Digital twins: why virtual reality is critical to enterprise efficiency

We're increasingly seeing the development of digital twins, so what do they mean for businesses?

This is a contributed article by Lee Beardmore, Vice President & Chief Innovation Officer for Business Services at Capgemini

A digital twin, according to Gartner, is a digital representation of a physical object. It includes the model of the physical object, data from the object, a unique one-to-one correspondence to the object, and the ability to monitor the object. As we enter industry 4.0, the digital twin is fast becoming core to interoperability, enabling machines and people to communicate with each other and foster transparency by creating a virtual copy of the physical world: visibility, predictive, preventive maintenance, and "what-if…" can all be analysed to understand the behaviour of assets by creating scenarios that are difficult to replicate in real life.

Digital twins are already establishing themselves in IoT heavy domains. In manufacturing, for example, they enable planners to gauge the effect of changes in production runs before taking them live to the factory floor. This helps to avoid bottlenecks through problem prediction, increasing efficiency, and reducing downtime.

Let's take a closer look at how digital twins can play a pivotal role in improving enterprise operations and overall efficiency.

The power of simulations

Part of the usefulness of a digital twin lies in its capacity to be modelled on reality but developed in isolation from the real world. This enables an organisation to test scenarios multiple times until optimum performance is reached, both for current and potential circumstances. By being able to simulate radical changes in operations, businesses can test what would have previously not been thought possible, leading to unreachable levels of efficiency as a result. More than that, digital twins also allow organisations to prepare and mitigate worst-case outcomes through these simulations.

Capturing the "as is" metadata of an organisation, its activities, people, and systems, drives a virtuous cycle of mining the business, modelling, and improvement that provide a clear perspective on how things are operating, and helps shape and define a model of the digital twin.

Armed with a digital twin to test out different scenarios, organisations can innovate their physical products, solutions and environments with more certainty, and with greater speed and efficiently. If an innovation works well for a digital twin - and has been scenario tested - organisations can feel more assured as they iterate on a physical asset. As the innovation speed and frequency then pick up, organisations are also able to react and adapt more quickly to the rapidly changing world, leading to competitive advantage.

Turning digital twins into a (virtual) reality

Entering "Twin Worlds" requires a change in mindset to understand how digital systems actually reflect and control the real world. Acceptance of errors, inaccuracies, or latency is replaced by the constant demand for both accuracy and control. It requires a consistent data landscape where governance, trust, and accessibility are core concerns within the fabric of the Twin Worlds. A virtual representation of the real world needs to be built up step by step, incorporating an increasingly better understanding of the key real-world assets and an improved ability to translate them into digital terms.

The key to unlock real value is harnessing, managing and manipulating the rich data of a product. Having a robust engineering change management process can ensure that the Digital Twin maintains built configurations. The other piece of the Digital Twin puzzle is the ability to manage big data, due to the amount of federated data amongst design partners and suppliers, to ensure that as the product evolves so does its digital twin. Lastly, the most challenging aspect of ensuring Digital Twin accuracy is collecting the as-maintained configuration information from operators of the products.

In order to get the full benefit of the digital twin, organisations must view the twin from different perspectives - a product perspective, focusing on the properties of the product in operation; an operations perspective, focusing on insights; and finally a user experience perspective, focusing on how the end user interacts with the product providing deep insights into future designs and innovations. Because data is generated from multiple sources due to multiple IT systems being deployed, some data may go undetected, hence it is important to connect the dots across these perspectives.

To ensure that the extensive amount of data coming from various sources is leveraged, product lifecycle management (PLM) will continue to play a pivotal role here, in acting as the backbone in storing all product insights and driving future innovations. In the age of smart and connected product development, PLM is further evolving and emerging as an innovation platform to enable digital twins.

Ultimately, digital twins have enormous potential to help organisations manage risk, innovate faster and unlock new revenue streams. By helping organisations to predict real-world outcomes and optimise their assets, digital twins can improve a variety of business processes and lead to a competitive advantage.