Digital Twins for Industry 4.0: Applications, Benefits, and Considerations

If you are a tech-savvy person, you’ve most likely heard about “Digital Twins”. Digital Twins are a major Industry 4.0 trend to watch in 2019 and this week we take a closer look at this innovative technology and why Digital Twins matter to the industrial world.

Digital Twins in A Nutshell

Digital Twins have garnered significant interest over the last few years as the Internet of Things (IoT) has become more and more pervasive. A digital twin is a virtual model that mirrors a physical object or process throughout its lifecycle. Providing a near real-time bridge between the physical and digital worlds, this technology enables you to remotely monitor and control equipment and systems. Ultimately, it can execute simulation models to test and predict asset and process changes under different “what-if” scenarios. Leveraging digital twins, companies can realize substantial benefits such as improved operations, product and service innovation, and faster time-to-market.

Creating a digital twin requires different elements, including:

Sensors capturing operational behaviors of assets and processes (vibration, temperature, pressure, etc.), alongside their functioning environments (air temperature, humidity, etc.)

Communications networks providing secure and reliable data transfer from physical devices to the digital world

A digital platform that serves as a modern data repository pooling and storing shop floor sensor data with high-level business data (e.g. MES, ERP). By combining these data sources, actionable insights can be derived for data-driven decision-making – using advanced AI/machine learning algorithms.

First realized in the aerospace industry, digital twins are now gaining traction across industrial verticals. You can build a digital twin of almost everything regardless of its size – from single components and assets (rotors, turbines, pipelines, etc.) to complex processes and environments (production lines, manufacturing plants, wind farms, etc.). The level of sophistication and detail of your digital twin models depends on the availability and maturity of your IT infrastructure.

3 Applications of Digital Twins for Industry 4.0

Digital twin technology renders unprecedented visibility into assets and production to spot bottlenecks, streamline operations and innovate product development. Below are the three major applications of digital twins for Industry 4.0.

Predictive Maintenance: Gaining a holistic view of the health and performance of equipment, companies can immediately detect anomalies and deviations in its operations. Maintenance and replenishment of spare parts can be proactively planned to minimize time-to-service and avoid costly asset failures. For OEMs, predictive maintenance using Digital Twins can provide a new service-based revenue stream while helping improve product reliability.

Process Planning and Optimization: A digital footprint ingesting sensor and ERP data of a manufacturing line can comprehensively analyze important KPIs like production rates and scrap counts. This helps diagnose the root cause of any inefficiencies and throughput losses, thereby optimizing yields and reducing wastes. Taking it one step further, rich, integrated historical data on equipment, processes, and environments can enable downtime forecasting to improve production scheduling.

Product Design and Virtual Prototyping: Virtual models of in-use products provide comprehensive insights into usage patterns, degradation point, workload capacity, incurring defects, etc. By better understanding a product’s characteristics and failure modes, designers and developers can correctly evaluate product usability and improve future component design. Similarly, OEMs can deliver customized offerings for different groups of customers based on specific usage behaviors and product implementation contexts. Digital twin technology additionally aids in developing virtual prototypes and running robust simulations for feature testing based on empirical data.

Real-world Use Cases

Key Considerations for Deploying Digital Twins

Analysts like Gartner have advocated for a soon-to-be digital twin explosion, predicting that half of all large manufacturers will have at least one digital twin initiative by 2020. Beyond the hype, it is important that companies accurately assess their readiness for undertaking the complexity of such an initiative. It boils down to how data – the lifeblood of digital twins – can be aggregated and put into use.

In legacy automation and control systems with proprietary industrial protocols, sensor data are encapsulated in local, closed-loop processes and not exchangeable externally. Retrofitting brownfield with IoT connectivity to break down these silos and make valuable data accessible across the company, can be a daunting process. What’s more, connecting mobile industrial vehicles and remote, hard-to-access equipment will require a different type of connectivity that satisfies special requirements like mobility support and range. Therefore, prior to starting a digital twin project, companies should consider whether a sufficient communications infrastructure is already in place for effective data collection.

Even with enough data at hand, structuring and analyzing these data to create values will be another hurdle to overcome. To avoid overcomplexity, it is important to assess your existing digital capability from the outset and determine the optimal level of digital twin model details accordingly. Similarly, a balanced approach to retaining software, simulation, and analytics resources is required.

Final Thoughts

The best way to embark on a Digital Twins initiative is to identify the asset(s) and process(es) with the highest potential for value creation, and then begin a pilot implementation. A digital twin should be a work-in-progress that continuously evolves and scales – as your IT capacity expands and matures. Typically, digital twins of various single components can later be interconnected to form a large, composite twin of a highly complex machine or process. Also, recursively monitoring and measuring created values over time will provide a better idea where the most tangible benefits can be realized.