OneStream Predictive Analytics 123 {update}

The Goal: Data-Driven Transformation

One such MNC, ABC, embarked on a journey to revolutionize its forecasting approach using Predictive Analytics 123 by OneStream. The primary objectives were:

1. Enhancing Forecast Accuracy: Leveraging advanced models like SARIMA and Holt-Winters to better capture trends and seasonality.
2. Centralizing Forecasting: Unifying operations across all regions on a single, automated platform.
3. Improving Efficiency: Automating manual processes to save time and resources.
4. Driving Data-Driven Decisions: Using accurate forecasts to optimize inventory, cash flow, and budgeting.

Roadmap to Predictive Excellence

ABC’s roadmap for implementing Predictive Analytics 123 included the following steps:

• Setup and Installation:
  • Installed the Predictive Analytics solution from the OneStream Marketplace.
  • Configured application server settings and assigned security roles.

• Data Preparation:
  • Gathered three years of clean historical data to predict growth for the next 12 months.
  • Defined data ranges to ensure accuracy.
• Template and Cube View Creation:
  • Set up forecasting templates with source and target scenarios.
  • Assigned cube views and selected models like ARIMA and Holt-Winters based on data characteristics.

• Model Execution and Adjustment:
  • Ran predictive models and reviewed accuracy using metrics like MAPE and MPE.
  • Refined the models based on test outcomes to enhance precision.

• Scenario Planning:
  • Simulated business conditions (e.g., market fluctuations) to prepare for potential impacts.
  • Made adjustments to forecasts to ensure adaptability.
• Implementation and Monitoring:
  • Integrated forecasts into decision-making processes such as budgeting and inventory management.
  • Continuously monitored model performance and made necessary improvements.

Forecasting Methods: The Backbone of Predictive Analytics

Predictive Analytics 123 offers several robust forecasting methods to suit various data patterns:

1. Simple Exponential Smoothing: A method for datasets without trends or seasonality, using a single parameter to control influence.
2. Holt-Winters: Extends exponential smoothing by accounting for both trends and seasonality.
3. SARIMA: Adds seasonal components to ARIMA, effectively handling recurring cycles in data.

By combining these models, ABC could achieve accurate and reliable predictions for even the most complex datasets.