Product Twin: Analytics-based product engineering

Fixing a design flaw after a product has been launched is a very expensive prospect for most manufacturers, and one fraught with legal implications. The imperative to get product design right-first-time is high.

Most product development initiatives are engineering-led, but involve collaboration across a range of functions and departments, each with their own systems. The siloed nature of these systems frequently serves to hinder collaboration rather than enable it. An inability to easily share data often forces product development teams to make hypothesis driven decisions that lead to over-or under-engineered products.

The application of IoT data, cloud computing and system simulation will help to:

• Solve asset performance issues faster and in a more efficient way
• Develop user-centric products to meet customer needs and increase customer satisfaction
• Decrease product development cost

Watch this webinar to explore best practices for:

• Using IoT platform that brings together data from multiple number of sources, supported by hundreds of applications
• Creating a digital twin of an asset and making decisions based on real machine data
• Interweaving machine performance data with design capabilities