MFG-YIELD-1 · Head of Manufacturing · AI Yield Optimization
Yield Optimization
First-pass yield
96%
from 88 %
Material waste
75index
from 100 index
Problem & Capability
What & howExecutive problem
Variability across temperature, pressure, adhesive mix and calibration hurts yield.
Capability
AI continuously adjusts process parameters to maximize yield.
Outcome & Strategic Impact
Why it mattersBusiness outcome
Higher first-pass yield.
Strategic impact
Recurring margin contribution.
KPI trajectory · Baseline → Target
ExhibitAI explainability — drivers, risks, next 90 days
Yield optimization through AI-driven process control will directly improve first-pass yield from 88% to 96%, reducing material waste and delivering recurring margin gains. This initiative positions Avery Dennison to lead in operational excellence and supports valuation expansion through higher profitability and efficiency. Execution will reinforce our category leadership in high-value categories and drive measurable financial impact.
Drivers
- Continuous AI-based adjustment of process parameters to minimize variability
- Real-time data integration across temperature, pressure, and adhesive mix
- Direct linkage between yield improvement and recurring margin contribution
Risks
- Data quality or sensor reliability issues impacting AI recommendations
- Change management challenges on the plant floor
- Potential underestimation of integration complexity with legacy systems
Next 90 days
- Pilot AI-driven yield optimization in two high-value category (HVC) plants
- Establish baseline and target KPIs with finance and operations leadership
- Develop change management and training plan for plant-floor adoption