Provides visibility into past performance using KPIs and dashboards. Answers the fundamental question of “what happened” by summarizing historical data from orders, inventory, and shipments.

Drills down to find the root cause of an event or outcome. This stage moves beyond simple reporting to uncover relationships and dependencies that explain performance deviations.

Uses statistical models and machine learning to forecast future outcomes. This is crucial for demand forecasting, predicting potential disruptions, and anticipating changes in lead times.

Goes beyond predictions to recommend specific actions to achieve desired outcomes. It evaluates trade-offs to suggest optimal inventory levels, routing decisions, or production plans.

The most advanced stage, using AI and ML to create self-learning systems. These platforms can automate decisions, continuously improve forecasts, and adapt to new data patterns without human intervention.