A full DMAIC engagement addressing a high-friction operational problem: The absence of prioritization framework for resource allocation during capacity limitations. I designed a closed-loop, AI-powered Prioritization Credit System (PCS)™ GPT that evaluates projects and requesters using measurable criteria such as NPV, completeness, cycle-time impact, collaboration load, and scope creep risk. The system optimizes limited capacity toward the highest-value, highest-impact work while maintaining equity, transparency, and creative flexibility.
Problems to Address
2025
The Define Phase
2025
The Measure Phase
2025
The Analyze Phase
2025
The Improve Phase
2025
The Control Phase
2025
Capstone Deliverable: Prioritization Credit System (PCS)™ GPT
As the capstone deliverable of the DMAIC project, I developed the Prioritization Credit System (PCS)™ GPT; a closed-loop, AI-powered prioritization engine that evaluates every project using measurable criteria such as NPV, cycle-time impact, scope-creep risk, completeness, and collaboration load. The PCS™ transforms subjective project selection into a transparent, data-informed, and equitable system that optimizes limited capacity toward the highest-value work while maintaining fairness, creative flexibility, and strategic alignment.
