COO-TWIN-1 · COO · Manufacturing Digital Twin
Plant Digital Twin
Capex success rate
90%
from 65 %
Throughput
115index
from 100 index
Problem & Capability
What & howExecutive problem
Capacity and process changes are high-risk and slow.
Capability
AI simulates production scenarios, machine behavior, throughput and energy.
Outcome & Strategic Impact
Why it mattersBusiness outcome
Less waste, optimized flow, better capacity planning.
Strategic impact
De-risks capex and capacity expansion.
KPI trajectory · Baseline → Target
ExhibitAI explainability — drivers, risks, next 90 days
Deploying a Plant Digital Twin enables Avery Dennison to simulate and optimize manufacturing scenarios, reducing risk and accelerating capacity decisions. This initiative directly supports higher capex success rates and throughput, strengthening operational resilience and supporting profitable growth.
Drivers
- AI-driven scenario modeling for rapid, low-risk process changes
- Real-time visibility into production bottlenecks and energy use
- Data-backed capacity planning to support strategic expansion
Risks
- Data integration challenges across legacy and new systems
- Change management resistance from plant operations teams
- Insufficient simulation accuracy impacting decision confidence
Next 90 days
- Select pilot plant and define baseline KPIs with operations leadership
- Integrate core production and energy data streams into digital twin platform
- Run initial simulations to validate model accuracy and identify quick wins