Lilly Tech Systems
Advanced
Best Practices
Implementation strategies, change management, cybersecurity, ROI measurement, and scaling AI across manufacturing operations.
Implementation Strategy
Start with a Pilot
Choose one high-impact, low-risk use case (e.g., predictive maintenance on a single machine). Prove value before scaling.
Data First
Before building AI, ensure reliable data collection. Install sensors, establish data pipelines, and validate data quality.
Cross-Functional Teams
Combine domain experts (operators, engineers) with data scientists. Factory knowledge is essential for useful AI models.
Iterate and Scale
Deploy, measure, improve. Once proven on one line, standardize and replicate across the factory and other plants.
Change Management
Technology is the easy part. Getting people to adopt AI is the real challenge:
- Involve operators early: Include shop floor workers in AI design and testing. They know the process best.
- Transparent AI: Show why the AI made a recommendation, not just what it recommends.
- Gradual trust building: Start with advisory mode (AI suggests, human decides), then move to automated mode.
- Training programs: Invest in upskilling operators to work alongside AI systems.
- Measure and communicate: Share success metrics (downtime reduced, defects caught) with the whole team.
OT Cybersecurity
| Risk | Impact | Mitigation |
|---|---|---|
| Network exposure | IT/OT convergence opens attack surface | Network segmentation, DMZ, firewalls |
| Data exfiltration | Proprietary process data leaked | Encryption, access controls, monitoring |
| Model tampering | Adversarial attacks on AI models | Model validation, input sanitization |
| PLC manipulation | Unauthorized process changes | Authentication, write protection, audit logs |
| Supply chain | Compromised software/firmware | Vendor assessment, code signing, updates |
IEC 62443: The international standard for industrial automation cybersecurity. Follow this framework for securing AI-connected industrial systems. Implement defense in depth with multiple security layers.
Measuring ROI
- Downtime reduction: Track unplanned downtime before and after predictive maintenance deployment
- Quality improvement: Measure defect escape rate, scrap rate, and rework costs
- Throughput increase: Units produced per hour with AI optimization vs. baseline
- Energy savings: Compare energy consumption per unit before and after optimization
- Maintenance costs: Emergency repair costs vs. planned preventive maintenance costs
- Labor efficiency: Inspection time saved, reduced manual data entry, faster root cause analysis
Scaling Across Plants
- Standardize data models: Use consistent sensor naming, data formats, and OPC UA information models
- Transfer learning: Train models on one plant, fine-tune on others to reduce data requirements
- MLOps pipeline: Automate model training, validation, deployment, and monitoring across sites
- Edge-cloud architecture: Run inference at the edge, train models in the cloud
- Center of Excellence: Build a central team that supports multiple plants with shared best practices
Congratulations! You've completed the Industrial Automation course. You now understand how to integrate AI with PLCs, build predictive maintenance systems, deploy vision-based quality inspection, create digital twins, and manage the human and organizational challenges of industrial AI. Go transform manufacturing!