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The latest manufacturing trends in Industry 4.0 and how our customers are implementing them with our help.

May 12, 2022

Hyperautomation and digital twins: complementarities and perspectives

Companies now understand that they need to integrate their intelligence to remain competitive and deliver an optimal customer experience while enabling employee development. Access to Big Data for applied analytics provides insights and visibility that fuel new business models.

As part of Transformation 4.0, the benefits of this integration have inspired the migration to hyperautomation. This irrevocably implies the adoption of digital twins.

This article describes the two concepts of hyperautomation and digital twins and explores the synergy between them.

What is hyperautomation?

Hyperautomation is a data-driven automation rather than process-driven. Through a combination of artificial intelligence, machine learning, natural language programming and predictive analytics technologies, hyperautomation represents the collection of tools that act as technology enablers. Taken together and ideally integrated, they offer the potential to automate large parts of end-to-end workflows.

Since no tool can replace humans, hyperautomation involves a combination of tools that guarantee more AI-driven decision making. – Gartner

As such, it pushes companies to go further in the way they approach business optimization. In particular, between departments that are not usually connected, hyperautomation provides the opportunity to make processes transparent and the management more holistic.

Hyperautomation has made its way into Gartner's top 10 strategic technology trends for 2020 and 2021, promising to improve efficiency and reduce operational costs for businesses. The term is used to emphasize the fact that it is not one, but many automation technologies that work in congruence to consolidate or replace human capabilities.

Characteristic synergy of hyperautomation and digital twins

As hyperautomation is the totality of automation efforts in an organization, we can observe a similar industry trend to that of digital twins. In fact, pushing hyperautomation within the company often leads to the creation of a digital twin. It allows organizations to visualize how functions, processes and key performance indicators interact to generate value. This digital twin then becomes an integral part of the hyperautomation process that provides continuous, real-time intelligence and drives business opportunity relevance.

Hyperautomation also facilitates the creation of digital twins. It is essentially the virtual replication of an entire organization. Indeed, if you create a digital representation of your equipment, or your services, hyperautomation requires that you do the same for each element of your organization.

In this way, your processes will be able to modify themselves and identify when other related processes are going wrong or underperforming. Theoretically, your processes should be able to show the past, present and possible futures of each system. In this light, hyperautomation presents itself as a digital twin that engages companies in the natural accumulation of quality data.

Evolution of digital twins through the stages of automation

Digital twins involve both IT and operational systems that are traditionally managed and operated by separate teams within a company. The study of the link between IT and operational technology involves the hyperautomation of processes. This means that we can describe digital twins in the context of automation maturity.

Phase 1: Reflection

In this phase, the digital twin automates the visualization, monitoring and analysis of the physical twin's data in order to obtain real-time information. Traditionally, it is this aspect of the digital twin that decision-makers think of first when they mention 3D images of plants and equipments capturing information on a variety of systems such as infrastructure, transportation, air quality, etc.

Phase 2: Learning

In this phase, the digital twin aims to automate thinking. It is the result of model-based reinforcement learning. The digital twin starts with a simulation model that can be built either by introducing first principles or via an approach based on the data collected in the first phase. The simulation model then generates training samples (data records) which are used to train a machine learning model. The training samples consist of environments, actions and rewards (or value functions) to learn which action lead to which reward/value/outcome.

Phase 3: Taking action

The trained model learns an optimal policy to provide a recommendation of the best action in a given state of the environment. This allows making inferences about the actions to be simulated without executing them in the real world. In this phase, the digital twin automates the execution of the best actions by leveraging the knowledge of the learning phase. Then, it re-integrates the predictive model into the physical environment. This can often take the form of deploying the solution in an IoT environment.

Examples of expected impacts of the hyperautomation/digital twin combination

1. By creating a digital twin of a hospital, managers will be able to assess, for example, the impact of a change in staffing or ward layout. They will be able to anticipate whether a change in one department will create a bottleneck in another, without any impact on patients and staff.

2. Another example is the optimization of physical machines. This equipment is equipped with a large number of sensors that can detect several parameters (temperature, vibration, pressure, etc.). Hyperautomation usually involves processes that are triggered according to monitored thresholds. If pressure becomes too high, a notification is sent to maintenance. If vibrations are slightly out of range, a maintenance ticket can be created automatically.

3. Hyperautomation also has enormous potential for the factory of the future. The combination of advanced technologies will not only help unlock factories, but also meet new sustainability requirements. Hyperautomation depends entirely on the existing digitalization in place. Production machines must provide relevant information about their status and use. Hyperautomation comes into play once you incorporate all this information into the wider framework of an organization.

4. Another important use case is the identification of business risks. Since hyperautomation provides a holistic view of an organization, companies will be able to develop a deep understanding of how they operate and simulate scenarios to identify where their organization is no longer scalable.

5. Hyperautomation will also bring more job satisfaction. In addition to business productivity, hyperautomation helps employees find a balance by automating tasks and giving human resources time to focus on higher value-added activities.

Challenges to widespread adoption of hyperautomation

The potential of hyperautomation is, therefore, clear but its widespread adoption will not be easy. Firstly, there is a global skills shortage to contend with. Technologies often advance faster than the pool of people to deliver them. Companies will be constantly looking for innovation talent and the skills required to succeed in the new era of manufacturing will be very different.

IDC predicts that 75% of the world's organizations will be completely digitally transformed by 2027, and the rest will go out of business.

On the other hand, since hyperautomation is a collection of tools, technologies and even practices, there are barriers to understanding the options available and how to bring these options together to create solutions. This means that organizations must first understand the problems they are trying to solve with hyperautomation, then navigate the landscape of tools and technologies to choose the way that suits their purpose. The amount of work required to realise the full potential of hyperautomation may put many companies off. But they need to think about what they want to achieve and how long they are prepared to wait to get there. Once you have a 100% accurate digital twin of your entire business, this data will confront you with some truths that may be fundamental to the sustainability of your organization.


Hyperautomation can improve processes through more efficient automation. In particular, it can help provide a tangible and documented understanding of organizational processes. It can also create a working environment where human resources have digital assistants to perform manual and tedious tasks. One of the most relevant benefits remains the ability to have digital twins to test organizational changes. Organizations create virtual representations of entities to support their business objectives. This synergy imposes challenges but is worth the effort as it results in maintaining competitiveness.

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