Digital twins are poised to revolutionize discrete manufacturing by reducing operating costs and extending the life of equipment and assets. While digital twins themselves are not a new concept, their application throughout the product lifecycle is. According to Gartner, digital twins are a Top 10 strategic trend for 2017 because of their ability to enable disruptive IoT solutions. They’re part of a broader digital transformation in which IDC says companies will invest $2.1 trillion a year by 2019. Analysts estimate an enormous monetary upside for manufacturers that implement IoT solutions. McKinsey, for example, predicts an annual economic value of up to $11.1 trillion by 2025. The greatest benefits are realized through a combination of product innovation, process innovation, and business model innovation. Digital twins are the key to making this disruptive innovation possible.
Digital twins reduce costs and optimize performance
A digital twin is a virtual representation of a real-world product or asset. This virtual representation is more than just a model of the physical object. Thanks to IoT sensors, the twin can receive continuous, real-time data from the object. This unique one-to-one correspondence makes it possible to virtually monitor the object. Digital twins are vital to improving situational awareness and allowing CIOs to test future scenarios that can enhance asset performance and proactively anticipate maintenance faults.
Digital twins are now being used to manage the performance, effectiveness, and quality of a manufacturer’s fixed assets such as manufacturing machines, lines, and plants. When advanced visualization, IoT, and analytics are applied to these assets, manufacturers can take a more strategic and holistic approach to asset management. Digital twins represent an enormous opportunity for manufacturers, including engineering, design customization, production, and operations.
Four digital twin applications in manufacturing
These are four key ways smart manufacturers will leverage digital twins to achieve a product-centric and model-based enterprise:
Traditionally, engineering has used digital twins to create virtual representations for designing and enhancing products. In this application, the digital twin actually exists before its physical counterpart does, essentially starting out as a vision of what the product should be. IoT innovations now make it possible to capture data from products deployed into the field. This data can be applied to the digital twin for continuous product improvement.
2. Design customization
As consumers continue to demand customized products, digital twins will allow design and engineering to model the various permutations. Previously, manufacturers have struggled with the best way to incorporate customer input into the manufacturing process. Digital twins will make it easier to meet customer demands and integrate usage data that will enhance customization options.
To understand the full impact of digital twins on product customization, suppose for a moment your business sells high-end custom bikes. Customers can choose different colors, wheels, and other specifications. Capturing customer preferences in the digital twin enables your business to generate a fuller picture of customer demand trends. And by capturing customer usage data, you can better understand how custom configurations affect product performance. This allows your business to offer the most reliable options and further allows customers to configure products based on performance attributes. Most importantly, your business will be able to visualize lightweight representations of the twin without the burden of heavyweight design systems and parameters.
Digital twins will make it possible for manufacturers to achieve a “single version” of the truth. Ideally, manufacturers will have a single set of digital twin master data that resides in a central location. That will give manufacturers one version of the truth. When combined with “in-memory” computing-based networks plus a lightweight, change-controlled model capability, manufacturers will be able to analyze and visualize data rapidly. The digital twins can also be used to compare quality data across multiple products. This provides deeper insight into global quality issues and allows manufacturers to quickly visualize issues against the “single source truth” model.
Operations enhancement is one of the best-understood applications for digital twins. Manufacturers first create a virtual representation of an asset in the field using lightweight model visualization. Next, manufacturers capture data from smart sensors embedded in the asset, providing a clearer picture of real-world performance and operating conditions. Manufacturers can also simulate that real-world environment for predictive maintenance.
For example, let’s say your business manufactures wind turbines. Your company can capture data on rotor speed, wind speed, operating temperature, ambient temperature, humidity, and other metrics to understand and predict product performance. This allows your business to schedule maintenance before a crucial part breaks, thereby optimizing uptime and minimizing repair costs.
Next steps: Bringing digital twin applications to your business
In the future, digital twins will facilitate new business models, including selling physical object-related performance data and pricing objects based on predictive performance data. But before businesses can capitalize on these new revenue opportunities, manufacturers must first master the basics of digital twin implementation with existing IoT infrastructure.
Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value by reading “Accelerating Digital Transformation in Industrial Machinery and Components.” Explore how to bring Industry 4.0 insights into your business today by reading “Industry 4.0: What’s Next?”