Close this search box.

How to get started with digital twins

Given the wide applications of the digital twin, how does one get started? Aaron Parrott, specialist leader in Deloitte Consulting LLP’s supply chain and manufacturing operations service offering gives us his answer

A major challenge in undertaking a digital twin process can reside in determining the optimal level of detail in creating a digital twin model. While an overly simplistic model may not yield the value a digital twin promises, taking too fast and broad an approach can almost guarantee getting lost in the complexity of millions of sensors, hundreds of millions of signals the sensors produce, and the massive amount of technology to make sense of the model. Therefore, an approach that is either too simplistic or too complex could kill the momentum to move forward.

Imagine the possibilities

The first step would be to imagine and shortlist a set of scenarios that could benefit from having a digital twin. The right scenario may be different for every organisation and circumstance but will likely have the following two key characteristics: 1. The product or manufacturing process being considered is valuable enough for the enterprise to invest in building a digital twin. 2. There are outstanding, unexplained process- or product-related issues that could potentially unlock value either for the customers or the enterprise.

After the shortlist of scenarios is created, each situation would be assessed to identify pieces of the process that can provide quick wins by using a digital twin. We encourage a focused ideation session with members of operational, business, and technical leadership for expediting the assessment.

Identify the process

The next step would be to identify the pilot digital twin configuration that is both highest possible value and has the best chance of being successful. Consider operational, business, and organisational change management factors in identifying which configurations could be best candidates for the pilot. Focus on areas that have potential to scale across equipment, sites, or technologies. Companies may face challenges going too deep into a specific digital twin of a highly complex equipment or manufacturing process, while the ability to deploy broadly across the organisation tends to drive the most value and support: Focus on going broad rather than deep.

Pilot a programme

Consider moving quickly into a pilot programme using iterative and agile cycles to accelerate learning, manage risk proactively, and maximise return on initial investments. The pilot can be a subset of business divisions, or products to limit scope, but with the ability to show value to the enterprise.

As you move through the pilot, the implementation team should support adaptability and an open mind-set – at any time of your journey, maintain an open and agnostic ecosystem that would allow adaptability and integration with new data (structured and unstructured) and leverage new technologies or partners. While you should want to be agnostic to any type of data sources (for example, new sensors and external data sources), you also need a solution that can support the expansion of an end-to-end solution (from early development to after sales).

As soon as the initial value is delivered, consider building on this momentum to continue the drive for greater results. Communicate the value realised to the larger enterprise.

Industrialise the process

Once success is shown in the field, you can industrialise the digital twin development and deployment process using established tools, techniques, and playbooks. Manage expectations from the pilot team and other projects seeking to adopt it. Develop insights on the digital twin process and publish to the larger enterprise. This may include moving from a more siloed implementation to integration into the enterprise, implementation of a data lake, performance and throughput enhancements, improved governance and data standards, and implementation of organisational changes to support the digital twin.

Scale the twin

Once successful, it can be important to identify opportunities to scale the digital twin. Target adjacent processes and processes that have interconnections with the pilot. Use the lessons learned from the pilot and the tools, techniques, and playbooks developed during the pilot to scale expeditiously. As you scale, continue to communicate the value realised through the adoption of the digital twin by the larger enterprise and shareholders.

Monitor and measure

Solutions should be monitored to objectively measure the value delivered through the digital twin. Identify whether there were tangible benefits in cycle time, yield throughput, quality, utilisation, incidents, and cost per item, among others. Make changes to digital twin processes iteratively and observe results to identify the best possible configuration. Most importantly, this is not a project that should typically end once a benefit is identified, implemented, and measured. To continually differentiate in the market place, companies should plan time to move through the cycle again in new areas of the business over time. All in all, true success in achieving early milestones on a digital twin journey will likely rely on an ability to grow and sustain the digital twin initiative in a fashion that can demonstrate increasing value for the enterprise over time. To help ensure such an outcome, one may need to integrate digital technologies and the digital twin into the complete organisational structure—from R&D to sales—continuously leveraging digital twin insights to change how the company conducts business, makes decisions, and creates new revenue streams.


CTS The industrialisation of IT
CTS - Industrialisation of IT
Related Posts
CTS The industrialisation of IT
Others have also viewed

Germany Energy Efficiency Act demonstrates importance of data centre supply chain collaboration

Following the signing into law of Germany’s Energy Efficiency Act (EnEfG), energy solutions specialist Aggreko ...
Data Centre

Vertiv collaborates with Intel on liquid cooled solution

Vertiv is collaborating with Intel to provide a liquid cooling solution that will support the ...

Generative AI at work: Creating a transparent company culture

The power of generative AI has risen to prominence in the past year. Even for ...

AI-powered computer vision enhances safety in industrial workplaces

RoboK, a startup applying AI-powered computer vision to logistics and industrial workplaces, has announced $2.1 ...