Modernizing financial forecasting for a global organization
Learn how a major global organization produced thousands of forecasts over 2 years with machine learning.
Financial forecasting is an essential but time-consuming part of most business operations. A major global organization realized accelerating their forecasting speed would allow them to be more tactical and adjust forecasts at more frequent intervals. To achieve this, the organization partnered with Neal Analytics to implement a modern forecasting solution leveraging a combination of machine learning and traditional forecasting methods. This solution allows the organization to:
- Automate the collection of forecasting data
- Provide initial forecasts leveraging machine learning
- Produce thousands of forecasts over two years
Most organizations view financial forecasting as an essential part of business operations. However, the process of building forecasts can be time-consuming and challenging to scale, especially for organizations with hundreds or even thousands of SKUs.
A major global organization with thousands of SKUs intended both for consumers and businesses recognized accelerating their forecasting capabilities could dramatically impact their business by allowing them to adjust forecasts at more frequent intervals and be more tactical in their decision-making. However, the organization faced a significant challenge in achieving this goal; compiling finance data is the most time-consuming portion of creating a forecast. While hiring more people could expedite data collection to some level, there remains a limit to the speed at which a human worker can compile and analyze data.
The organization reached out to the AI and machine learning experts at Neal Analytics for help to solve this problem. Neal practitioners worked with the organization to understand the unique business culture and data challenges involved and formulated a strategy to reduce time spent compiling data and generating forecasts. The proposed strategy leveraged a combination of machine learning and more traditional forecasting techniques like ARIMA to help the organization rapidly generate on-demand forecasts that forecasters could then adjust as appropriate.
To achieve this, Neal worked with several individual business units within the organization, following an established process for each. For each business unit, practitioners would:
- Gain access to and learn about the data
- Pull data from data sources to conduct exploratory data analysis such as time series analysis
- Determine the suitability of data for financial forecasting
- Develop automated data pipelines to pull data from data sources into the solution
- Run Neal-developed machine learning models on the data
- Perform data analysis to assess model performance
- Adjust models as appropriate until accurate
- Once accurate, build reporting tools (e.g., Power BI dashboards) or integrate the solution with other forecasting toolsets.
- Confirm the accuracy of raw output from the forecasting models
- Continuously monitor and iterate on models to improve quality and accuracy
By following these steps, Neal Analytics effectively scaled out a modern, machine learning-based forecasting solution to multiple business units at the organization and continues to scale the solution out to other business units today. The solution has dramatically accelerated the forecasting process, and the organization has used it to produce thousands of unique forecasts over the previous two years. We expect to see the number and rate of report generation continue to grow as the organization scales this solution out to new business units in the coming years.