Dashboards show the score.
They never show why you lost.
[1]
Plan vs. actual gaps compound with no shared understanding of root cause
AI-powered Root Cause Analysis algorithms automatically trace every gap — excess inventory, service failures, cost overruns — back to specific decisions across forecasting, procurement, and production.
[2]
Tribal knowledge about "why" lives in people's heads and walks out the door
When algorithms reach the edge of structured data, AI agents prompt experts for their tribal knowledge and digitize it into the Enterprise Knowledge Graph — transforming one-time human insight into permanent institutional memory.
[3]
Without shared accountability, improvement initiatives never get traction
AI storytelling agents produce management-ready narratives — a shared, organization-wide story of what went wrong and what must change. The enterprise equivalent of game video replay in elite sports.
Clear Questions.
Clear Answers.
Getting started with your first Post Game Analysis (PGA) doesn't require perfect data; a few core files are sufficient.
While the specific inputs depend on the analysis type, they generally include your item master, historical actuals, and plan snapshots covering demand, production, or procurement. For a more in-depth Root Cause Analysis (RCA), include multiple historical plan versions to accurately trace outcome evolution.
For a complete breakdown of mandatory and optional data inputs that unlock deeper insights—organized by analysis type—please refer to the Data Requirements documentation.