The idea of a digital twin is not new to the space of emergent technologies. However, it has witnessed appreciating popularity as of late, particularly in the healthcare, manufacturing, and automobile sectors. One factor behind why Digital Twins have obtained increasing focus is owing to the fact that the idea was integrated in Gartner’s Top 10 Strategic Technology Trends for 2020, in addition to being included in the list the year before. 

Due to the competitive edge that digital twins can provide, an increasing number of organizations are leveraging digital twins to improve efficiency levels, maximize and streamline optimization, and minimize risks. As an outcome, it’s an idea that leadership should have the low down on with regards to functional nature, in order to go about implementation. Furthermore, the Internet-of-things (IoT) has rendered digital twins more accessible for several organizations, facilitating companies in several industries and sectors to leverage the many advantages this technology confers upon them. 

Digital Twins 101 

Gartner’s definition of Digital Twins is that is “a digital representation of a real-world entity or system.” The idea of a digital twin has long been leveraged in industries ranging from mining to aerospace, mainly to develop simulations. It’s specifically leveraged to develop simulations of windmills and engines. Although, in the previous few years, it has experienced mainstream proliferation and has been leveraged to develop models of various disparate things and systems, frameworks. This evolution is primarily due to the Internet of things and Industry 4.0 and the massive amount of data available at an organization’s disposal as a result of IoT. 

In a wider sense, a digital twin is a digital model that simulates the real-world components and behavior of how an IoT gadget works across all stages of its lifecycle. A digital twin is developed leveraging sensors to gather real-time information from real-world elements. That data is the leveraged to develop a cyber duplicate that can assist teams with an improved understanding, by providing insight for the analysis of real-world things, objects, or systems. 

Data and analytic are driving forces underlying digital twins. Critical elements of digital twins are a model of the real-world object, information which includes identity, context, time, and events, uniqueness, implying that the model is connected to a unique physical object, and the capability of monitoring the object in the physical world, which includes the capacity to query or obtain notifications about particular events or modifications. 

Advantages of Digital Twins 

Digital Twins leverage data, ML, and the internet-of-things to confer systems and enterprises with increased efficiency, therefore influencing improved results. The primary advantage of digital twins is that it furnishes real-time information that can assist with learning, reasoning, and comprehending how things and systems function. It facilitates greater comprehension, better modelling, and optimization of performance of a physical entity over the course of its lifecycle.  

Particularly speaking, digital twins assist in highlighting operational aspects where efficiency is lacking, enabling an extension of the lifespan of infrastructure and tools and devices owing to enhanced maintenance and optimization, resulting in reduced downtimes, and giving R&D teams with a gold mine of critical data that can influence enhanced future designs and developments. 

Owing to the several advantages, an increasing number of enterprises have started leveraging digital twin cloud services with the aim of modelling and simulating infrastructure assets over the course of their lifecycle. Additionally, developing a digital variant of assets in the cloud assists organizations stay on top of changes while making the required modifications with regards to performance optimization. 

Also, as digital twin services become more and more advanced, they have become accessible, user-friendly, and feature increased effectiveness. At the moment, digital twin services enable increasingly sophisticated digital simulations and models that are capable of autonomous learning. These systems facilitate improved insights, easy-to-understand information, and user-friendly dashboards that can be leveraged by teams without the requirement of data experts. 

Digital twin of businesses 

As the functionalities of digital twins become increasingly sophisticated, a corresponding increase is witnessed in the number of industries that are developing these models to assist in enhanced performance, leading to improved business outcomes. As digital twins witness mainstream proliferation, it’s becoming more and more obvious that literally anything can be used as a model for developing a digital twin. While the concept has conventionally been leveraged with infrastructure, physical equipment, or hardware, it’s not restricted to these areas. Provided that anything from a construction to a pair of boots can have a digital twin, it’s not shocking that the concept of a digital twin of organization (DTO) is witnessing increasing prevalence. 

The concept of a digital twin of an organization, conceptualized and developed by Gartner, was fueled by the objective of leveraging a digital representation of an organization to assist in implementing changes or new initiatives. A digital twin of an organization furnishes a virtual model of an organization that leadership can evaluate and alter as required. In its complete implementation, it furnishes a full twin in the context of operations. 

What’s noteworthy is that the information leveraged in digital twin of organization models can be updated on an ongoing basis, which provides enterprises real-time data on how the business is doing, leveraging resources, proactively managing and responding to changes, and meeting client requirements. Obviously, having access to a model like this provides enterprises with a plethora of advantages that give them the competitive edge. 

The reasoning behind why enterprises should develop DTOs 

The obvious first phase in developing a digital twin of an organization is producing a virtual representation of the business that is precise and comprehensive. After a model has been developed, teams can undertake analysis and go about interpreting data to understand more about frameworks, systems, and processes while forecasting problems and spheres of concern. 

For enterprises that are thinking about implementing a digital twin of their organization, it’s a wise decision to begin with a small project and then slowly scale up from that starting point. Notwithstanding the size of the model, the critical components of digital twin of an organization are destination, map, performance, scenario, and decision. These aspects enable the identification of relevant business frameworks in addition to the generation of goals of the DTO, KPIs, a system to go about monitoring performance, and a plan for the implementation of changes to accomplish improved outcomes. 

While developing a DTO, instead of implementation of changes in the actual world, leadership can go about implementing them in a virtual model. As we can imagine, this enables simpler adjustments and changes. Of equal importance, it implies that leadership have the capability to review several options and scenarios prior to implementation of drastic business changes.  

In addition to the importance of influencing changes, a digital twin of an organization also assists with everyday operations. It enables leadership to collect insights regarding how their business is functioning and facilitates identification of regions of inefficiencies or deficiencies that can influence organizational improvements. A digital twin of an organization also furnishes comprehensive historical performance data which assist with strategic planning and optimization of processes. 

Also, as it imparts insight into business objectives and models, a digital twin of an organization can be a critical tool to train new and seasoned employees. Leveraging a digital twin of an organization to train can have the outcome of a more comprehensive understanding of cumulative business operations, its performance targets, it’s organization-wide strategy, and its primary directives. As an outcome, it enables all stakeholders to understand and reach a consensus on organization-wide objectives, and operations, therefore facilitating greater alignment.