You may certainly have heard about this many times: the impact of digitalization on asset management, the advantages of the 4.0 Industry on Reliability, the impulse of the latest digital technologies on productivity, efficiency and profitability, and other similar aspects. Indeed, the implementation of digitalization on the industrial scene has drastically changed the way of working, producing and being present in the market.
At the Association of Asset Management Professionals, we like to share and dive into these aspects that positively impact our daily performance we like to share and dive into these aspects that positively impact our daily work, the work that is performed in plants, industries, production chains, and everything that contributes to improve the performance of organizations and the people that make them up. That is why we thought that this is a good opportunity to reflect on the Digital Twin and how it favors the work within Asset Management.
Digital Twins are central elements of Industry 4.0, along with Augmented Reality, Cloud Computing, Big Data or Machine Learning. The digital twin provides constant information on how projects are progressing and provides solutions on their possible best performance options in the real world, resulting in considerable savings and also minimizing the environmental impact, mobilization of personnel or unexpected expenses. The control and projection obtained thanks to digital twins is unprecedented in industrial history.
Highlights and types of digital twins
In principle, a digital twin is a virtual simulation of something that happens in the real world. There are digital twins for specific equipment, for complex assets, for entire industrial systems, twins about customer behavior and flow, about motion analysis of medical prostheses, locomotion, digital twins about oceans and ecosystems, and so on. In other words, it is possible to create digital twins to analyze almost anything that requires it. In the world of asset management, this is especially important.
According to experts, the four main types of digital twins are:
Component Twins: these are the twins that virtually represent the parts of a system, or the parts of an engine, for example.
Asset Twins: As their name indicates, they are the digital double of two or more pieces of equipment that work within a system and allow us to understand how these pieces of equipment interact with each other.
System Twins: These are the twins that represent both the system and its components, providing performance and workflow data that facilitates managerial, operational, investment, performance, etc. decisions.
Process Twins: To a higher degree than the previous ones, these digitally show the environment in which assets and asset systems operate, also offering behavioral information of the asset components. They are suitable for virtually reproducing large systems such as plants or complex buildings, providing information on both the operation and interaction of all the elements of that system.
Gartner Consultancy identifies three types of Digital Twins:
Organization Digital Twin: uses operational data to understand the management of an organization’s business model and how it manages change or generates more value for the customer. It impacts aspects such as inventory management, customer relationship management, etc.
Composite Digital Twin: monitors and optimizes large industrial ecosystems, such as manufacturing companies, and analyzes and optimizes functions using data coming from other digital twins within the system, such as production planning and execution digital twins.
Discrete Digital Twin: According to Gartner, this type of twin is geared towards monitoring and optimizing resources (people, individual products, pieces of equipment, etc.). It uses a huge amount of data coming from IoT, especially data coming from enterprise asset management digital twins, or those used in field services.
In general, digital twins need other Industry 4.0 tools and technologies, such as IoT or machine learning, to function, since their operation depends on the data they are fed with from the devices that monitor equipment and facilities.
They can be as sophisticated as required, and in their application and development they admit the inclusion of new components that enrich the virtual scenario in which the digital twin works, and where the calculations, analysis and decision making that impact the real world are made. They can be used in new assets and also to upgrade or optimize existing assets.
Advantages of using Digital Twins in Asset Management
Asset management is based on managing the entire life cycle of a piece of equipment, facility or system, so that it lasts as long as possible in the best possible condition and delivers the full value for which it was designed. Reliability and maintenance, as part of asset management, are in charge of applying the strategies, developing the culture, executing the tasks and making the decisions that allow achieving the productivity and operability goals, in a safe environment and with the highest possible return on investment.
With all of the above, the advantages of using digital twins are quite clear. However, to summarize, we could say then that the digital twin allows studying and analyzing in real time the data obtained from the devices that monitor the condition and behavior of the assets. Although it is not possible to anticipate in time to predict how the equipment will behave, through the digital twin we can identify and correct early failures, which will put asset managers on that desired path of economic robustness and operational safety.
Digital twins enable improved decision making, planning and production, identify failures as well as strengths and opportunities, and accurately show process costs.
One of the most fascinating features of the digital twin is that it allows simulating possible scenarios, calculating and analyzing the behavior of equipment under different conditions, and thus anticipating negative scenarios.
In addition to simulating and providing behavioral data, it also learns and evolves, reflecting the changes that happen in its real twin, while keeping historical records, previous equipment data and analysis of evolution and behavior, real-world maintenance data, financial data, performance data and analysis of possible future behaviors. All this information, organized, visual and projected over time, greatly facilitates asset lifecycle management. It is a fundamental tool in the Industrial Digitalization process.
To implement this technology, it will be necessary to hire experts and follow a series of steps, starting with the collection of data from the assets you already have, because this is the information base on which the virtual counterparts will be built. We invite you to learn more about this fascinating technological tool and check out the articles section we have in the Association of Asset Management Professionals, a careful selection of topics that we have available for all members of our growing community of Reliability and Asset Management maintainers and specialists.
