Probabilistic Demand Forecasting with ToolsGroup SO99+

White papers

SCP

Probabilistic Demand Forecasting with ToolsGroup SO99+

Learn how probabilistic forecasting and machine learning improve demand plans for slow-moving and intermittent SKUs while reducing inventory risk.

Published

June 4, 2026

Read time

3 min read

Source

This datasheet explains ToolsGroup SO99+ demand forecasting, which replaces single-point forecasts with probability distributions generated by a single self-tuning algorithm. The approach layers machine learning, demand sensing, promotions, seasonality, and new product launch profiles to produce reliable forecasts across multi-echelon networks. Planners shift from manual forecast tuning to exception-based management, supporting higher service levels with lower working capital.

Key takeaways

Probabilistic forecasts output ranges of demand outcomes with occurrence probabilities instead of single numbers

One self-tuning algorithm replaces best-fit model selection and improves forecast stability

Machine learning incorporates macro trends, POS, web, and social signals beyond demand history

Demand modeling layers baseline, sensing, promotions, NPI, and market intelligence for optimized plans

Typical results include 50-90% reduction in planner workload and better service-inventory tradeoffs

Market overview

SCR methodology note

Vendor landscape

Leaders

Implementation considerations

Important consideration

Original Source

Vendors mentioned in this report

No items found.