Leveraging Forecasting with Smart Predict in SAP Business Data Cloud

Smart planning in SAP Analytics Cloud refers to the intelligent, predictive-enabled planning capabilities that combine traditional business planning with machine learning, artificial intelligence, and advanced analytics to create more accurate, automated, and data-driven planning processes. It becomes significantly more powerful within the SAP Business Data Cloud ecosystem, leveraging unified data products, seamless integration with SAP Datasphere, and enhanced AI capabilities through SAP Databricks to deliver intelligent, predictive-enabled planning processes. It can be used for Revenue projections, cost forecasting and budget planning with predictive accuracy.

The three core components of smart planning are:

• Smart Predict: A predictive forecasting tool that uses machine learning, statistical analysis, and artificial intelligence to define probability scenarios based on historical company data.
• Predictive Planning: Advanced forecasting capabilities that leverage time series models and predictive analytics for planning data
• Automated Workflows: System-driven task generation and workflow automation based on driving dimensions

Through these components, smart planning in the SAP Analytics Cloud offers three key capabilities:

• Machine Learning-Powered Forecasting: Utilizes AI algorithms to analyze historical data patterns and generate intelligent forecasts for planning models
• Predictive Scenarios: Creates multiple forecast scenarios to support decision-making by comparing different versions of data
• Automated Data Processing: Uses Actions and Triggers to perform complex planning calculations automatically or semi-automatically

While there are many smart features in SAP Analytics Cloud, Selina decided to focus on Smart Predict to help Natalie.

Selina quickly realized that to help you make better business decisions, SAP has integrated automated predictive capabilities into SAP Analytics Cloud.

With Smart Predict, you do not need to be a data scientist to access predictive scenarios and perform financial forecasts. Through it's simple visual user experience it makes machine learning algorithms accessible to everyone, and predicts potential outcomes and forecasts with the push of a button. Smart Predicts offers:

• Simple visual experience
• Typical guided workflow for classification and regression models
• No technical terminology
• No coding
• Business oriented questions

A predictive scenario is a preconfigured workspace that finance teams can use to create predictive models and reports to address business questions requiring the prediction of future financial events or trends. You choose the one that is relevant to the type of predictive insights that you are looking to create.

Create sorted lists based on expected probability (classification models), estimates about future or unknown values (regression models), or forecasts (time series forecasting models). These predictive models allow you to generate future, hidden, or unknown data.

Smart Predict can be used on various acquired sources, to help you to answer precise questions. There are three main predictive techniques that cover business use cases and are covered in this use case:

• Purpose: Finance teams use classification models to assess financial risks and categorize financial events based on probability outcomes. These models help finance professionals make informed decisions about resource allocation and risk management strategies.
• Classification models address questions such as "Which customers are most likely to default on payments?" and "What is the probability of budget overruns for specific projects?"

• Purpose: Regression models in finance focus on predicting numeric financial values and identifying key influencers that drive financial performance. These models enable finance teams to estimate future financial outcomes based on various business factors.
• Regression scenarios answer questions like "What will be the revenue generated by each customer segment in the next six months?" and "How will operational changes impact our cost structure?"

• Purpose: Time series models analyze financial data over time to identify trends, seasonality, and cyclical patterns that influence future financial performance. These models are essential for financial planning and budgeting processes.
• Time series forecasting addresses questions such as "What will be our monthly cash flow for the next 12 months?" and "How will seasonal variations affect our quarterly revenue?"

Having learned about different Predictive Scenarios and Models, Selina decides that to help Natalie forecasting the gross sales, a Time Series Model would fit best.

Configuring the predictive scenario is straightforward with Smart Predict. You don't need to be a machine learning expert or a data scientist. The basic workflow is as follows:

1. Select the planning model to be augmented.
2. Select the version of the planning model to learn from. Smart Predict should learn from actual data.
3. Select the measure to be predicted. In this scenario, you want to predict the gross revenue measure.
4. Select the number of forecast periods to be predicted. The planning granularity is monthly. If you would like predictions for 12 months, request 12 forecasts.
5. Select the entities. For example, you can share the predictions and insights with stakeholders responsible for different products in different countries/regions.

You can further refine your predictive forecasts by using the Influencers setting that allows you to select which influencer variables to include before training your predictive planning model.

With Smart Predict, you can go one step further and generate forecasts for each entity to get accurate, business-oriented insights, not just raw forecasts. You can use your planning model directly as the data source for the predictive scenario; no need to extract data into a dataset

Smart Predict uses the data available in your planning model to create and train a predictive scenario. You can then analyze predictive forecast accuracy across the combined dimension values and understand signal breakdown in detail. Once you're satisfied with the accuracy of your predictive scenario, you can generate the predictive forecasts: they save back directly in the private version of your planning model. It’s then easy for you to augment your story with actual and predictive forecasts.

When you create a predictive scenario, you initially specify a training data source, a target or signal variable, and then define additional training settings. Training is a process that uses SAP automated machine learning algorithms to find relationships or patterns of behavior in the data source. You can apply the result to a new data source to predict with a probability what could be the value of the target or signal for each element of the data source.

When using a planning model, the input version must be a public version, not in edit mode, or a private version. You have at least read access to it.

A predictive model produces performance indicators and reports as a result of a successful training.

Here's a summary of the different components that you can use to debrief your results so you can verify the accuracy of your predictive model:

• Horizon-Wide MAPE: Is the main performance indicator high enough to consider my predictive model robust and accurate? Check the quality of your model performance over the Horizon-Wide MAPE. It evaluates the "error" when using the predictive model to estimate future signal values, with zero indicating a perfect model. The lower the Horizon-Wide MAPE, the better your predictive model performance. For more information, refer to Horizon-Wide MAPE
• Signal Analysis: What forecasts are provided by the predictive model? Have a close look at the signal and forecasts. Signals show trends, cycles, and fluctuations in the signal, each with a description. Check if there are outliers in the forecasts and detect anomalies on the signal. For more information, refer to The Predictive Forecasts, The Signal Outliers and The Signal Anomalies
• Signal vs. Forecast: How accurate is my predictive model? Use the Signal vs. Forecast graph to visualize the forecast (predicted) and signal (actual) values for the data source. You can then quickly see how accurate your predictive model is, what are the outliers, the zone of possible errors. For more information, refer to The Forecast vs. Actual Graph and The Signal Outliers

If you're not satisfied with the predictive scenario once you’ve analyzed the results, you can improve it by changing the settings, or if necessary, changing the data source.

Check the quality of your predictive model performance over the Horizon-Wide MAPE. The Horizon-Wide MAPE is the evaluation of the "error" made when using the predictive model to estimate the future values of the signal. A Horizon-Wide MAPE of zero indicates a perfect predictive model. The lower the Horizon-Wide MAPE, the better your predictive model performance.

Analyze the predicted values for the predictive model over a set of known data from the training data source. Check if there are outliers in the forecasts and detect anomalies on the signal.

Use the Signal versus Forecast graph to visualize the predicted values (predictive forecast) and actual values (signal) for the training data source. You can then quickly see how accurate your predictive model is, what the outliers are, and the zone of possible errors.

For more information regarding Smart Predict, you can go to Analyzing the Results of Your Time Series Predictive Model.

After running Smart Predict and confirming that the results are reasonable, you can save the results to the planning model in a private version.

You can write Smart Predict results only to private planning versions. The goal is to enforce a validation workflow and avoid unvalidated forecasts from being written to public versions.

The confidence interval (dotted lines) helps to understand the level of confidence we have in the forecast. If the interval is narrow, we have a high confidence in the forecasts.

After running Smart Predict and confirming that the results are reasonable, you can save the data to the planning model in a private version.

After analyzing the results in a story, the values can then be published to a public version.

For more information regarding on predictive modeling, go to the Applying AI-powered Visualizations and Augmented Analytics to Business Data in SAP Analytics Cloud course, where augmented analytics are ecplored in more detail in SAC, including time series predictive models.