In today’s business world, improving production efficiency is a top priority for organizations of all sizes and industries, driven by an increasingly competitive environment, changing economic environments and consumer expectations. In this context, digital tools play a key role in determining ways and means to achieve optimal results in production operations.
Manufacturing, as one of the fundamental branches of human activity, is constantly exposed to various external and internal factors. In today’s world, enterprises and organizations face multiple challenges such as climate change, the growth of the global population and increasing consumer expectations. Solving these problems requires new approaches and innovations, and in this context, digital technologies are being introduced into the business process to improve production efficiency and ensure the sustainability of industries.
Digital twins, or virtual models of physical objects and processes, represent one of the most advanced and innovative technologies that can transform production. Their use opens new horizons for monitoring, analyzing and managing production processes and resources. The use of these tools is especially relevant in a rapidly changing economic environment. Decisions based only on intuition and past experience can cause significant losses, given the constant emergence of new factors. These tools provide companies with the opportunity to take these new factors into account and make better decisions.
In general, the toolkit of digital technologies for improving production efficiency is an integral part of successful management in modern conditions and can serve as a key factor for achieving competitive advantages and sustainable growth.
In a study of methodologies for assessing the effectiveness and effectiveness of the digital twin in an industrial enterprise, various methodologies aimed at measuring the effects of digitalization were considered. The analysis examined the tools offered by these methods, which allow assessing the impact of the digital twin on various aspects of an enterprise’s activities. Such tools can include the collection of performance data, the analysis of changes in processes and improvements in performance, and the evaluation of economic performance.
Events related to the pandemic in 2020 left their mark on all spheres of activity of enterprises and people. This was not bypassed by digital technologies. Some domestic and foreign authors refer to these economic events as a “breakthrough” against the background of the crisis of the economy. Thus, the development of Industry 4.0 has led to the emergence of the term VUCA-world (abbreviations from the words volatile, uncertain, complex and ambiguous), which in post-cove times is replaced by the term BANI-world (from the words fragile, alarming, non-linear and incomprehensible).
As a result, in response to these measures, the domestic economy launches an import substitution program, which should lead to the development of economic activity to increase the innovative activity of enterprises, as a result of these events, the need for a digital approach to technology has significantly increased. A strategic approach to the introduction of digital technologies in the production process will undoubtedly yield results, including in unstable environments, in the form of increasing economic and social efficiency. The subjective approach that we use in our research shows that knowledge, skills, hard skills and soft skills are interrelated. It affects the way we do business and organize; digital coordination and subjective approach create new business models with competitive advantages; it also sees the need for a professional approach from management accounting and marketing; one of the latest, but not the most recent, technologies is the technology of creating a digital twin. It appeared in 2003, when Michael Greaves introduced it in his work on the product life cycle, and later became firmly entrenched in the high-tech industry.
The main applications of the digital twin technology in various sectors of the economy are the processes of design, planning, optimization, maintenance, security, decision-making, remote access and training, which can be used by various enterprises and organizations and aimed at improving competitiveness, efficiency and productivity. It is also necessary to create methodological tools using digital technology, which will allow more fully and at all levels to carry out the relationship between reality and virtual vision, create a holistic picture of the physical object and all indicators of its activity, can predict the sequence of actions or the results of scenarios.
The global digital twin market is estimated to be worth $5.1 billion in 2021 and is expected to grow in the years to come (Figure 1). The pandemic has changed the way we look at manufacturing, trade and service, which has accelerated the adoption of the technology. It is therefore important to understand and assess the implications of the digital twin in which it is used. The digital twin may not only target processes and phenomena, but also production and business as a whole. The underlying management accounting scenarios will allow for sound and cost-effective management decisions. An integral part of the toolkit is analytics, because the ever-changing external environment generates new factors. The overall digital model uses artificial intelligence and big data, as it digitizes organizational and production processes, assets and liabilities of enterprises, employees, interactions with counterparties and fiscal authorities. The use of such tools involves fundamental investments in skills, projects, infrastructure, people, machines and organizational processes. And this is a combination of the technical infrastructure, on the one hand, and the skills in the organization to implement the strategy, on the other.
Methodical tools, using digital technologies, improving the efficiency of the enterprise should have the following characteristics: forecasting, universality, systematization, socialization, development (Figure 2).
Digital twins are proving to be effective across industries, especially in improving manufacturing processes. One of the key functions of digital twins is to create virtual replicas of companies’ smart enterprises, which can identify problem spots in components, systems, processes and other assets. Through digital twins, companies can also test potential solutions, simulate interactions between components, and predict stochastic changes that may occur during operations. Such simulations allow organizations to save time, resources and money that were previously required to conduct practical tests of working hypotheses.
Digital twins play a key role across industries, providing a wide range of benefits and unique opportunities. First, they enable virtual modeling and simulation of design and production processes. Using digital technologies, you can create an accurate virtual replica of an object, including its components, systems and interactions between them, allowing you to test and optimize the object, test different scenarios and analyze their impact on performance and safety. This approach reduces the time and cost of developing new management solutions and allows you to quickly bring innovative and reliable products to market.
Second, digital technologies play an important role in managing the life cycle of an object, enabling real-time tracking of condition and performance using sensors and data collection systems, enabling manufacturers and management centers to obtain job information, identify problems, and prevent possible production downtime and failures. Digital twins can also help plan routine maintenance and provide personalized support. The formation of economic effects from the introduction of digital twins can have various directions that directly affect the increase in revenues and reduction of costs of the enterprise (Figure 3).
The introduction of digital twins leads to organizational changes that accelerate the process of getting to market. One of the advantages of using digital twin technology is reducing the time required to introduce new products or services to the market. This is due to the fact that digital twins allow virtual simulation and testing of products or processes before a physical instance is created or put into production.
The use of digital twins significantly reduces the time previously spent on physical testing, process tuning and optimization, and results analysis. With the ability to create virtual prototypes and simulations, companies can accelerate the development cycle, improve product quality, and respond quickly to changes in market requirements.
Moreover, reducing the time it takes to bring a product to market has a positive impact on the competitiveness of organizations and creates an enabling environment for successful sales in the future.
- Reducing labor costs and reducing the cost of production;
- Expansion of production capacities and increase of production volume.
Of course, there are high-tech, mass-manufacturing industries that are embracing digital technologies and using their methodological tools extensively. In a highly competitive environment, enterprises are constantly looking for new solutions to attract customers. Various companies are actively using digital twins in the automotive industry. For example, Maserati is partnering with Siemens to better meet customer requirements and reduce time to market. With Siemens software, Maserati has created a digital twin of the Ghibli model that matches the original. This has significantly reduced the cost and development time of the car by 30 percent, with Mercedes-Benz (Daimler), which will commission Factory 56 in 2020, using Internet of Things (IoT) technology, high-performance WLAN and 5G networks to share data between robots as part of flexible manufacturing.
Mercedes-Benz has also leveraged the MO360 digital ecosystem, which integrates information from a variety of production processes and IT systems from around the world.This ecosystem enables optimisation of production management based on key performance indicators (KPIs). Thanks to the flexibility of production, Mercedes-Benz can quickly switch to different car models, adapting to changing demand. These technologies have already led to a 25% increase in efficiency compared to the old S-Class assembly line. Companies such as Maserati and Mercedes-Benz are showing examples of successful adoption and use of digital twins in the automotive industry. These technologies reduce costs, increase efficiency and improve the competitiveness of companies in the market. Mass-consumer manufacturers have also begun to actively use digital technologies to optimize their operations. At a conference on digital twins, in the Netherlands, an interesting paper was presented on the creation of a digital twin electric razor company Philips. This digital twin not only simulates the normal functioning of a device, but also includes situations where a razor falls from the hands of the user. To make the device more resistant to such “emergency” situations, developers conduct virtual drop tests and crash tests. Unilever, a global leader in food and household products, also uses digital twins to improve the efficiency and flexibility of its production processes. The company has created virtual models of its factories around the world. IoT sensors installed in each enterprise transmit real-time performance data, such as engine temperature and speed, to cloud storage. Using analytics and machine learning algorithms, digital IoT twins simulate complex “what ifs” scenarios to determine optimal operating conditions. This helps manufacturers use materials more efficiently and reduce waste that fails to meet quality standards.
The use of technology has enabled Unilever to achieve the following:
- Improve product quality: Unilever PLC delivers 40 percent improvement
For the first time using the concept of a digital twin. - Higher production efficiency: Unilever PLC reduces time spent
For their production customers, by 20%. - Increased profitability: Unilever PLC noted that the use of digital twins
It helped the company raise its annual rate of return to 13 percent.
Unilever currently operates eight digital twins in North America, South America, Europe and Asia.
The term “digital twin” has also begun to be used in finance to model purchasing behavior and resource allocation, including personal finance and borrowing. One example of the technology’s use in finance is the PwC study, where the authors suggest using digital twins to formulate financial recommendations to clients, taking into account socio-demographic, behavioral, financial and health factors that influence their preferences.
Digital twin technology gives asset managers and insurance companies more power to understand customers’ needs and anticipate the changes that may occur in their lives. Digital technologies and their toolkit help develop optimized and personalized financial management strategies for each client. For example, to predict a client’s post-retirement spending needs, a financial adviser needs to consider each client’s specific financial, social and health aspects and predict their future changes.
In the course of the analysis, it was revealed how the introduction of digital twins has an impact on a number of
Key business processes of the enterprise.
These processes include:
- the main business process related to the production of goods or services;
- business process of sales responsible for the sale of products;
- the business process of bringing products to the market, which includes marketing and distribution
Limitation activities; - business process of developing new products, including research and development of new products
ideas and concepts; - business process of equipment maintenance management, including technical
maintenance and repair; - business process of personnel training aimed at improving skills and development
skills of employees.
The above examples of digital twins have shown many business benefits, and digital twins are increasingly available even in smaller organizations, largely through rapid improvements in modeling capabilities, the proliferation of IoT sensors, and the availability of tools and computing infrastructure.
The results of the research represent methods and tools for improving the efficiency of enterprises, and the applicability of methodological and digital tools is reflected in all sectors of the economy. In addition to the positive aspects described, of course, there are problems. The convenience and complexity is that changing one parameter entails changing all other indicators and data, and it is a continuous process with many changing methods, while reducing errors, smoothing uncertainties and improving efficiency. There’s a complex vision of decentralized governance when you have separate responsibility centers, and the novelty and lack of technology makes it difficult to see the full potential of the toolkit, and there’s a problem with software and human resources, but there’s also the complexity of standardization and regulatory and security.
It should be said that leading universities and research institutes are developing digital twins, and this is where the question of human resources, technology and engineering is addressed. But by projecting and scaling this toolkit, the technology faces the problems described above. Therefore, we need a broad view and a focus of methodological tools on improving the efficiency of enterprises. As a result, we see that expanding the methodological and digital tools to improve efficiency and global competition is very important today. The focus is on performance in the relationship between business processes at the digital twin level with big data and artificial intelligence. Modeling such systems is difficult, but the use of flexible digital tools will make it easier to make the right economic decisions and controls, they will allow detailed and multiple assessment of scenarios to test potential tactics and strategies, and will provide companies with the ability to determine the best decisions and actions based on evidence.”
Over the past few decades, the concept of digital twins has evolved, and only recently have we seen its real impact on various industries and spheres of life. The digital twin is a virtual model or replica of a real object, system or process that contains information about its physical and functional characteristics. Using advanced technologies such as the Internet of Things (IoT), artificial intelligence and cloud computing, DD is becoming a powerful tool for analyzing, predicting and optimizing various aspects of the real world. Within a variety of applications, digital twins have significant potential and offer a variety of benefits in the context of Industry 4.0. Choosing the right type of digital technology and understanding their potential enables the development of tools that facilitate modeling, forecasting, record keeping and troubleshooting. The introduction of digital twins leads to a number of benefits, including cost reduction, improved productivity and improved process quality.
Digital technology and its toolkit make it possible to anticipate possible problems and plan resources more efficiently, resulting in reduced equipment downtime and improved material use. In industry, for example, such technologies allow real-time monitoring and analysis of production data, identifying bottlenecks and optimizing them to improve productivity and reduce costs. However, as a technology, digital twins are still in their infancy and have not yet reached their full potential. Recognizing and solving the problems of digital twins is a crucial factor for the successful application of this technology in various sectors.
Many of the problems associated with digital twins are related to their novelty: the lack of consensus in determining and evaluating value, the lack of standards and regulations, the lack of competent specialists and software. Data security and property rights also require additional attention, since data is the basis of digital technologies. In addition to solving current problems, it is also necessary to anticipate and identify future challenges and problems that will face or create a digital twin as a technology. To unlock the true potential of digital twin technology, a full understanding of its definition, characteristics, benefits, implementations and challenges will be needed to broaden the horizons and achieve the full implementation of digital technologies and their methodological tools in various fields.