Key Strategies for Successful Implementation of Digital Twins

In .Data & applied AI, Blogfest-en by Baufest

By Ariel Martín Bensussán, Software Head Architect at Baufest

Thursday 13 - June - 2024
mano toca inteligencia artificial, datos digitales y tecnología futura.

In today’s digital era, the implementation of innovative technologies such as Digital Twins is gaining significant traction in the corporate world of large companies, as various reports suggest.

In this McKinsey report, for example, it is stated that 75% of companies have already adopted this technology, and it is expected that by 2027 the global market value will exceed $73.5 billion.

On the other hand, according to the Fortune Business Insights report on the growth of Digital Twins, this market is projected to exceed $259.32 billion by the end of 2032.

These tools offer the ability to create hyper-realistic digital models of complete physical environments and end-to-end operations/processes, allowing companies to boost their efficiency, enable informed decision-making, and optimize business processes.

This relevance can be seen in this McKinsey article, where a survey conducted among senior executives shows that 86% of respondents said Digital Twins were applicable to their organization, and around 44% indicated they had already implemented this technology, while 15% planned to do so in the short/medium term.

With this outlook, it becomes imperative for companies to adopt and implement these technologies, or at the very least, consciously evaluate them.

This is why at Baufest, we want to share our vision on how to approach a project of this nature through 7 keys that will enable a successful implementation of a Digital Twins adoption project.

Define clear objetives and goals

Before embarking on a Digital Twins implementation project, it is essential to define clear objectives and goals for all involved in the project, aligned with the company’s business strategies.

It is crucial to ask why and for what purpose we want to build a digital model, and then set expectations about what we expect it to solve or help us with once the Digital Twin is deployed.

Integrate into the corporate digital strategy

The investment (time, effort, and financial) in developing a Digital Twin must align and integrate with the company’s digital strategy.

This will ensure not only an efficient implementation but also support from key areas within the organization at different stages of the project: planning, design, construction, and commissioning.

Have a clear future readmap

Success will not be determined solely by the results of the first implementation; the success of such projects lies in their medium- and long-term work plan.

For this, it is crucial to have a roadmap that describes how the Digital Twin will evolve in the future. This includes determining whether it will be an independent digital model or if it will be part of or integrated with other models, systems, or processes within the organization as part of a much more complex model.

These first three recommendations will allow us to correctly align the project’s goals over time and focus efforts on achieving the objectives.

Model to the right extent

Determining the appropriate scope of the Digital Twin is crucial to avoid both over-modeling, which can cause the project to fail due to extreme complexity, and a poor or overly simplistic model that does not meet the initial expectations or goals.

Evaluating the complexity, breadth, and depth of the model will help find the right balance, focusing on the business objectives and project goals.

Iterate the digital model

To ensure an effective and progressive implementation of the Digital Twin, it is essential to plan the construction of the model iteratively and incrementally over time.

For this, it is necessary to have concrete milestones to achieve in each phase and clearly defined and communicated success criteria at all levels of the organization from the initial stages of the project.

Feed with relevant data

Providing the Digital Twin model with relevant and quality data is essential to later generate valuable results that provide effective support in the decision-making processes where it will be used.

Data integration must align with the objectives sought with the model’s construction, as well as the metrics and simulations to be evaluated.

Having too little data or irrelevant data can lead to unhelpful simulations as they will not correctly reflect reality, while an excess of data can overwhelm or bias the model.

Have a qualified personnel

Finally, but no less important, having qualified personnel or considering hiring technological experts to implement these types of projects is crucial for success.

Although technology is advancing and becoming more accessible day by day, creating a Digital Twin remains a complex process that requires specialized technical knowledge on one hand, and knowledge of the processes and businesses being modeled on the other.

At this point, the correct integration of all involved as a multidisciplinary team is necessary to achieve the objectives.

A successful implementation of Digital Twins requires careful planning, clear objective definition, consideration of technical and strategic aspects, and the availability of qualified personnel integrated with process and business experts.

By following these recommendations, companies can create cutting-edge digital models and maximize the potential of Digital Twins to optimize their operations and make better decisions based on more accurate data to monitor their current operation and also run simulations/projections on their future operation in the digital model without affecting the normal operation of the physical environment.