The situation
A mid-size CPG manufacturer with a portfolio of food and household brands had invested significantly in AI for demand planning, dynamic pricing, and trade promotion optimization. The supply chain team had a clear view of what the AI was designed to deliver, and a clear understanding that the data quality across systems needed to be resolved before the models could produce outputs they were ready to act on. The same customer appeared under different identifiers across the ERP, demand planning system, and trade promotion platform. No recommendation could be traced to the specific data that produced it.
Separately, the organization had accumulated AI and automated systems over time and had a growing need to understand exactly which systems were influencing pricing and inventory outcomes.
The solution
The Knowledge Fabric™ connected the ERP, demand planning system, trade promotion platform, and syndicated POS data feed. Entity resolution reconciled customer and product master data across all four systems. Every data point the AI models read now carries a PolyPhaze Trust Score™.
The Optimize capability deployed seven domain agents against the governed foundation. Trade promotion ROI was tracked by event against actual consumer sell-through. Inventory positioning was optimized against the connected demand signal, surfacing more than $4 million in annually recoverable stranded inventory with specific disposition recommendations.
AI Decision Trace established a governed baseline before any AI changes were made. The Coverage Discovery capability identified three automated decision-making systems operating outside the organization’s sanctioned AI registry, including a legacy trade promotion tool making adjustments at a velocity and uniformity pattern indicating algorithmic, not human, decision-making. Post-deployment monitoring runs every two hours across pricing and inventory decisions.