Proven Digital Twin Strategies Across the Product Lifecycle
As deep lifecycle intelligence becomes a business requirement, so does the need for practical insights and solutions to maximize the impact of your data-driven journey – regardless of your starting point. Curated by our Event Advisory Committee, the program is reviewed and updated each year to meet the accelerated needs of industry.
Presented by the authorities on industrial digital twin implementation, the program provides the critical knowledge you need to successfully build operations that function with the speed and resilience required by today’s challenging market conditions. Sessions are provided for all levels of learning, with information for experienced engineers, recent graduates, and everyone in between.
Conference Topics:
Applications
- How to secure IT/OT convergence.
- What are the types of threats and risks both internal and external?
- Cybersecurity considerations in for your IoT infrastructure
Architecture / Supporting Technologies
- Demystify how raw data is processed into actionable and insights
- How to build your data strategy with your business use case.
- What elements comprise the data and analytics ecosystem of solutions?
Fundamentals (CIO, CDO, CFO)
- What’s hype, reality, and is my organization ready for it?
- What are the most common uses cases today?
- What is the right level of autonomy for my business?
Workflow
- What types of sensors are used and how are they integrated into the analytics solution?
- How is the data captured processed and synthesized?
- How to implement a predictive maintenance strategy.
Frameworks
- Scaling from point solutions trials to Digital Twin platforms.
- Using customer insights to drive product development and service opportunities.
- Delivering the right insights at the right time to the right decision maker.
Demonstrating Value / ROI
- Examples of leading industrial applications that are creating value.
- Why proving value out of the starting block is critical to long term success.
- What are the value traps and mistakes that should be avoided?
