Adoption of digital twins across products, machines and processes continues to skyrocket across enterprises. Industry analysts and consultants agree; Deloitte forecasts the global market for digital twin technologies will reach $16 billion by 2023.
IDC suggests that 30% of Global 2000 companies will be using data from digital twins of IoT connected products and assets to improve product innovation success rates and organizational productivity, achieving gains of up to 25%.
The nature of selling products continues to change from the factory floor to point of sale to support services, having a correlating impact on industrial companies’ business models.
Manufacturers are already bringing digital twin intelligence to the factory floor; IDC forecasts 70% of manufacturers will use ‘digital twins’ to conduct simulations and scenario evaluations, reducing equipment failures by 30%.
Digital twins can play major roles in other manufacturing use cases including:
• Closed-Loop Engineering: Real-world performance data of deployed connected products provide pivotal real-world insight and feedback loop to designers and engineers.
• Connected Operational Intelligence: Streamlining operations through twins of the manufacturing process and specific machines on assembly lines reduce production downtime and increase yield.
• Predictive Monitoring & Remote Service: Extending value-added services to field technicians through predictive insights improve product reliability, brand reputation and customer satisfaction.
• Flexible Business Models: Sales & Marketing teams empowered with digital product information can open additional post initial sale revenue streams.
A critical functionality of digital twins will be the timeliness and delivery of increasingly powerful simulation applications. While applying a computer-aided engineering (CAE) simulation algorithm to historical digital twin generates value, there will be enormous opportunities for simulations based on real-world twin data that can be quickly implemented in a production environment or for a smart connected product.
ANSYS applies its simulation applications to further improve real-time predictive maintenance applications.
Further feeding in business system context through integrations as well as environmental data feeds provides an even greater data pool to apply simulation algorithms. Machine and deep learning algorithms will increasingly play a role in comprehending these disparate data sources, unveiling novel business inputs and enterprise-wide strategic recommendations.
IIoT platforms will also be key to make rapid bi-directional communication and data transmission between the physical system and its digital counterpart.
This idea isn’t far off the horizon; IDC forecasts that by 2022, 40% of IoT platform vendors will integrate simulation platforms, systems, and capabilities to create digital twins.
As digital twins multiply across the enterprise and use cases branch out far past ad-hoc deployments, orchestrating different twins with one another will be a giant opportunity for further streamlining business processes.
Creating this ‘twin network’ will require an interwoven digital thread enabled through interoperability across different forms of an organization’s twins from products to machines to processes.
This futuristic deployment could look like a service technician equipped with AR glasses viewing a digital twin of a specific machine, on a digital assembly-line sending real-time performance feedback data to the manufacturer’s product design team.
Orchestrating these twin networks can unlock many other use cases past universal data access and both IDC and 451 Research cite digital twin orchestration as a long-term essential capability.