Information Technology in Industry

Artificial Intelligence in Manufacturing

Main article: Artificial intelligence in the production sector

Digitalization of the urban industry

Main article: Digitalization of urban industry

In Russia

Main article: Digitalization of the Russian industry

In the world

2025:6 trends in digitalization of industrial production for 2025

Industrial production It is characterized by complex processes influenced by many factors, including supply chain failures, changing customer needs, technological advances, macroeconomic situation and competitive environment. Analysts Gartner have identified six main trends in digitalization in the field under consideration, which TAdviser they got acquainted with in mid-March 2025.

1. Computer Vision Technologies for Worker Safety

Computer vision tools, as noted by Gartner analysts, play an important role in ensuring labor safety and efficiency of production processes. For example, on assembly lines, intelligent video surveillance allows real-time detection of violations of technological operations, determination of the presence of foreign objects and compliance with the correct sequence of assembly stages. Computer vision helps to detect people in hazardous areas and issue appropriate alarms. Such systems also provide predictive analytics: this makes it possible to prevent serious injuries and deaths in enterprises.

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Фото: Freepik

2. Software Specification (SBOM)

This is a list of dependencies, files, libraries and other software elements related to a particular service or infrastructure. By forming an SBOM, the risk of equipment defects is reduced and transparency, control and traceability throughout the software supply chain is improved. This approach allows you to speed up the solution of security problems and troubleshooting software products. According to Gartner, SBOMs need to be synchronized with other manufacturing platforms, such as PLM systems.

3. The concept of "zero trust"

This approach involves a lack of trust in any objects of the organization's IT infrastructure, whether users, devices or programs. As more businesses and governments use cloud and hybrid environments to operate, the "zero trust" model is increasingly in demand. It helps enterprises operate safely and efficiently, even if their employees and corporate data are distributed across many offices, and the company uses different work environments. The model provides for authentication and authorization at each attempt to access certain resources.

4. Generative AI

These tools have tremendous potential for automating resource-intensive operations and analyzing vast amounts of information. GeneAI can be used in the field of industrial design, in the formation of technical reports, to create specialized program code and other tasks. AI is able to quickly process information from all kinds of sensors, providing the ability to proactively maintain equipment.

5. Digital streams

We are talking about special frameworks that provide communication, orchestration and presentation of data in systems that affect the product and process. Such streams simplify the formation of digital twins - virtual copies of various objects and products.

6. Hyper automation

Hyperautomatization tools integrate digital technologies such as AI and robotics with IT systems and engineering platforms to perform complex workflows. This is a business-centric approach that organizations use to quickly identify, validate, and automate as many business and IT processes as possible.^[1]

2024

Growth in the global VR technology market in industry to $6.78 billion

At the end of 2024, the cost of virtual reality (VR) solutions in industry amounted to approximately $6.78 billion. For comparison, in 2023, expenses in the relevant area were estimated at $5.83 billion. Industry trends are addressed in the Market Research Future survey published in mid-January 2025. Read more here

Changan opened a self-learning car factory. It is controlled by one computer

At the end of October 2024, Changan Automobile opened an intelligent digital plant with self-learning functions. China Unicom and Huawei took part in the creation of innovative automotive production. Read more here.

2023

The global market for smart manufacturing technologies grew by 5% over the year and reached $1.67 trillion

In 2023, spending in the global smart manufacturing technology market reached $1.67 trillion. For comparison, in 2022, costs in the relevant area were estimated at $1.6 trillion. Thus, growth was recorded at 5%, as stated in the Market Research Future review, published in mid-November 2024.

One of the key drivers of the industry in question is technological advances, including artificial intelligence systems, Internet of Things (IoT) equipment and advanced analytical tools. These solutions help in real-time data collection, monitoring, and analysis, resulting in improved production efficiency and reduced human error. In addition, the information received allows you to make more informed decisions. Companies that implement these technologies are better positioned to improve operations, reduce operating costs, and innovate at an accelerated pace.

At the same time, the use of big data analytics provides enterprises with the necessary information about customer preferences and market trends. Based on this information, marketing activities and strategic planning can be carried out.

Another stimulating factor is the increasing need to improve efficiency and optimization in various industries. Organizations are constantly looking for ways to improve operations, reduce costs and maximize the use of available resources. Plus, there is a growing focus on sustainability and environmental issues, including the need to reduce the carbon footprint. As a result, the introduction of intelligent technologies is accelerating.

The authors of the study identify four key segments in the smart manufacturing technology market: the Internet of Things, artificial intelligence, big data analytics and cloud computing. In 2023, the IoT direction provided revenue of $450 billion, while AI solutions accounted for about $400 billion. Big data analytics brought in approximately $300 billion. The contribution of cloud computing is estimated at $523.36 billion. It is noted that the interaction between these sectors not only stimulates market growth, but also opens up opportunities to increase productivity and further innovation. The list of leading players in the global industry includes:

• Mitsubishi Electric;
• IBM;
• Oracle;
• Emerson Electric;
• Toshiba;
• Honeywell;
• Cisco Systems;
• Siemens;
• General Electric;
• Bosch;
• Hitachi;
• Schneider Electric;
• ABB;
• Rockwell Automation.

Geographically, the leader in 2023 was North America, where costs reached $600 billion: the region dominates thanks to its advanced technology infrastructure and extensive investments in automation. In Europe, spending amounted to approximately $450 billion, while the Asia-Pacific region provided $500 billion. South America brought in $70 billion, and the total contribution of the Middle East and Africa is estimated at $53.36 billion.

Overall, market statistics highlight its sustainability, and the findings point to the continued evolution of smart technology. At the end of 2024, revenue in the segment of smart production systems is estimated at $1.75 trillion. Market Research Future analysts believe that in the future, the CAGR will be 4.56%. As a result, by 2032, costs on a global scale could increase to $2.5 trillion.^[2]

Named 7 main directions of digital transformation of machine tools

In October 2023, the Institute for Statistical Research and Knowledge Economics of the Higher School of Economics published a study in which it listed the seven main areas of digital transformation of machine tools. According to experts, some trends reflect scenarios for the implementation of advanced digital solutions in industry, others are related to practices to improve the functionality and efficiency of certain types of equipment for solving production problems. There are directions, for example, robotic devices that are at the junction of technologies and types of equipment, combining the characteristics of both, the report says.

Experts attribute the advantage of digitalization of the industry to the production of more functional equipment, automation of processes (together with a decrease in the influence of the human factor), an increase in the quality of products and the speed of their production, as well as a reduction in operating costs.

The researchers called the automation of production processes and the introduction of intelligent systems the main direction of digital transformation of machine tools. It is the core of the digital transformation of industry; in the context of machine tools, it implies the transfer of control over operations from a person to automated systems. The final stage in the development of the direction is deserted production, where operators are assigned mainly process control functions.

The second direction is peripheral computing, which, according to analysts, helps to reduce the amount of information supplied to the machine and bypass capacity restrictions. Support for intelligent systems requires powerful processors to handle large amounts of data.

Industrial robots, which are also mentioned in the study, simplify the maintenance of machines, replace their functions, assist in the execution of production tasks (supply parts for processing, replace work tools, etc.).

The next trend is related to additive technologies of high-speed production. They significantly increase the productivity and multifunctionality of the machines. Another area is deep learning. Such algorithms are widely used in industry in general and in machine tools in particular. However, there is a significant reserve for the introduction of advanced digital technologies (primarily based on AI, big data, etc.), capable of detecting hidden dependencies in data, predicting production parameters, and supporting automatic decision-making.

Experts called laser equipment the penultimate key direction of digital transformation of machine tools. It makes it possible to perform more and more complex and high-precision operations, which is especially important for microelectronics. For example, automatic adjustment of laser beam speed and diameter to cut materials reduces power consumption.

Finally, the Institute for Statistical Research and Knowledge Economics of the Higher School of Economics highlights such a direction as equipment with numerical program control. Such machines have greater flexibility and speed of reconfiguration compared to ordinary equipment. Among their advantages are the automation of individual production processes, the ability to release various parts, the production parameters of which are set in programs, etc. A new direction is the implementation of augmented reality applications for reading information about production drawings, assembly and other documentation in 3D format. This can speed up the tuning of the machine by an average of a minute.^[3]

2020: Trends and effects of digital applications in industry

The Institute for Statistical Research economies and Knowledge HSE has identified on the basis of analysis big data the most significant digital technologies already used or implemented in the world and. The the Russian industries institute announced this on August 11, 2021.

In the industry, digital technologies are used at all stages of the life cycle - from concept idea, design, production and operation to service and disposal. Reliance on "digital" provides businesses with significant competitive advantages, especially in the face of uncertainty. Digital technologies played a critical role in 2020, when the most robotic, automated and ready for joint remote work of the enterprise coped with the challenges of the COVID-19 pandemic.

Opening the top 15 industrial robots (No. 1) help reduce labor costs, keep the quality of products at a stable level, and increase the technological flexibility of production. In the Russian industry, robots are most used in the automotive industry, in chemical and petrochemical enterprises.

In the field of artificial intelligence (AI) (No. 2), a leap has been made in recent years from the use of semi-autonomous robotic manipulators on flexible production lines to the control of autonomous vehicles moving in workshops and between workshops. In the future, increasingly advanced AI technologies will fully automate production processes and optimize the work of not only individual enterprises, but entire industries.

In situations in which it is either dangerous or impossible or ineffective to use human resources (for example, to work in hard-to-reach places, in conditions of permafrost or increased radiation, in harmful chemical industries), machine learning technologies are increasingly used (No. 3). They are also relied on when, as arrays of data on the state of industrial equipment accumulate, people are unable to predict its residual life and critical malfunctions, prevent sudden failure and perform maintenance for the state.

For adaptive control of robot operations, solutions based on computer (machine) vision (No. 11) are used. For example, the Philips razor factory (Netherlands) looks like an unlit room with 128 robots installed, the work of which is monitored by only nine employees. Computer vision also helps to monitor personnel actions in terms of compliance with safety requirements. Technologies for automatic fixation and processing of mobile and stationary objects using computer tools are already capable of determining in real time by video or photo image where a person and his body parts (head, arms, legs) are located, and assessing the correctness of wearing overalls (gloves and helmets), and in the near future will bring the work of enterprises to a qualitatively different level.

Multiplies the production efficiency and significantly reduces the payback period of projects implementation industrial internet of things (No. 13). Arrays (big data No. 8), obtained, in particular, from wireless devices with protocol support, IP including, sensors, and smartphones tablets other devices, are used in a wide range of applications. The main ones are market forecasting, product improvement, optimization marketing and sales. Supply chain tracking based on (No. 7), blockchain (No. 12) smart contracts and other electronic transactions, as well as marketplaces, contribute to increased industrial cooperation. Thanks to the study of user experience based on data wearable devices from the enterprise, they move from "repair according to the regulations" to "repair according to the state" and in general develop the service business model "goods as a service" (No. 10).

Designers, manufacturers and engineers use digital prototyping (# 4) to design products and visualize the entire manufacturing process. VR testing (No. 9) allows you to reduce the time and cost of product development, test and improve product quality. So, thanks to the introduction of digital tests of aircraft at virtual training grounds, PJSC UAC managed to almost halve the number of flights for debugging onboard systems.

Enterprises often combine developments of various technological directions. For example, for the accelerated creation and launch of products and services, systems based on "digital twins" (No. 14) of production processes are used, including elements of AI, the Internet of Things, sensorics (5) and wireless communication technologies (No. 6). During operation, such systems help optimize the operation of enterprises, minimize failures and shutdowns; according to OECD estimates, they can predict the response of equipment to loads with an accuracy of 95% and reduce the cost of servicing complex industrial complexes by 5-10%. The annual growth of the "digital twins" market from 2020 to 2026, according to MarketsandMarkets, will be about 60%. Another clear example of combining digital technologies is smart factories (No. 15), fully automated (robotic) production, in which the control of all processes in real time and taking into account constantly changing conditions is ensured by a combination of IoT technologies, Big Data analysis and information systems for managing production and business processes.

Шаблон:Quote 'author=said Nina Tarasova, Director of the Center for Industrial Policy of ISIEZ HSE.

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