Industry 4.0, a name coined to encompass the widespread integration of information and communication technologies that converge the physical and digital of industrial manufacturing, is an ongoing pursuit for manufacturers worldwide.
Successful enterprises are using a full stack of technologies to achieve the goals of Industry 4.0: efficiency, speed, agility, and customer-centricity. Spurred by market forces, such as worker shortage, customer demands for personalization, and global competition, manufacturers are not only leveraging technologies to differentiate, but also to uncover new business opportunities.
The influx of data is central for Industry 4.0 and intelligent use of that data is essential to achieving the ultimate goals. With the right technologies, data can be harnessed and used to put data into context, deliver it in specific and relevant ways, and drive operational efficiency.
One such technology is the digital twin. Once a pie-in-the-sky concept, it’s now achieving real value and presence in the industrial space. In fact, it’s now recognized as a key part of the Industry 4.0 roadmap; a recent study found predicted at 30 percent of G2000 companies will have implemented advanced digital twins to optimize operations by 2020.
One of the primary reasons digital twin technology is rapidly being adopted is there are multiple use cases across the industrial enterprise: engineering, manufacturing and operations, and maintenance and service. Digital twins are made possible (and improved) by a multitude of Industry 4.0 technologies – IoT, AR, CAD, PLM, AI, edge computing, to name a few – to create a powerful tool that’s driving business value.
Let’s take a deeper dive into the role of digital twins in Industry 4.0 objectives:
The technology associated with Industry 4.0 produces – or requires – a tremendous of data. For example, IIoT creates petabytes of data within a large enterprise and requires the cloud for both storage and compute power to analyze the data, alongside artificial intelligence and machine learning to contextualize it with predictive insights.
Key to the success of Industry 4.0 is harnessing this data and using it to drive improvements and find efficiencies across the value chain. For this purpose, the digital twin is a highly effective tool to deliver data and insights in context, in a consumable and actionable format.
For example, there are an increasing amount of smart, connected products that, until recently, product manufacturers didn’t have an effective way to leverage this information to inform future designs or products. With a digital twin architecture, design and engineering teams are analyzing real-world data within the context of physics-based engineering simulations, resulting in insights into how the product is being used and user experience.
Whirlpool is using this type of digital twin to test new innovations with minimal investment and, as a result, is accelerating innovation and getting products to market faster.
Placing data in context is an essential component to the digital twin value proposition. Developed from products, processes, or even people, twins can be delivered in a way that’s relevant to the user’s particular role or task-at-hand. A service technician will want vastly different information than a product engineer.
With high-fidelity twins, users can identify the data and insights most appropriate to their specific situation. This not only creates flexibility, but also opens up a variety of use cases and applications.
Today’s global enterprises are complicated, and many businesses struggle with siloed operations and lack transparency across their value chain. These siloed operations – often driven by disconnected and disparate information systems – cloud opportunities for greater efficiency and operational improvements.
Increasingly, enterprises are looking to gain greater visibility into their operations through the implementation of digital thread, a single set of related data along the entire product lifecycle, from design inception to customer service. This single source of truth presents myriad opportunities for a digital twin to mirror a product or a process.
With a backbone of the digital thread, asset and process-related data is woven together to create digital twins. Through simulations or analysis of patterns within the data, enterprises are uncovering ways to optimize upstream and downstream operations.
In 2019, we’re just at the tip of the iceberg in terms of the potential impact of digital twins will have on the industrial market. Early use cases are promising and as the enabling technologies get more sophisticated, so will the fidelity of twins.
For more on digital twin – and how to implement your own strategy – check out our whitepaper, Digital Twin: A Primer for Industrial Enterprises.