Digital Twin Trust & Value Assessment for Manufacturing

Welcome, and thank you for participating in this survey about digital twins in our organization. The purpose is to understand how digital twins are used, trusted, and valued across different functions. Your participation is entirely voluntary and you may stop at any time. All responses are confidential and will be reported only in aggregate — there are no right or wrong answers. The survey takes approximately 10 minutes to complete.

What is your current level of involvement with our digital twins?

• Hands-on user of a digital twin
• Project owner or decision-maker
• Collaborates occasionally with the twin team
• Aware, not involved
• Not familiar with digital twins

Which data sources are currently integrated into the digital twin(s) you use or support? (Select all that apply)

• PLC/SCADA data
• IoT sensors (condition monitoring)
• MES/production execution
• ERP (orders, inventory)
• CAD/BOM/PLM
• Maintenance/CMMS
• Simulation models
• Not sure
• Other

Overall, how much do you trust the outputs from our digital twins today?

Which barriers most limit the adoption or impact of our digital twins today? (Select all that apply)

• Data quality and availability
• Integration with existing systems
• User skills and training
• Unclear ROI or business case
• Security and compliance requirements
• Model transparency/explainability
• Tool usability
• Change resistance/culture
• Other (please specify)

What is one metric you would use to judge the digital twin's usefulness in your area?

Where is your primary work location/region?

• North America
• Europe
• Asia-Pacific
• Latin America
• Middle East/Africa
• Multiple regions
• Prefer not to say

Thank you for your time and insight! Your responses will help us prioritize improvements to our digital twin program.

Which area best matches your current function?

• Operations/Production
• Maintenance/Asset Management
• Quality
• Process/Manufacturing Engineering
• R&D/Product Engineering
• IT/OT
• Supply Chain/Planning
• Other

How are the digital twin's outputs validated in your area? (Select all that apply)

• Compared with live production data
• Backtesting with historical data
• Subject matter expert sign-off
• Automated drift/accuracy monitoring
• Formal measurement and verification (M&V)
• We do not validate today
• Not sure

How useful is the digital twin for predicting or preventing production issues in your area?

Rank the following areas by where investment would most improve digital twin trust and outcomes (most to least important).

1. Data quality and availability
2. Validation and accuracy monitoring
3. Explainability and transparency
4. User experience and training
5. Integration and performance
6. Governance, ownership, and support

Based on your responses in this survey, please share any additional thoughts or suggestions on how we can improve digital twin trust, usefulness, or adoption in your area.

How many years have you worked in manufacturing or industrial operations?

• 0–2
• 3–5
• 6–10
• 11–15
• 16+
• Prefer not to say

How accurately does the digital twin mirror current production conditions in your area?

How useful is the digital twin for optimizing process parameters or throughput in your area?

Are there any security or compliance risks related to our digital twins that you believe should be addressed? If so, please describe them briefly.

Which best describes your current role level?

• Individual contributor
• Team lead/Supervisor
• Manager
• Director or above
• Consultant/Contractor
• Prefer not to say

How useful is the digital twin for supporting planning, scheduling, or capacity decisions in your area?

What best describes your primary work environment?

• Shop floor
• Office
• Hybrid
• Remote
• Field/on-site customer locations
• Prefer not to say

How useful is the digital twin for quality monitoring or root-cause analysis in your area?

Rank the following factors by how much they increase your trust in a digital twin (most to least important).

1. Transparent versioning and change history
2. Validation against ground truth data
3. Explainable recommendations/visibility into drivers
4. System uptime and performance
5. Clear ownership and support model