historical and real-time data, ML algorithms can identify early warning signals for financial risks, such as liquidity issues, credit defaults and market volatility.  FP&A can then leverage these insights to develop risk mitigation strategies, establish contingency plans and make informed decisions to protect the organization’s financial health.

Sensible ML Makes Forecasting Easy

Sensible ML makes forecasting easy by breaking down the barriers that have traditionally held back Finance and Operations teams and others from embracing ML within core planning processes.  While ML has powerful potential to help scale work like never before, organizations face several challenges when using traditional machine learning. Figure 4 depicts some of the biggest traditional ML challenges.

Figure 4:  Sensible ML Solves for Traditional ML Challenges

Sensible Use Cases Foster Success

Sensible ML enables organizations to more quickly and accurately foster success with the following use cases (see Figure 5):

Figure 5:  Sensible ML Use Case Matrix

Conclusion

As the role of FP&A continues to evolve, embracing ML-enabled analytics becomes crucial for steering performance and driving organizational success.  FP&A can leverage the power of ML algorithms to extract valuable insights from vast amounts of financial data, enhance forecasting accuracy, proactively identify risks, optimize costs and make informed decisions.  In those ways, the integration of ML into Finance functions enables FP&A to become a strategic partner to business leaders, providing the organization with the tools to navigate complex challenges, drive growth and create long-term value for organizations.

Learn More

To learn more about how FP&A teams are moving beyond the AI hype, stay tuned for additional posts from our Sensible ML blog series or download our white paper here.

Download the White Paper

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

Conclusion

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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Changing rooted behaviors is one of the hardest jobs for leaders. And what about trying to bring innovative technologies and novel approaches to accomplishing tasks tied to those rooted behaviors? Well, that’s a lot like pulling teeth. But those changes pay off, for they position the organization to reach higher levels of value.

This blog post introduces machine learning for demand planning by looking at how car safety relates to demand planning, and exploring the obstacles organizations might experience with adoption and how to overcome them.

How Car Safety Relates to Demand Planning

When thinking about car safety, most people likely picture one or more of the images below:

Volvo first comes to mind first. And indeed, Volvo pushed for standardizing the 3-point seatbelt in the car industry, so the company can proudly take credit for saving millions of lives [1] since the 1970s. But adoption didn’t come easily. Why not? Well, it’s simple. Safety belts meant a radical change in people’s habits with a difficult trade off: losing comfort for safety in the supposed event of an unlikely accident [2]. Using safety belts meant a behavioral paradigm shift for drivers and passengers alike that even today faces resistance from some motorists. And to make true change possible across the industry, Volvo had to open up the patent for safety belts to competitors to accelerate adoption.

Similarly, just like drivers and passengers with the introduction of the seat belt, demand planners are going through a behavioral paradigm shift with the introduction of machine learning (ML). Why? Because old habits die hard! Let’s dive into it.

Demand planning work is usually a manual activity grounded in a low-accuracy system-generated baseline. To get the accuracy right, several external and internal source inputs enrich and adjust this baseline multiple times. Spreadsheet wrangling, reconciliation and error are thus inevitable fallouts of this approach. Yet many planners prefer it. Why? Because they feel comfortable with it. That also means they refrain from learning new technologies and methods despite – as is the case with ML – the game-changing benefits.  Using machine learning for demand planning drastically improves accuracy and exponentially increases the number of forecasts run.

Fortunately, not every organization is resistant to changing the status quo. Some companies are trailblazing the adoption of machine learning to improve forecasting accuracy in demand planning. One such company is Autoliv, a tier-1 automotive supplier of safety components for major carmakers in the world.

Better Demand Planning Fuels Profitability

Supplier relationships in the automotive industry are based on a pull system that gives car manufacturers strong leverage on pricing. Consequently, margins can be razor thin for suppliers, and the risk of falling into loss is high. This axiom is valid in other industries as well, including in transportation, retail, wholesale, consumer products, and more.

While margins can be improved in many ways, a robust approach is needed to better understand and plan demand effectively. Why? Because an organization that appreciates the business drivers that shape future demand can better draw sales projections. Additionally, the organization can better adjust inventory levels, avoiding stock outs and breaches of service levels.

Autoliv offers a good real-world example of putting this robust approach in action. How? The company successfully embarked on a transformation journey to have a single view of profitability across Sales, the Value Chain and Finance. Autoliv also knows that – in the automotive industry – understanding demand is key to protecting and growing profit margins. Accordingly, the company is exploring the use of Sensible Machine Learning to improve demand planning. You can read the Autoliv case study here.

Dodging Obstacles Along the Way

Many organizations are using artificial intelligence (AI) or machine learning (ML) in the business in one way or another. One of the most used approaches is to build a data lake and apply ML algorithms. However, this approach does not always work well for planning use cases. When dealing with ML for demand planning, organizations may encounter the following challenges that hinder adoption:

  1. High complexity, low generalization. Many purpose-built applications in the market are complex. They often require additional programming skills and the transfer of sensitive data outside the module. Conversely, in-house applications are highly customized to only serve a specific use case. As a result, when business conditions change (and they change a lot!), in-house applications must be re-programmed.
  2. Lack of talent, lack of focus. Data scientists have one of the most sought-after skill set in the job market, in any industry. So they’re not only expensive profiles, but also ones that are hard to find and retain. Often, resident data scientists work on a wide variety of use cases. The problem with such positions is that the data scientists lack the business context needed to build solutions, without tedious interaction with functional roles.
  3. The black-box effect. Demand planners often perceive ML planning solutions as a black box. Planners get the results from the algorithm but know little about how the solution handles the data – perceiving lack of transparency that ultimately diminishes trust in the results.

Not to mention, resistance to change can be high, and only 13% of standard ML projects make it into production. What’s the point of producing an ML forecast that no one uses? [3] Luckily, there is a way to deal with the obstacles along the way.

Keeping the Eyes on the Road

Having the expected benefits clear from the start is key when considering machine learning for demand planning. Does it need to underline new patterns? Should it address variability? Can it produce a high volume of forecasts at speed? The ultimate litmus test is that the solution delivers more forecasting accuracy and that planners are trusting it.

Many organizations hold a vast amount of data, but it is pocketed in different systems and databases. When a lot of effort goes into preparing the data for the ML engine, organizations may lose sight on what’s important. A solution that can ingest volume and disparate datasets is therefore key for demand planning use cases. For that reason, the following key attributes must be considered when looking for an ML solution for demand planning:

Beyond considering the necessity of the above attributes, organizations must also stay focused on the business outcomes they expect.

It Is Not the Destination But the Journey

When an organization has clarity on business outcomes, ML and other technologies become an enabler to accomplishing those outcomes. This clarity on goals helps organizations decide between costly and lengthy home-built ML solutions and market-leading planning solutions with built-in ML services. The latter will help better break old habits, enable enterprise-wide adoption and accelerate the time to value.

Learn More

Ready to find out how to break away from old habits on demand planning?

Discover OneStream’s Sensible ML for Demand Planning, the only solution that helps demand planners and finance teams embrace machine learning with trust and full transparency into the data and results.

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[1] CDC Road Traffic Injuries and Deaths—A Global Problem
[2] Read Volvo’s amazing story here
[3] VentureBeat. Why do 87% of data science projects never make it into production?

Just when some light seemingly appeared at the end of the tunnel as the global pandemic waned, 2022 proved to be a challenging year for both individuals and corporations thanks to a host of other reasons.  Geo-political instability due to the war in Ukraine led the headlines for most of the year.  But higher fuel prices, widespread inflation, continued supply chain bottlenecks, rising interest rates and falling financial markets all played a role, too.  With planning and budgeting season here, what assumptions are CFOs and Finance executives making about what lies ahead in 2023?  And how are those assumptions impacting corporate planning?

For the past few years, OneStream Software has sponsored Hanover Research surveys of Finance executives to better understand how they’re helping their organizations navigate the complexities of today’s economic landscape.  Hanover Research recently surveyed over 650 financial decision-makers in North America, as well as EMEA, to understand their expectations for 2023. 

The survey asked about decision-maker’s expectations regarding inflation, a potential recession, supply chain disruptions, talent management, Environmental, Social & Governance (ESG) and Diversity, Equity & Inclusion (DEI) initiatives, and technology investments.  

Here’s a summary of what we learned from the 2022 Hanover Research Finance Decision-Makers survey.

Key Objectives of the Survey

Recommendations for Financial Decision-Makers

The takeaways from the report emphasized renewed enthusiasm towards machine learning (ML) and its impact on organizational performance.  Increased economic uncertainty has emergedin recent months (e.g., inflation, tax reform, supply chain shortages, the lingering effects of the COVID-19 pandemic and a potential recession).  Amid that environment, businesses continue to reallocate spending within their businesses.

Key Findings Every CFO Should Know

Stormy Economic Conditions Ahead

With inflation continuing to plague both individuals and enterprises, price increases are the number-one way businesses have addressed inflation (56%), followed by slowed hiring or reduced specific operational costs (47%) (see Figure 1).

Almost half of businesses have slowed hiring or reduced specific operational costs, another significant increase from a year ago.

Figure 1:  Preparation for changing inflation rates

When asked how long they expect inflation to persist, three-quarters of financial leaders do not expect inflation to slow down until mid-2023 or later.  This group includes one-fifth (20%) who do not expect inflation to slow down until 2024 or later, representing a shifting timeline.  Last fall, half (54%) believed inflation would stabilize by the end of 2022, and earlier in 2023, under half (47%) expected inflation to slow in mid-2023 or later.

Investment in Cloud Planning and Analysis Tools Increasing

Over two-thirds of businesses regularly use cloud-based planning and reporting, and one in five (20%) report regularly using machine learning within their departments.  Looking forward, over half of financial leaders predict investing more in cloud-based solutions.

Meanwhile, only one-third of companies (37%) predict investing more in machine learning, which is significantly fewer companies than predicted both last fall and earlier in 2023 (see Figure 2).

Figure 2:  Data Analysis Tools Investment Changes

When asked about the top use cases for artificial intelligence or machine learning, financial leaders surprisingly identified financial reporting as the top opportunity in the fall 2022 survey.  The financial reporting use case was followed by sales/revenue forecasting (41%) and demand planning (39%) as the second and third largest opportunities for organizations, respectively (see Figure 3).

Figure 3:  AI/ML Learning Opportunities

DEI and ESG Initiatives Still in Focus

With ESG reporting guidelines converging and new mandatory disclosure requirements being proposed by the US SEC and regulators in other countries, investments in ESG and DEI remain a priority.  Half of the organizations surveyed expect to invest more in DEI and ESG goals and initiatives in 2023 compared to 2022 investments.  This change is a significant drop compared to expectations from earlier in 2023 (65% in DEI and 60% in ESG).  Still, over a third of enterprises expect to maintain their 2022 investment levels in both DEI and ESG in 2023 (38% and 39%, respectively) (see Figure 4).

Figure 4:  DEI & ESG Investment Plans

When asked about their plans to prepare for changing ESG Reporting requirements, nearly half of the financial executives surveyed have started or plan to start forming an internal ESG/Sustainability team to define policies and disclosures.  A similar proportion (41%) will begin (or have already begun) implementing new ESG/sustainability policies.  Compared to earlier in 2022, fewer are planning to invest in software to support ESG data collection and reporting.  Among those who currently don’t have a plan in place, half (50%) indicate they may implement a plan if ESG reporting mandates impact their organizations (see Figure 5).

Figure 5:  Preparation for ESG Change

Conclusion

The results of the recent Financial Decision-Makers Survey highlight the ongoing business challenges CFOs and Finance leaders face as they look to drive performance ahead in 2023.  Inflation, higher interest rates, supply chain bottlenecks and recession are here to stay, and most Finance executives expect those challenges to continue into 2023

The good news is that today’s cloud-based analytical software technologies are seeing increased adoption and proving their worth in helping Finance teams become more efficient, plan and navigate a volatile economic landscape, and increase their agility to respond.  Artificial intelligence and machine learning adoption still lag behind mainstream planning and predictive analytics tools.  But as these capabilities are embedded into modern planning, reporting and analytical software applications, Finance adoption is poised to expand rapidly. 

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This report delves into the latest trends in modern planning, reporting and analytics software applications, from predictive analytics to artificial intelligence and machine learning.  And with expert insights from leading CFOs, you’ll gain valuable knowledge and actionable strategies to stay ahead of the game with more effective planning and budgeting. 

To learn more,– download our CFO Executive Outlook Report today to gain a competitive edge in 2023.

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