He has two years’ experience in copywriting, translation and proofreading. His goal is to help people communicate in a concise and understandable way. Rafał is an archaeology graduate who’s fascinated by both prehistoric and modern technologies.
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As the decision-making processes continue to become increasingly data-driven, organisations seek new ways of leveraging data to support their business. One of these options is the implementation of a digital twin — a detailed, virtual model of an object based on real-life data. Building it requires IoT sensors, strong data and analytics, and integration tools, which all contribute to the authenticity and usefulness of the model.
A digital twin can be a representation of a whole enterprise or just a specific part of it. These solutions have incredible potential, as they can help identify potential risks and find ways of mitigating them. They can offer significant advantages in specific industries, such as manufacturing. As much as 37% of the manufacturing companies surveyed by Gartner responded that they’re already piloting or implementing this technology in their operations.
However, early digital twin initiatives often fail to deliver according to the business stakeholders’ expectations. According to Gartner, the reason for them underperforming doesn’t lie in the limitations of the technology itself, but in the lack of organisational readiness and a suboptimal implementation process. In this article, we’ll go over the critical best practices of a digital twin project, and provide recommendations on how to prepare your organisation for this initiative.
Prepare Before Starting the Digital Twin Implementation
The lack of sufficient preparation is the downfall of many attempts at implementing digital twins. This inadequate amount of prework can result in misalignment of expectations and poor execution of the implementation phase.
Gartner recommends as long as six to twelve months of preparation work before launching implementation. This time should be devoted to defining the scope of the project, setting milestones, and creating cross-functional teams for specific elements of the process. It’s also crucial to set clear and realistic targets that will be rooted in the realm of business and create an alignment between business-driven use cases and technical teams responsible for implementation. Covering these steps should prevent some of the most common mistakes and rushed, ill-considered decisions.
Designate a Sponsor to Drive the Project and Educate the Company
The entire process of implementing a digital twin will require the participation of multiple people from different areas of your organisation. However, especially in the early stages of the process, having a single person devoted to driving the business objectives and campaigning for the project can be very beneficial.
This person will be responsible for securing the funding and retaining the correct focus and direction of the digital twin initiative. They will also have to keep the management educated and informed about the project and its goals, as well as communicate the requirements to the teams working on implementation.
Start Small
Many digital twin projects fail because they’re overly ambitious and, due to their scale, take a long time to produce any value. You can avoid that mistake by focusing on a specific use case and producing a minimum viable product (MVP) to create a solution that will address this individual problem. This is considered one of the best practices of implementing a digital twin, because this way, you’ll be able to generate tangible business value in a short time and at a relatively low cost.
Additionally, the ability to quickly present the return on investment to key stakeholders within your organisation can facilitate further buy-in into the digital twin initiative.
Ensure modularity and extendibility of your digital twin solution
While starting with a singular use case is a great practice, you need to remember about the possibilities of growing your digital twin project. Implementing the modular approach and building compatible, interoperable solutions will help you support the gradual adoption of this technology across your organisation. Starting small is definitely the right approach in most cases, but the ultimate goal is to scale up the solution at a high pace and leverage the full potential of the digital twin technology.
Avoid labelling it as an IT-only project
The lack of interest from business stakeholders can be a killing blow to any digital twin project. The core of the problem is the fact that many companies treat this as an IT-only initiative. Gartner even suggests that using the ‘digital twin’ in the company communication can inhibit the project’s success.
Essentially, you need to do everything in your power to keep the business stakeholders involved in this project. They’re necessary to ensure the correct focus of your actions and that the digital twin actually produces insights relevant to your business. Without them, you’d face a real threat of creating a digital twin that’s an interesting technical exercise but has next to no significance to your company’s profit margins.
Enable easy access to digital twin insights
The lack of accessibility to produced insights is another way of turning your digital twin project into an exercise without tangible business value. Even the most intelligently devised data project won’t support your company if the relevant people can’t obtain the insights it produces. According to Gartner, decentralisation and pushing the strategic decision-making to lower levels of organisations is a common trend that continues to accelerate.
Digital twins can make that happen, but only when they’re implemented effectively. In addition to an organisational architecture which provides all the appropriate stakeholders, you should also consider the different needs of different roles. Providing the data at the right level of abstraction to everyone who needs it is a crucial success factor of a digital twin project. Moreover, you should continuously evaluate if organisational changes cause the emergence of new potential user groups, and make sure you’re responding to these needs.
Conclusion
Digital twins can be extremely powerful tools in the portfolio of many organisations. However, due to their high complexity, these projects can be difficult to plan and implement effectively. Some of the most important best practices of digital twin implementation include strong preparation, both in terms of planning the actual initiative and assigning roles, as well as expectation management and education. Moreover, it’s crucial to stay focused on use cases and the business value they’ll provide while also constantly evaluating and staying open for new user groups as you continue to scale up.
In order to successfully execute such a demanding data project, you might want to team up with an experienced technology partner. Visit our data & AI page to learn more about Objectivity’s expertise and approach.
References
Digital Twins Drive Next-Generation Digital Operations, Gartner, 2022
5 Keys to Unlocking Business Value From a Digital Twin, Gartner, 2020
21 Lessons From Successful Digital Twin Implementations for Manufacturing, Gartner, 2021
He has two years’ experience in copywriting, translation and proofreading. His goal is to help people communicate in a concise and understandable way. Rafał is an archaeology graduate who’s fascinated by both prehistoric and modern technologies.
