Virtual models of products and assets – now more commonly known as digital twins - have been used by designers and engineers in concert with simulation to meet product and production requirements for years. The opportunity with “digital twins” is to take these virtual models and extend them to the rest of the team, outside of engineering, involved with product design and development, as well as production and operations.
Providing this view of the product, production, and asset lifecycle is necessary as the innovation process broadens in manufacturing organizations, and demand and competitive pressure continues to rise. Manufacturers need the ability to respond quickly, through timely communication of related information such as:
- Product and Process Model: Plant and quality information can be viewed in a product digital twin to ensure proper equipment tooling and performance, and manufacturing processes, are in place to support the changing mix of products that is present in every industry today.
- Facilities operations: A manufacturing plant digital twin could include facility information such as manufacturing plant floor layout, building function, and energy efficiency, along with a view of manufacturing lines, equipment, and process performance.
- Quality Metrics: Overall Equipment Effectiveness (OEE) and Manufacturing Execution System (MES) metrics could be viewed within the digital twin plant model, and if any production or equipment quality issues, changes can be made quickly.
- Condition Monitoring and Predictive Maintenance: Condition monitoring systems and analytics available from vendors with manufacturing operations management (MOM) software like Siemens or GE could be tied to the building model and predict manufacturing line, machine, robot, or building infrastructure quality or performance issues that could affect the production or quality of a product.
Better decision support and closed-loop quality are two of the biggest strategic reasons why digital twins of products and assets are important to manufacturers today. Having this living, digital model with multi-physics, systems-level content that ties big data analytics with statistical analytics from the product or plant floor improves communication and expedites action, and leads to faster time to market and resolution of quality issues.
Predictive and prescriptive analytics becomes possible as the digital twin of a product or asset grows in content richness, thereby enabling improved ongoing operations, maintenance, and service. This visual, virtual copy of a product or asset can also lead to better communication and collaboration among team members as diverse as sales, marketing, product management, manufacturing, supply chain, and service, who can securely view the level of content they need.
The extended use of simulation, beyond design and engineering, and use of digital twins can also lead to manufacturing optimization – consider the following scenarios:
- One large automotive manufacturer spoke at an industry event last year about the importance of using ergonomic simulation to ensure proper operator training and usage of shop floor equipment and manufacturing lines.
- Simulation and digital twins can also greatly enhance the effectivity of manufacturing by ensuring proper capability and capacity is in place to meet dynamic product demand and customer needs.
- The energy efficiency and performance of connected products, assets, manufacturing lines, and factories can also be tracked and adjusted as necessary over their lifetime through a digital twin.
Digital twins of products, equipment, buildings, and processes – brought to life through simulation - are necessary for innovation transformation. Essentially, digital twins are the extended application of simulation and visualization throughout a digitally transformed organization, for better communication and collaboration. When you have a connected view of all processes, people, and assets, you can respond more quickly to demand because the optimal manufacturing plant layout, processes, and capabilities are in place, and you can determine and address quality issues quickly and proactively across the enterprise. So important in today’s complex world of connected products, processes, production, and customer experiences.
I welcome your comments, as I continue to write about the emerging application of visualization, simulation, and digital twins to the broader enterprise and value chain.