Proposed performance indicators for supply chain planning operations where AI and automation create decisions that legacy metrics cannot measure.
The Generative Metrics Discovery Framework has produced 30 proposed KPIs for Supply Chain Planning across 7 categories, including Accuracy-Outcome Correlation Decay, Consequence-Weighted Forecast Accuracy Gap, Disruption Window Accuracy Differential. Each specification includes a computable formula, data source mappings, a maturity classification, and a validation pilot design.
Legacy Supply Chain Planning metrics were defined before AI systems began creating operational decisions. When an AI capability creates a new decision, that decision carries failure modes that no existing metric is structurally capable of detecting. The framework maps each AI capability to the decisions it creates and defines the measurement gap behind each invisible failure mode.
Of the 30 proposed KPIs, the maturity distribution is: 10 Pilot-Ready, 12 Integration-Required, 5 Infrastructure-Dependent, 3 Research-Forward. Maturity reflects the data infrastructure required to compute each KPI. All KPIs and thresholds are proposed, pending validation pilots.
The AI-Era Supply Chain Planning Reference Catalog presents the complete KPI set in a compact reference format and publishes through Amazon KDP. The free AI-GPS Assessment also produces vertical-specific KPI recommendations for your implementation roadmap.
The AI-GPS Assessment evaluates your AI governance maturity and recommends specific KPIs from this vertical for your implementation roadmap.
Take the Free AI-GPS AssessmentAll KPIs are proposed, not proven. Developed using the patent-pending Generative Metrics Discovery Framework.