Digital twins are an emerging and fast-developing trend in Enterprise Architecture. By creating a virtualized representation of themselves, organizations can make decisions faster and execute digital transformation efforts faster with a greater understanding of their impact and performance. In this article, learn more about their value in business and discover how Ardoq can be the hub for your digital twin efforts.
Jump to:
- What Is a Digital Twin?
- Why Do Enterprises Need a Digital Twin?
- Digital Twin Benefits
- How Does a Digital Twin Work?
- Types of Digital Twins
- How to Create a Digital Twin
- How Enterprise Architecture Supports Digital Twin Initiatives
- What Challenges Do Organizations Face in Digital Twin Development?
- Can Digital Twin Technology Be Combined with Generative AI?
- Learn How Ardoq Supports the Creation of Digital Twins
- FAQs About Digital Twins
What Is a Digital Twin?
In Enterprise Architecture (EA), a digital twin, sometimes known as a digital twin of an organization (DTO), is a dynamic software model of an organization and how it operates. The digital twin definition comes from its use in manufacturing, engineering, and design, which is a virtual model of an object, a system, or a process. However, the idea of a digital twin of an organization has only come about with recent advances in computing and data processing capabilities, including business process mining.
Why Do Enterprises Need a Digital Twin?
Traditionally, architecture models have been static and difficult to adapt to businesses' changing needs, as they are siloed. With digital twins, however, these models can adapt in real-time, marrying performance to design and allowing businesses to see the performance of their activities and adapt accordingly.
Digital Twin Benefits
The benefits of digital twins for organizations are numerous. A continuous feedback loop between the model and real performance data means the twin always gets updated, leading to more accurate decision-making and faster execution. An accurate simulation of processes allows organizations to experiment risk-free to find optimum ways of working and solutions to important business problems.
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Improved understanding of systems: Experimenting and modeling different outcomes with a virtual system can improve one's understanding of how real systems will perform under the same conditions.
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Better financial perspective: Modeling decisions before making them helps organizations understand financial impact and potentially avoid high risks.
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Can aid governance, security, and risk initiatives: Security teams are traditionally siloed away from IT and Enterprise Architecture. A digital twin lets them see how the organization performs as a system. This overview lets them spot risks and evaluate their impact to decide where to distribute resources.
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Process optimization: Monitoring system performance using real-time data allows for detecting inefficiencies and a greater understanding of dependencies.
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Communication and change management: Organizations can prioritize resources and plan effectively by identifying which teams need to be involved with changes and who is affected.
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Reduced costs: By identifying areas for improvement, organizations can better use resources and allocate spending more efficiently.
How Does a Digital Twin Work?
A digital twin is built by collecting data from across the organization concerning its structure and performance and integrating it into a virtual model. This model allows analysis to produce insights, which may be presented as visualizations for straightforward understanding and communication with stakeholders.
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Data Collection
Depending on the type of digital twin, data is collected from sensors on physical objects or software sources like ERPs, data logs, and business process mapping tools.
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Model Building
This data is used to map and create a virtual model of the organization, including its structure, processes, systems, and people.
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Simulation and Analysis
With this data, organizations can run simulations and analyze the impact of different scenarios. They can also look for optimization opportunities by identifying inefficiencies or forecasting future trends or potential problems.
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Decision Making
The results of this analysis can guide decision-making, identify areas for improvement and potential areas of risk, and determine the best way to prioritize and allocate resources.
Types of Digital Twins
With its history of use in manufacturing and design, many types of digital twin technology exist, and highly specialized models are being developed for a wide range of industries and use cases.
Product Digital Twin
Product digital twins offer a virtual representation of a product over its lifecycle. They combine data from multiple sources to improve product and service design, manufacturing, and support.
Supply Chain Digital Twin
A supply chain digital twin simulates an organization’s supply chain. This can help model supply chain dynamics, allowing analysts to spot abnormal behavior and create plans for certain scenarios, such as bottlenecks or inventory challenges.
Digital Twin of a Customer (DToC)
A digital twin of a customer mimics the behaviors of a customer or group of customers based on their interactions. This model can predict what customers might do to meet their needs better.
Digital Twin of an Organization (DTO)
Digital twins of organizations are simulated representations of an entire enterprise, including its processes, structure, and operations. Providing a complete business overview allows Enterprise Architects to analyze performance and run scenarios to test their business impact. This is generally what we mean when we talk about digital twins in Enterprise Architecture.
How to Create a Digital Twin
Creating a digital twin requires buy-in from key stakeholders. It can require much business time to develop and the input of many different departments across the enterprise. The process starts with a detailed blueprint to guide development and ensure the project fits business needs and use cases.
1. Create a Blueprint
The first step is to decide exactly what you want to create and what to use it for.
Talk to stakeholders to establish the following:
- What use cases require it, and what value will it bring?
- What data will this require?
- How should this be sourced?
- What will the end-state architecture look like?
These are some of the questions you should be asking according to McKinsey.
2. Build the Twin
A project team builds the twin. Data engineers will need to sift through data to ensure its quality. High-quality data can be turned into visualizations and the early use cases most important to the business. Lots of cross-team input will be required, so both technical and business teams should be involved in building the twin using an iterative approach.
3. Boost Capabilities
Once the initial twin has been built, it can mature by adding more capabilities, data, and analytics to support new use cases. AI could provide simulations and prescriptions to make inferences based on the data. A layer of automation could also make adding data less resource-intensive.
How Enterprise Architecture Supports Digital Twin Initiatives
Enterprise Architects help decision-makers understand what changes they could make and their potential effects. The more quickly they can do this, the more likely the organization will be able to achieve rapid innovation. Digital twins provide the perfect partner to Enterprise Architecture because they allow the effects of changes to be simulated and their effects measured. By combining digital twins with traditional EA models, Architects can understand how the business performs as a system using real operational data. This can help leadership make decisions more effectively.
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Visualization and Analysis: EA tools traditionally provide a static view of an organization's systems. Digital twins add a dynamic layer, showing how these systems interact and perform in real-time. This allows for a deeper analysis of complex systems and their interdependencies.
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Impact Assessment: By simulating change virtually using the digital twin, EAs can identify what departments and resources are affected by the change and avoid potential conflicts or bottlenecks.
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Scenario Planning: Digital Twins allow improved scenario planning. Users can test the effects of different scenarios and see how systems would respond, which allows for better risk planning and mitigation.
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Optimization: Data from the digital twin can be analyzed to identify areas of weakness in the systems and processes. This helps to uncover opportunities for improved business performance or customer experience.
Learn more by reading our blog about digital twins of an organization.
What Challenges Do Organizations Face in Digital Twin Development?
Developing a digital twin takes work. It requires a high degree of alignment between business and technology teams and buy-in from senior management who understand the benefits of the time and money that must be spent.
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Digital maturity: To get the most out of a digital twin requires a relatively high level of digital maturity to ensure data quality.
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Getting the data: Data can come from disparate sources in different formats. This could be legacy systems that are difficult to interface with. Data quality must be maintained.
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Advanced technical expertise: This type of project requires experienced tech talent and expertise not all teams may have.
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Lack of standardized frameworks, guidance, and processes: Digital twins are still fairly new and highly specialized, so depending on the type, there may not be guidance for organizations wishing to create one.
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Scalability: Digital twins can be made for one-off use cases, so knowing what to do with them can be difficult once an organization wants to find new use cases.
Can Digital Twin Technology Be Combined with Generative AI?
Theoretically, yes. Just as AI is beginning to be used in Enterprise Architecture, it could be used in various stages of digital twin development, operation, and maintenance.
AI could prepare data for analysis and ensure data quality or, during the analysis stage, infer connections between data sets and create visualizations. An LLM interface could allow the user to issue instructions to the AI on processing the data and running simulations with less effort.
We expect the use cases for AI and digital twins to grow as the technology develops.
Learn How Ardoq Supports the Creation of Digital Twins
By serving as an organization's single source of truth, Ardoq is the ideal starting point for digital twin development and digital transformation efforts. Ardoq can map an organization’s structure and IT systems by integrating information and data from various automated and manual sources.
The enterprise's architecture can be combined with a digital twin of the business’s processes using our joint solution with the Celonis Process Intelligence platform. Together, the two solutions can enhance decision-making and accelerate transformation.
Find out more about how Ardoq can help you produce a digital twin of your organization and get started producing insights by booking a demo.
FAQs About Digital Twins
What Problems Do Digital Twins Solve?
Digital twins improve time to execute by providing an advanced understanding of the effects of decision-making. They also remove some risk by allowing organizations to simulate the effects of different changes without affecting the live environment. This reduces risk and allows organizations to ensure resources are deployed where most needed to support change efforts.
How Do I Build a Digital Twin, and What Data Is Needed?
The data required to build a digital twin depends on the type of twin required. Some of the main data types include descriptive data, configuration data, operational data, financial data, customer data, and event logs. However, these may only sometimes be required in some instances. For example, process optimization will rely heavily on operational data, whereas something more product-focused could rely on performance and customer data.
How Are Digital Twins Managed?
Managing a digital twin requires high cross-department collaboration between tech and business teams. The project's direction will depend on alignment with leadership to ensure the twin meets the use cases that provide the most value to the business and align with the overall strategy. A feedback loop should be established to ensure the model evolves to use insights from its operation and continues to meet organizational needs.
