The Internet of Things and digital twins are changing the ways digital and physical interact. IoT provides the connection and access to intelligence in the physical world and interlinked with digital twins, which are digital models that virtually represent their physical counterparts.
In 2020, IoT must be a key strategic consideration in order to realize the full potential of digital twins of physical products, operational processes, or person’s tasks. The physical world experiences of these three ‘P’s’ – products, processes, and people – captured through sensors and IoT is a fundamental requirement of a true digital twin.
The digital twin market is forecasted to be $16 billion by 2023, and analysts agree IoT will be a cornerstone of this growth; IDC expects that 30% of Global 2000 companies will be using data from digital twins of IoT connected products and assets.
Digital twins fulfill their terms by being live and dynamic whereas lesser terms, including digital replicas and shadows, imply minimal real-world implications and less impactful use cases. 451 Research cites that ‘Twin implies that what happens to one happens to the other, in a mutable fashion’, which puts IIoT as the bi-directional link to enact this and empowers the transformative use cases that come with it.
3 Ways IIoT Is Enhancing Digital Twins
Smart connected products are replacing assumptions with facts; real-world IIoT data closes the feedback loop with product usage data, which then informs future iterations – and even business model changes, including product-as-service. Product telemetry also gives engineers and product designers behavioral characteristics of deployed products or fleets of products.
Digital Twin of Products: IoT Offers Visibility Into the Full Product Lifecycle
Providing a frame of reference to compare the ‘as-is’ versus ‘as-used’ product usage is an extremely powerful IIoT-enabled insight that can inform the development of future product iterations. Its applicability can range from replacing or modifying certain features to drilled-down insights into the specific performance of part(s).
Expanding visibility into the product lens through cross-functional collaboration can also drive downstream efficiencies. This includes change management in manufacturing and service processes, which lowers scrap, rework, and lead times.
Real-World Example: Whirlpool is achieving data-driven design by connecting deployed appliances through IIoT and analyzing operating performance metrics (torque, drum speed, motor temperature, etc.) across fleets of products to improve future iterations.
Many operational processes are plagued by two factors: disparate and black-boxed information sources. IIoT unlocks these unknown insights and threads them with different sources both in real-time and historical systems of record. Twins of these connected assets and workers and how they interact are critical to a constructing a process lens – essentially a system-wide view of an industrial environment.
Digital Twin of Processes: IoT Unlocks Deeper Operational Intelligence
IIoT through a process lens can drive critical manufacturing KPIs. For example, improving the uptime of a single asset on a factory floor through IIoT-driven predictive insights can drastically improve throughput while a twin of a production line can reduce bottlenecks through enhanced operational visibility.
This connected operational intelligence from diverse assets creates the real-time 360-degree visibility manufacturers need to be flexible and agile – a necessity in today’s changing markets and shifting customer demands.
Real-World Example: Woodward is gaining operational visibility by integrating its technologies and workers through the IIoT across its factories. An IIoT platform contextualizes myriad information sources in its production facilities, including connected devices (torque wrenches, pressers, etc.), manufacturing execution systems (MES), and product centric-software (CAD, PLM), to give an end-to-end operational view.
Much of a product’s operational condition and performance in the end user’s environment hasn’t been accessible to the manufacturer or customer. With maintenance and service being critical functions to reduce asset downtime and differentiate offerings, digital twins with IoT can drastically improve these metrics and enable new revenue streams.
Digital Twin of Service: IoT Optimizes Maintenance
Digital twins can bolster remote service IIoT use cases where software updates, patches or reboots for deployed assets can negate the need to send a technician on-site. IIoT’s flexibility can enable mission-critical systems to sample data every second to inform services, or less frequently to optimize resources, all depending on the digital twin use case.
Telemetry data can also feed into the deployed asset’s digital twin to gain a baseline of its health and apply next-generation predictive maintenance modules blending machine learning and physics-based simulation techniques. Simulating historical patterns of machine performance with design expectations against real-time sensor data will reduce unplanned downtime and add another layer of intelligence, which further maximizes asset utilization.
Real-World Example: Howden is helping customers succeed through its ‘Data Driven Advantage’ program. This initiative has embedded IIoT-driven insights into customers service and maintenance workflows to save millions in unplanned downtime and reduce business risk.
Bringing the Digital Twin to Life
The full fidelity of a digital twin will become available as IIoT is added to organization’s products, processes, and people. This ‘live’ data will also serve as an entryway for next-generation use cases, as it’s a lucrative inputs for physics-based simulation, artificial intelligence, and computer vision applications.
The time is now to start on a digital twin strategy, read our whitepaper for inspiration on where to begin.
Industrial Internet of Things
About the Author
David Immerman is as a Consulting Analyst for the TMT Consulting team based in Boston, MA. Prior to S&P Market Intelligence, David ran competitive intelligence for a supply chain risk management software startup and provided thought leadership and market research for an industrial software provider. Previously, David was an industry analyst in 451 Research’s Internet of Things channel primarily covering the smart transportation and automotive technology markets.