The Potential of AI in Planning Analytics

IBM Planning Analytics, an AI-infused planning, forecasting, and reporting solution offered by IBM, has for long been a popular tool among enterprises. Over the years, it has received numerous accolades thanks to its consistent improvements and innovation. At the core of it, there’s the ‘TM1 server’, which is a powerful calculation engine capable of working with large data sets and performing real-time calculations.

The powerful software can help organizations automate spreadsheet-based processes which are, by definition, manual in nature. The automation and intelligence the solution offers can help enterprises link financial plans much more effectively to operational tactics. The tool features a customizable workspace and also offers a Microsoft Excel Interface.

Here’s what users can achieve by using IBM Planning Analytics –

• Driver-based planning
• Rolling forecasts
• Complex dimensional calculations to analyze product profitability, price/volume variance, and sales mix
• Discovering insights directly from data
• Monitoring KPIs
• Communicating business results through various types of visualizations

The software has been available as both SaaS and on-premise offering for years. But despite that, the higher cost and longer implementation times meant that Planning Analytics was still out of reach for many SMBs. But with the launch of IBM Planning Analytics on Demand, a SaaS offering, it has become easier for smaller-sized businesses to benefit from the AI-fueled forecasting, planning, and reporting without relying on IT support.

Since it enables smaller user groups to benefit from AI, the potential is huge. Let’s use this article to discuss the potential of AI in Planning Analytics and touch upon how small businesses can benefit from it.

Predictive forecasting (AI-based forecasting)

Predictive forecasting in Planning Analytics uses historical time-series data to create forecast data. It involves using a series of algorithms to run data to figure out the algorithm that seems the best fit in terms of highest accuracy and least errors in predicting a future value.

The key in this AI-based forecasting is to find the best-fit projection for the future by choosing from a number of forecasting methods. This forecast can then also be saved as a version.

The forecasting algorithms in the software discover and model seasonality, trends, and time dependence in data. The forecast is displayed as a continuation of historical data with highlighted confidence intervals.

AI-based forecasting is not only more accurate but also fully autonomous, thus continuously reconfiguring forecast projections.

Another key benefit of AI-based projection is that the solution can be fed as many business metrics and key performance indicators as you have at your disposal. Machine learning is continuously able to find patterns and correlations in large amounts of data which is otherwise difficult to find for humans.

Predictive forecasting can improve the following aspects of forecasts –

• Accuracy – It can greatly improve forecast accuracy by assessing trends and seasonality patterns in historical values.

• Consistency – It helps produce dependable forecast values using consistent algorithms across different business areas.

• Timelines – It can help reduce the time required to produce an accurate forecast, thus allowing users to only focus on process optimization.

Here are some examples of how organizations benefitted from improved forecasting processes –

• For Hyundai Motors, there was a 20% reduction in delivery time due to better forecasting.

• For Reynolds Aluminum, there was a reduction of forecasting errors by 2%.

• Unilever was able to save multi-million dollars by reducing forecasting errors from 40% to 25%.

• SCI Systems saved $180 million by reducing on-hand inventory by 15%.

Guided planning

The budgeting and planning process can be difficult for any organization given the scale of the exercise. But guided planning can make the process easier by helping budget and planning managers create plans containing multiple stages and steps within each stage. After completing plan creation, the planning managers can then invite business users to participate in the plan and allocate them to specific steps.

This helps planning managers monitor the progress of plan activities while understanding the status of each step. They can use guided planning to efficiently manage their company’s planning process and eliminate many common stressors associated with the planning process. All of this comes with automated model creation and natural language processing can help make it even quicker and easier to create an accurate plan.

Business intelligence

Business intelligence (BI) leverages technologies, architectures, or services to take raw data and convert it into actionable insights that can drive profits. Thanks to IBM Planning Analytics’ capability to integrate with IBM Cognos Analytics and IBM Cognos applications, it becomes easier for organizations using Planning Analytics to benefit from BI.

Cognos Analytics is IBM’s AI-fueled BI platform that supports the entire analytics cycle, from discovery to operationalization. This integration thus helps extend the potential of Planning Analytics to BI as well.

Here’s how smarter BI can help organizations –

• Visualizing business performance by creating dynamic dashboards and reports with AI-backed recommendations.

• Letting AI uncover hidden patterns in data and interpreting the data to present actionable insights in plain language.

• Sharing critical insights with anyone in the organization.

• Saving time with AI-assisted automated data preparation.

• Using machine learning to automatically discover and combine related data sources into a single module.

Conclusion

For the past few years, Planning Analytics has been the preferred choice for users who want to migrate budgeting and forecasting processes to automated AI-based processes. Organizations can use this tool to identify meaningful patterns in data and extract valuable insights.

As shown in this article, AI has a huge potential in Planning Analytics in helping organizations move away from traditional processes and adopt newer automated data-backed processes. The tool, therefore, allows organizations to quickly realize the power of AI and automation. If you need any help with Planning Analytics, then we might be able to help as the first IBM partner in India to receive IBM Cognos TM1 Gold Accreditation. Kindly connect us here