Introduction

ecr

Strategic decisions and risk management

Стратегические решения и риск-менеджмент

2618-947X2618-9984

Real Economy Publishing House

10.17747/2618-947X-2022-3-210-225

ecr-1005

Research Article

Статьи

STRATEGY OF DIGITAL TRANSFORMATION OF INDUSTRIAL ENTERPRISES: THE EFFECTS OF THE INTRODUCTION OF SMART MANUFACTURING TECHNOLOGIES

СТРАТЕГИЯ ЦИФРОВОЙ ТРАНСФОРМАЦИИ ПРОМЫШЛЕННЫХ ПРЕДПРИЯТИЙ: ЭФФЕКТЫ ВНЕДРЕНИЯ ТЕХНОЛОГИЙ УМНОГО ПРОИЗВОДСТВА

https://orcid.org/0000-0002-8187-8290

Илькевич

С. В.

Ilkevich

S. V.

Кандидат экономических наук, доцент департамента менеджмента и инноваций, Финансовый университет при Правительстве Российской Федерации (Москва, Россия). ORCID: 0000-0002-8187-8290; Scopus ID: 56028209600; SPIN-код: 6655-7300. Область научных интересов: инновации и бизнес-модели, международный бизнес, цифровая трансформация отраслей, экономика совместного пользования, фондовый рынок, портфельные инвестиции, экономика впечатлений, интернационализация образования.

Candidate of economic sciences, associate professor, Department of Management and Innovation, Financial University under the Government of the Russian Federation (Moscow, Russia). ORCID: 0000-0002-8187-8290; Scopus ID: 56028209600; SPIN-code: 6655-7300. Research interests: innovations and business models, international business, digital transformation of industries, sharing economy, stock market, portfolio investment, experience economy, internationalization of education.

SVIlkevich@fa.ru

Финансовый университет при Правительстве Российской Федерации (Москва, Россия)РоссияFinancial University under the Government of the Russian Federation (Moscow, Russia)Russian Federation

2022

21102022

133210225

2022

Илькевич С.В.

Ilkevich S.V.

This work is licensed under a Creative Commons Attribution 4.0 License.

https://www.jsdrm.ru/jour/article/view/1005

The socio-economic effects from the introduction of smart manufacturing technologies are of significant interest in terms of their generalisation and systematisation at the current stage of the digital transformation on industrial enterprises, as well as the objectives in the context of industrial modernization and new business model development. The proposed systematisation is based on the allocation of three groups of socio-economic effects according to the main direction of their action. The first group of effects primarily leads to reduction in the costs of industrial enterprises. The second group of effects leads mainly to an increase in revenues: some effects to a greater extent in the short and medium term, others in the long term, including through the creation of long-term distinctive capabilities, unique competencies, and sustainable competitive advantages for industrial companies. The third group of effects includes social and economic effects that are broader in focus and have a multiplicative effect, as well as the character of positive externalities (external effects).

As a result of systematisation, the author identified in three groups, respectively, 12, 8 and 13 effects from the implementation of the complex of smart manufacturing technologies. The author stresses the particular importance of studying the socio-economic effects from the implementation of smart manufacturing technologies, since many improvements at the intersection of production and social transformation are currently insufficiently studied. It contrasts to the core production effects, many of which have been studied in sufficient detail by the scientific and expert communities. Systematisation, classification, differentiation and quantitative assessment of various socio-economic effects of the complex of smart manufacturing technologies can and even in a certain sense should (in the context of the tasks to modernise the economy and industries of the Russian Federation) become a separate subject area at the intersection of performance management and smart production.

Социально-экономические эффекты внедрения технологий умного производства представляют существенный интерес с точки зрения их обобщения и систематизации на текущем этапе цифровой трансформации промышленных предприятий, а также тех задач, которые стоят в контексте модернизации промышленности и построения новых моделей бизнеса. Предложенная в статье систематизация базируется на выделении трех групп социально-

экономических эффектов по основной направленности их действия. Первая группа эффектов по основному вектору действия приводит к снижению затрат промышленных предприятий. Вторая группа эффектов ведет преимущественно к повышению выручки: одни эффекты в большей степени в краткосрочном и среднесрочном периоде, другие – в долгосрочной перспективе, в том числе благодаря созданию долгосрочных отличительных способностей, уникальных компетенций, устойчивых конкурентных преимуществ у промышленных компаний. Третья группа эффектов – это более широкие по фокусу воздействия социально-экономические эффекты, имеющие мультипликативное воздействие, а также характер действия положительных экстерналий (внешних эффектов).

В результате систематизации автором выявлено по трем группам соответственно 12, 8 и 13 эффектов внедрения комплекса технологий умного производства. Автор отмечает особую важность исследования социально-экономических эффектов внедрения технологий умного производства, поскольку многие улучшения на стыке производства и социальной трансформации являются в настоящее время недостаточно изученными, в отличие от собственно производственных эффектов, некоторые из которых научное и экспертное сообщества исследовали достаточно подробно. Систематизация, классификация, разграничение и количественная оценка различных социально-экономических эффектов комплекса технологий умного производства могут и даже в некотором смысле должны (в контексте задач модернизации экономики и промышленности Российской Федерации) стать отдельной предметной областью на стыке управления эффективностью (Performance Management) и умного производства (Smart Manufacturing).

умное производствопромышленные предприятияпромышленностьцифровые технологиицифровая экономикацифровая трансформацияиндустрия 4.0киберфизическая системабизнес-моделицифровые двойники

smart manufacturingindustrial enterprisesindustrydigital technologydigital economydigital transformationIndustry 4.0cyber-physical systembusiness modelsdigital twins

Introduction

The smart manufacturing system has become one of the most significant technology complexes within the framework of the general trend of the formation and development of the digital economy. As defined by the US National Institute of Standards and Technology (NIST), Smart Manufacturing is “fully integrated enterprise manufacturing systems that are able to respond in real time to changing production conditions, supply chain requirements, and meet customer needs” [ Merzlikina, 2021]. The concept of "smart manufacturing" can also be defined as the intelligent management and optimisation of business, production and digital processes along the entire value chain in real time [Geerts, 2016]. In another definition, the focus is on the potential for increased productivity: smart manufacturing is a combination of big data processing technologies, artificial intelligence and advanced robotics, interconnected machines and tools used to increase enterprise productivity and optimise energy and workforce [Phuyal et al., 2020a]. The complex of smart manufacturing technologies in the most extensive and enumerative interpretation combines digital product design, analytics, production process, inventory and supply chain system, product customisation, real-time operational process blocks, product delivery system and end customers using cloud computing, which allow to increase production to order and make product customisation and the overall maintenance of the supply and demand ecosystem more efficient [Phuyal et al., 2020b].

A very similar concept (which, in the context of this study, it is advisable to use as a full analogue for the term "smart production") "smart factory" refers to a factory that has reached a level that makes possible the functions of self-organisation in production and in all processes associated with it. The main advantage lies in the mutual complementarity of diversified areas of the production ecosystem, from smart production to smart logistics networks [Strozzi et al., 2017]. Powerful capabilities allow you to perform operations with minimal manual intervention and high reliability in various aspects of the ecosystem, including high values of automated workflows, asset synchronisation, improved tracking and scheduling, optimised energy consumption inherent in a smart factory to increase productivity, uptime and quality. The key capabilities of a smart factory are highly interconnected, transparent, proactive and flexible. This helps in the overall efficiency of the ecosystem supply chain [Odważny et al., 2018].

At the same time, there is a point of view that it is more expedient to consider two sets of technologies - "digital design" and "customised product" as separate components of the development of digital transformation of industrial enterprises beyond the scope of smart production in a narrower sense. The isolation of these two sets of technologies is justified primarily by autonomy (both software and process and organisational), as well as the distinctive features of their implementation and the specifics of the technology commercialisation logic, as well as those specific effects that were identified separately for customisation in the context of the digital transformation of industrial enterprises [Titov, Titova, 2022]. Based on these considerations, in this paper preference is given to a narrower definition of smart production, since it also seems appropriate from the point of view of describing and systematising the entire set of socio-economic effects from the introduction of a set of smart production technologies. A narrower interpretation of smart manufacturing allows us to more accurately define and delineate its effects in the context of the digital transformation of industrial enterprises.

It is also very important to understand the relationship between different sets of technologies and the choice of specific, niche business models by industrial enterprises. So, a monograph edited by A. Trachuk "Transformation of industry in the context of the fourth industrial revolution" presents three distinctive business models: a smart automated plant, a customer-oriented plant, a mobile plant [Trachuk et al., 2018]. It is logical to assume that industrial enterprises focused on the implementation of a set of "smart production" technologies will gravitate towards the "smart automated plant" business model. The success of the business of an industrial enterprise fundamentally depends on the degree of complementarity of the complex of digital technologies and the business model, since inconsistencies will affect the stability and effectiveness of both individual blocks of business processes and the entire strategy.

In industry, value creation processes are changing as information and communication technologies are integrated with manufacturing processes. This change could lead to efficiency gains and new business models. The digital disruption embodied in smart manufacturing is already here and happening faster than many companies thought. Numerous studies have shown that the use of intelligent manufacturing technologies provides the first mover advantage. For example, mid-sized companies that are more digitally advanced grow significantly faster than lagging companies. Producers can get ahead of the curve, capitalise on new opportunities. Studies also show that the relationship between investments in smart manufacturing technologies and the fourth industrial revolution, the results of innovation and productivity growth are non-linear and have a stable positive relationship only after a certain critical mass of investments has been reached [Trachuk, Linder, 2020]. Most companies that do not adapt their business models to the opportunities created by digital technologies will fail [Bughin et al., 2018]. Figure 1 shows a tipping point where there is a sharp decline in the market share of traditional companies that have failed to respond to the challenges of the digital economy. This is partly due to an insufficiently structured understanding by companies of how to relate digital transformation tasks to the transformation of business models [Schallmo et al., 2018]. However, some companies manage to adapt primarily due to the rapid reorientation to niche markets.

The intensive development of production systems based on the implementation of the Smart Production Complex of Technologies at the current stage is mainly carried out by innovative companies, as is supposed to be a model of Rogers’s innovation: innovators (2.5%), the first users (13.5%), early majority (13.5%), late majority (34%), conservatives (16%). Of course, the model of diffusion of innovation is more emphasised by the user aspects from the consumer, and not organisational and informational. Nevertheless, according to the sum of the shares of innovators and early users (16%), this model can be relatively accurately, although in a general sense characterise the current stage of the use of smart production technologies in Russian industry. This is quite well correlated with the data of the study “Digital Economy 2022”, presented in Table. 1 In the context of the use of digital technologies in organisations according to the type of economic activity [Digital economy .., 2022]. Particular importance and interest now are the speed and completeness of the exit to the areas of the early and late majority of Rogers curve. At the same time, the studies noted that the development directions of Russian industrial companies correspond to global trends, however, the pace of implementation of digital initiatives is noticeably lag behind the pace of leading countries - according to various estimates, from 5 to 10 years [Digital transformation of industries .., 2021]. This explains the severity and urgency of modernisation tasks facing Russian industrial enterprises.

The concept of smart production is based on a whole range of advanced and promising technologies of the fourth industrial revolution (industry 4.0), among which, first of all, virtual modeling, big data, cloud computing, artificial intelligence (AI), Internet of things (IoT), connected robotics, predictive analytics, additive manufacturing, etc. [Digital transformation of industries.., 2021]. The diversity of a large conglomerate of smart manufacturing technologies to a large extent predetermines the variety of socio-economic effects from their implementation.

The generalisation of the main trends in the development of production and logistics systems based on the introduction of smart production technologies, proposed by O. Myasnikova (Fig. 2) is a bright example.

However, it is important to note that the pace of digitalisation depends not only on the development of technologies themselves. L. Berg and colleagues pay attention to the aspect of cultural and social transformation, speaking about the pyramidal structure of the digital economy (Fig. 3), where the fundamental layer is a data-based culture, or data-driven culture, which is understood as a culture of willingness to create and share data throughout the value chain [Berg et al., 2020].

The overall socio-economic effect from the introduction of a complex of smart production technologies in industry and in the economy as a whole is characterised by a large and rather diverse set of effects leading to an increase in the efficiency of enterprises, a reduction in many cost groups and an increase in demand for products and revenue, which ultimately affects growth of profitability. The scientific and expert communities have already developed a fairly mature and evidence-based understanding of a number of central effects of smart manufacturing. At the same time, a number of specific and broader socio-economic effects are still receiving insufficient attention even in the leading publications on the digital transformation of industrial enterprises.

Among the socio-economic effects of a complex of smart production technologies, it seems most appropriate to single out three enlarged groups of effects. The first on the main vector of action leads to cost reduction. The second leads mainly to an increase in revenue: some effects to a greater extent in the short and medium term, others in the long term, including through the creation of long-term distinctive abilities, unique competencies, and sustainable competitive advantages for industrial companies. The third group of effects is the wider socio-economic effects from the use of a complex of smart production technologies, which can be generally characterised as having a multiplier effect for the entire economy and the properties of positive externalities (external effects). Such a division into three groups of effects seems appropriate from the point of view of the main focus of effects, however, at the same time, it should be noted that in many contexts, the use of a set of smart manufacturing technologies directly or indirectly affects both costs, and the future sales potential of companies, and multipliers for industry and economy level. The boundaries between individual effects can be somewhat blurred.

The importance of systematising the effects in terms of dividing into three groups of effects lies in a more complete and broader understanding of the potential of a complex of smart manufacturing technologies, which can positively influence the dynamics of the implementation and scaling of both the technologies themselves and the business models interconnected with them. This is important from the point of view of the readiness and speed of decision-making both by the industrial enterprises themselves and by other stakeholders, including those influencing the innovation, technology and industrial policies of the state.

To the group of effects of a smart production technology complex can be attributed the following:

The group of effects that mainly lead to an increase in revenue can primarily include:

The group of broader socio-economic effects from the use of a complex of "smart production" technologies, which can be generally characterized as having a multiplier effect for the entire economy, as well as the impact as positive externalities (external effects), can primarily be attributed to:

Fig. 1. New digital business models are replacing old ones

Source: [Bughin et al., 2018].

https://cdn.elpub.ru/assets/journals/ecr/2022/3/yHYLNTeH6hDrtDJkKgepS6QJXeeQ2i6Ob0U8RKc3.png

Table 1

Use of digital technologies in organizations by type of economic activity in 2020 (% of the total number of organizations)

Source: compiled by the author based on [Digital economy.., 2022].

Undustry	Cloud services	Big Data	Digital platforms	IoT	AI	Robots
Mineral extraction	19.0	21.8	13.2	14.6	2.5	4.2
Manufacturing industry	27.1	26.5	16.0	15.8	3.6	17.2
Energy supply	19.4	23.7	16.6	15.9	3.3	2.0

Fig. 2. Trends in the development of production and logistics systems

Source: [Myasnikova, 2020].

https://cdn.elpub.ru/assets/journals/ecr/2022/3/IPIdopBgoeRER2J8u6PsyU02NukdwcWlBjMLMU9r.png

Fig. 3. Structural representation of the digital economy

Source: [Berg et al., 2020].

https://cdn.elpub.ru/assets/journals/ecr/2022/3/iXpX6okAsjgMtwqvwBUwYcsNqwxnMrOxkRA4i7Op.png

Fig. 4. Prospects for reducing the unit costs of small and medium-sized manufacturers through the reduction in the cost of additive manufacturing technologies

Source: [Mahoney, Kota, 2020].

https://cdn.elpub.ru/assets/journals/ecr/2022/3/BGoCPcKtpL6PwCKHRvWig0QIfZtyA9y6d2QV24NQ.png

Table 2

Systematization of the socio-economic effects from the implementation of smart production technologies

Source: compiled by the author.

Cost reduction effects	Revenue increase effects	Multiplier effects and positive externalities
Reducing the cost of control and monitoring of production processes	Improved understanding of shopping habits and requirements	Growth of the semiconductor electronics and industrial equipment market
Reducing the cost of parts and accessories	Better customer satisfaction	Increasing the level of science intensity and manufacturability of products and services in related industries
More efficient use of production capacity through ecosystem integration	Fast adaptation of products to customer requirements due to production flexibility	Intensification of the applied science development, especially technical and engineering
Reduce downtime, losses and waste	Ability to manufacture small series of specific modifications of products and parts	Changing the structure of employment towards highly skilled jobs
Reducing the costs associated with equipment failure	Improving the quality of products, reducing manufacturing defects	Increasing demand for IT professionals
Reducing reverse engineering costs	Increase in prices and margins of products due to the factor of monopolistic competition	Increasing labor productivity in industry and in the economy as a whole
Optimisation and information integration of supply chains	Reduction for consumers of the total cost of ownership of complex technical products at the digital service stage	Reduction of economic and social damage from non-compliance with safety regulations
Reduced electricity consumption	Increase delivery lead time by providing a more innovative product	Broader transition of industries and sectors of the economy to PaaS business models
Reducing the cost of training highly qualified engineering and working specialists		Increasing the share of medium and small enterprises in the volume of industrial production
Time reduction and cost in R&D		Increasing the investment activity of enterprises
Reduction of sunk costs by reducing the importance of the factor of specific assets		Improving the quality and transparency of management
Reducing the need for working capital		Improving corporate governance and ESG factors in industrial companies
Increasing the environmental sustainability of production

It seems appropriate to present the socio-economic effects of the introduction of smart manufacturing technologies identified above for three groups in tabular form (Table 2.). Some of the extended effect titles have been shortened for tabular presentation.

As a result of the study, 12 cost reduction effects, 8 revenue increase effects and 13 multiplier effects and the nature of positive external effects from the introduction of a set of smart production technologies in industrial enterprises are summarised and highlighted. Of particular importance at present are the areas of research on the socio-economic effects of the introduction of smart manufacturing technologies, since some improvements at the intersection of production and social transformation are currently insufficiently studied, in contrast to the actual production effects, many of which have been studied in sufficient detail by the scientific and expert communities.

It seems that the systematisation, classification, differentiation and quantitative assessment of the various effects of the "smart production" complex can and even in a certain sense (in the context of the tasks of modernising the economy and industry of the Russian Federation) should become a separate subject area at the intersection of performance management (Performance Management) and smart production (Smart Manufacturing). The question of the feasibility and prospects of building a certain composite index of the level of maturity and / or the effectiveness of the introduction of smart manufacturing technologies at the level of industry or individual industries and sectors may deserve special attention.

From the point of view of the state industrial policy, it is important to understand the priority of ensuring the wider use of a set of smart manufacturing technologies. State industrial policy instruments and a favorable institutional environment can help with the rapid scaling of a set of smart manufacturing technologies across a wide range of enterprises in various industries and sectors of the economy.

References1

Багаутдинова Н.Г., Багаутдинова Р.А. (2018). Новые конкурентные преимущества в условиях цифровизации. Инновации, 8: 80–83.

Bagautdinova N.G., Bagautdinova R.A. (2018). New competitive advantages in the context of digitalization. Innovation, 8: 80-83. (In Russ.)

Барвинок В.А., Смелов В.Г., Кокарева В.В., Малыхин А.Н. (2014). Прогрессивные технологии в машиностроении. Проблемы машиностроения и автоматизации, 4: 142–149.

Barvinok V.A., Smelov V.G., Kokareva V.V., Malykhin A.N. (2014). Progressive technologies in mechanical engineering. Problems of Mechanical Engineering and Automation, 4: 142-149. (In Russ.)

Баурина С.Б. (2020). Технологии будущего: умные производства в промышленности. Вестник РЭУ им. Г.В. Плеханова, 17(2): 123–132. DOI: http://dx.doi.org/10.21686/2413-2829-2020-2-123-132

Baurina S.B. (2020). Technologies of the future: smart manufacturing in industry. Bulletin of the Russian University of Economics G.V. Plekhanov, 17(2): 123-132. DOI: http://dx.doi.org/10.21686/2413-2829-2020-2-123-132. (In Russ.)

Глазков Б. (2016). Возможности индустриального интернета. Трамплин к успеху, 7: 24–27.

Glazkov B. (2016). The possibilities of the industrial Internet. Springboard to Success, 7: 24-27. (In Russ.)

Зубрицкая И.А. (2018). Цифровая трансформация промышленных предприятий Республики Беларусь: экономическое содержание, виды и цели. Цифровая трансформация, (3): 5–13.

Zubritskaya I.A. (2018). Digital transformation of industrial enterprises of the Republic of Belarus: Economic content, types and goals. Digital Transformation, (3): 5-13. (In Russ.)

Иванов Д. (2016). Испытательный полигон для передовых производственных технологий. Трамплин к успеху, 7: 14–16.

Ivanov D. (2016). A testing ground for advanced manufacturing technologies. Springboard to Success, 7: 14-16. (In Russ.)

Касяненко Е.О., Шимченко А.В., Салкуцан С.В. (2020). Сравнительный анализ традиционной модели производства и «умных фабрик». Журнал правовых и экономических исследований, 3: 7–17.

Kasyanenko E.O., Shimchenko A.V., Salkutsan S.V. (2020). Comparative analysis of the traditional production model and “smart factories”. Journal of Legal and Economic Studies, 3: 7-17. (In Russ.)

Мерзликина Г.С. (2021). Экономическая эффективность «Умного производства»: от целевых установок к регламентации. Вестник АГТУ. Экономика, 3.

Merzlikina G.S. (2021). Economic efficiency of “smart production”: From targets to regulation. Bulletin of ASTU. Economy, 3. (In Russ.)

Мясникова О.В. (2020). Теоретико-концептуальные подходы к формированию производственно-логистической системы «умного» производства как социокиберфизической системы. Экономика. Управление. Инновации, 1(7): 29–35.

Myasnikova O.V. (2020). Theoretical and conceptual approaches to the formation of the production and logistics system of “smart” production as a socio-cyber-physical system. Economy. Control. Innovation, 1(7): 29-35. (In Russ.)

Тарасова Н.Н., Шпарова П.О. (2021). Топ-15 цифровых технологий в промышленности. https://issek.hse.ru/mirror/pubs/share/494926392.pdf.

Tarasova N.N., Shparova P.O. (2021). Top 15 digital technologies in the industry. https://issek.hse.ru/mirror/pubs/share/494926392.pdf. (In Russ.)

Титов С.А., Титова Н.В. (2022). Оценка экономических эффектов от кастомизации продукции российских промышленных предприятий. Стратегические решения и риск-менеджмент, 13(1): 26–36. DOI: 10.17747/2618-947X-2022-1-26-36.

Titov S.A., Titova N.V. (2022). Estimation of economic effects from customization of products of Russian industrial enterprises. Strategic Decisions and Risk Management, 13(1): 26-36. DOI: 10.17747/2618-947X-2022-1-26-36. (In Russ.)

Трачук А.В., Линдер Н.В., Тарасов И.В., Налбандян Г.Г., Ховалова Т.В., Кондратюк Т.В., Попов Н.А. (2018). Трансформация промышленности в условиях четвертой промышленной революции: монография. СПб.: Реальная экономика.

Trachuk A.V., Linder N.V., Tarasov I.V., Nalbandyan G.G., Khovalova T.V., Kondratyuk T.V., Popov N.A. (2018). Transformation of industry in the conditions of the fourth industrial revolution: monograph. St. Petersburg, Real’naya ekonomika. (In Russ.)

Трачук А.В., Линдер Н.В. (2020). Влияние технологий индустрии 4.0 на повышение производительности и трансформацию инновационного поведения промышленных компаний. Стратегические решения и риск-менеджмент, 2(11): 132–149.

Trachuk A.V., Linder N.V. (2020). The impact of industry 4.0 technologies on increasing productivity and transforming the innovative behavior of industrial companies. Strategic Decisions and Risk Management, 2(11): 132-149. (In Russ.)

Хейфец Б.А., Чернова В.Ю. (2019). Зарубежные программы умной реиндустриализации. ЭКО, 8: 118–140. DOI: 10.30680/ЕСО0131-7652-2019-8-118-140.

Kheifets B.A., Chernova V.Yu. (2019). Foreign programs of smart reindustrialization. ECO, 8: 118-140. DOI: 10.30680/ЕСО0131-7652-2019-8-118-140. (In Russ.)

Цифровая Россия: новая реальность (2017). Digital McKinsey. https://www.mckinsey.com/ru/~/media/mckinsey/locations/europe%20and%20middle%20east/russia/our%20insights/digital%20russia/digital-russia-report.pdf.

Digital Russia: A new reality (2017). Digital McKinsey. https://www.mckinsey.com/ru/~/media/mckinsey/locations/europe%20and%20middle%20east/russia/our%20insights/digital%20russia/digital-russia-report.pdf. (In Russ.)

Цифровая трансформация отраслей: стартовые условия и приоритеты (2021). М.: НИУ ВШЭ. https://conf.hse.ru/mirror/pubs/share/463148459.pdf?ysclid=l2yt4ijpgh.

Digital transformation of industries: Starting conditions and priorities (2021). Moscow, NRU HSE. https://conf.hse.ru/mirror/pubs/share/463148459.pdf?ysclid=l2yt4ijpgh. (In Russ.)

Цифровая экономика 2022: краткий статистический сборник (2022). М.: НИУ ВШЭ. https://issek.hse.ru/mirror/pubs/share/552091260.pdf.

Digital economy 2022: A brief statistical compendium (2022). Moscow, NRU HSE. https://issek.hse.ru/mirror/pubs/share/552091260.pdf. (In Russ.)

Щербаков Г.А. (2022). Глобальный дефицит полупроводниковых компонентов как источник современного кризиса мировой автомобильной промышленности. МИР (Модернизация. Инновации. Развитие), 13(2): 270–287. https://doi.org/10.18184/2079-4665.2022.13.2.270-287.

Shcherbakov G.A. (2022). The global shortage of semiconductor components as a source of the current crisis in the global automotive industry. MID (Modernization. Innovations. Development), 13(2): 270-287. https://doi.org/10.18184/2079-4665.2022.13.2.270-287. (In Russ.)

Alkaraan F., Albitar K., Hussainey K., Venkatesh V.G. (2022). Corporate transformation toward Industry 4.0 and financial performance: The influence of environmental, social, and governance (ESG). Technological Forecasting and Social Change, 175: 121423. https://doi.org/10.1016/j.techfore.2021.121423.

Anackovski F., Kostov M., Pasic R., Kuzmanov I. (2021). The impact of Industry 4.0 on education and future jobs (2021). In: 56th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST): 185-188. DOI: 10.1109/ICEST52640.2021.9483516.

Berg H., Le Blévennec K., Kristoffersen E., Strée B., Witomski A., Stein N., Bastein T., Ramesohl S., Vrancken K. (2020). Digital circular economy as a cornerstone of a sustainable European industry transformation. European Circular Economy Research Alliance (ECERA). https://ss-usa.s3.amazonaws.com/c/308476495/media/19365f987b483ce0e33946231383231/201023%20ECERA%20White%20Paper%20on%20Digital%20circular%20economy.pdf.

Berg L.P., Vance J.M. (2017). Industry use of virtual reality in product design and manufacturing: A survey. Virtual Reality, 21(1): 1–17.

Berg L.P., Vance J.M. (2017). Industry use of virtual reality in product design and manufacturing: A survey. Virtual Reality, 21(1): 1-17.

Bughin J., Catlin T., Hirt M., Willmott P. (2018). Why digital strategies fail. McKinsey Quarterly, Jan.

Caccamo C., Pedrazzoli P., Eleftheriadis R., Magnanini M.C. (2022). Using the process digital twin as a tool for companies to evaluate the return on investment of manufacturing automation. Procedia CIRP, 107: 724–728. https://doi.org/10.1016/j.procir.2022.05.052.

Caccamo C., Pedrazzoli P., Eleftheriadis R., Magnanini M.C. (2022). Using the process digital twin as a tool for companies to evaluate the return on investment of manufacturing automation. Procedia CIRP, 107: 724-728. https://doi.org/10.1016/j.procir.2022.05.052.

Carvalho T.P., Soares F.A., Vita R., Francisco R.P., Basto J.P., Alcalá S.G. (2019). A systematic literature review of machine learning methods applied to predictive maintenance. Computers & Industrial Engineering, 137: 106024. https://doi.org/10.1016/j.cie.2019.10602.

Deac C.N., Popa C.L., Ghinea M., Cotet C.E. (2017). Using augmented reality in smart manufacturing. In: Proceedings of the 28th DAAAM International Symposium: 0727–0732. Vienna, DAAAM International. 10.2507/28th.daaam.proceedings.102

Deac C.N., Popa C.L., Ghinea M., Cotet C.E. (2017). Using augmented reality in smart manufacturing. In: Proceedings of the 28th DAAAM International Symposium: 0727-0732. Vienna, DAAAM International. 10.2507/28th.daaam.proceedings.102

Etz D., Frühwirth T., Kastner W. (2020). Flexible safety systems for smart manufacturing. In: 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): 1123-1126. DOI: 10.1109/ETFA46521.2020.9211905.

Gajdzik B., Wolniak R. (2022). Smart production workers in terms of creativity and innovation: The implication for open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 8(2): 68. https://doi.org/10.3390/joitmc8020068.

Geerts R. (2016). The smart manufacturing elevator pitch, literally! MESA International’s. https://blog.mesa.org/2016/03/the-smart-manufacturing-elevator-pitch.html.

Grenčíková A., Kordoš M., Berkovič V. (2020). The impact of Industry 4.0 on jobs creation within the small and medium-sized enterprises and family businesses in Slovakia. Administrative Sciences, 10(3): 71. https://doi.org/10.3390/admsci10030071.

Guneshka D. (2021). Benefits of product personalization. https://www.iplabs.de/en/blog/blog/benefits-of-product-personalization.

Hinz P. (2013). Effects of mass customization on manufacturing. https://www.adaptalift.com.au/blog/2013-06-17-effects-of-mass-customisation-on-manufacturing.

Jagannathan S., Ra S., Maclean R. (2019). Dominant recent trends impacting on jobs and labor markets – an overview. International Journal of Training Research, 17(sup1): 1–11. DOI: 10.1080/14480220.2019.1641292.

Jagannathan S., Ra S., Maclean R. (2019). Dominant recent trends impacting on jobs and labor markets - an overview. International Journal of Training Research, 17(sup1): 1-11. DOI: 10.1080/14480220.2019.1641292.

Kanawaday A., Sane A. (2017). Machine learning for predictive maintenance of industrial machines using iot sensor data. In: 8th IEEE International Conference on Software Engineering and Service Science (ICSESS), IEEE: 87–90. https://doi.org/10.1109/ICSESS.2017.8342870.

Kang H.S. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing Green Technology, 3(1): 111–128.

Kang H.S. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing Green Technology, 3(1): 111-128.

Kumar M., Shenbagaraman V.M., Shaw R.N., Ghosh A. (2021). Predictive data analysis for energy management of a smart factory leading to sustainability. In: Favorskaya M.N., Mekhilef S., Pandey R.K., Singh N. (eds.). Innovations in electrical and electronic engineering. Lecture notes in electrical engineering, 661. Singapore: Springer. https://doi.org/10.1007/978-981-15-4692-1_58.

Kumar M., Shenbagaraman V.M., Shaw R.N., Ghosh A. (2021). Predictive data analysis for energy management of a smart factory leading to sustainability. In: Favorskaya M.N., Mekhilef S., Pandey R.K., Singh N. (eds.). Innovations in electrical and electronic engineering. Lecture notes in electrical engineering, 661. Singapore, Springer. https://doi.org/10.1007/978-981-15-4692-1_58.

Lage M.J., Filho M.G. (2010). Variations of the Kanban system: Literature review and classification. International Journal of Production Economics, 125(1): 13–21.

Lage M.J., Filho M.G. (2010). Variations of the Kanban system: Literature review and classification. International Journal of Production Economics, 125(1): 13-21.

Lu Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6: 1–10.

Lu Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6: 1-10.

Lu Y., Morris K.C., Frechette S. (2016). Current standards landscape for smart manufacturing systems. National Institute of Standards and Technology, 8107.

Mahoney T.C., Kota S. (2020). Smart manufacturing: Premier for small manufacturers, March. Report MF-TR-2020-0301. www.MForesight.org.

Martin N.L., Dér A., Herrmann C., Thiede S. (2020). Assessment of smart manufacturing solutions based on extended value stream mapping. Procedia CIRP, 93: 371–376. https://doi.org/10.1016/j.procir.2020.04.019.

Martin N.L., Dér A., Herrmann C., Thiede S. (2020). Assessment of smart manufacturing solutions based on extended value stream mapping. Procedia CIRP, 93: 371-376. https://doi.org/10.1016/j.procir.2020.04.019.

Mathai P. (2015). Big data: Catalyzing performance in manufacturing. Wipro, Jan., 8. http://www.wipro.com/documents/Big%20Data.pdf.

Matulik P. (2008). Mass customization. Zlin: Tomas Bata University. http://195.178.95.140:8080/bitstream/handle/10563/6523/matul%C3%ADk_2008_dp.pdf?sequence=1&isAllowed=y.

Matulik P. (2008). Mass customization. Zlin, Tomas Bata University. http://195.178.95.140:8080/bitstream/handle/10563/6523/matul%C3%ADk_2008_dp.pdf?sequence=1&isAllowed=y.

Moon S., Becerik-Gerber B., Soibelman L. (2019). Virtual learning for workers in robot deployed construction sites. In: Advances in informatics and computing in civil and construction engineering. Proceedings of the 35th CIB W78 2018 Conference: IT in Design, Construction, and Management. Springer, 889–895.

Negi S., Dhiman S., Sharma R.K. (2013). Basics, applications and future of additive manufacturing technologies: A review. Journal of Manufacturing Technology Research, 5(1/2): 75.

Odważny F., Szymańska O., Cyplik P. (2018). Smart factory: The requirements for implementation of the Industry 4.0 solutions in FMCG environment-case study. LogForum, 14(2): 257–267. http://doi.org/10.17270/J.LOG.2018.253.

Paelke V. (2014). Augmented reality in the smart factory: Supporting workers in an Industry 4.0. environment. In: Proceedings of the 2014 IEEE emerging technology and factory automation (ETFA), IEEE, 1–4.

Phuyal S., Bista D., Bista R. (2020а). Challenges, opportunities and future directions of smart manufacturing: A state of art review. Sustainable Futures, 2: 100023. https://doi.org/10.1016/j.sftr.2020.100023.

Phuyal S., Bista D., Izykowski J., Bista R. (2020b). Design and implementation of cost-efficient SCADA system for industrial automation. International Journal of Engineering and Manufacturing, 10(2): 15–28. 10.5815/ijem.2020.02.02.

Piller F.T., Moeslein K., Stotko C.M. (2004). Does mass customization pay? An economic approach to evaluate customer integration. Production Planning & Control, 15(4): 435–444. doi:10.1080/0953728042000238773.

Piller F.T., Moeslein K., Stotko C.M. (2004). Does mass customization pay? An economic approach to evaluate customer integration. Production Planning & Control, 15(4): 435-444. doi:10.1080/0953728042000238773.

Rahmanzadeh S., Pishvaee M.S., Govindan K. (2022). Emergence of open supply chain management: the role of open innovation in the future smart industry using digital twin network. Annals of Operations Research, preprint. https://doi.org/10.1007/s10479-021-04254-2.

Ren S., Zhang Y., Liu Y., Sakao T., Huisingh D., Almeida C.M. (2019). A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions. Journal of Cleaner Production, 210: 1343–1365.

Riemer K., Totz C. (2003). The many faces of personalization. In: The Customer Centric Enterprise: 35–50. Berlin; Heidelberg: Springer Berlin Heidelberg.

Riemer K., Totz C. (2003). The many faces of personalization. In: The Customer Centric Enterprise: 35-50. Berlin, Heidelberg, Springer Berlin Heidelberg.

Rovito M. (2022). Smart manufacturing: The future of making is digital. https://redshift.autodesk.com/smart-manufacturing/.

Schallmo D., Willams C.A., Boardman L. (2018). Digital transformation of business models-best practice, enabler, and roadmap. International Journal of Innovation Management, 21(8): 1740014. https://doi.org/10.1142/9781786347602_0005.

Strozzi F., Colicchia C., Creazza A., Noè C. (2017). Literature review on the ‘Smart Factory’concept using bibliometric tools. International Journal of Production Research, 55(22): 6572–6591. http://doi.org/10.1080/00207543.2017.1326643.

Strozzi F., Colicchia C., Creazza A., Noè C. (2017). Literature review on the ‘Smart Factory’concept using bibliometric tools. International Journal of Production Research, 55(22): 6572-6591. http://doi.org/10.1080/00207543.2017.1326643.

Ulas D. (2019). Digital transformation process and SMEs. Procedia Computer Science, 158: 662–671. https://doi.org/10.1016/j.procs.2019.09.101.

Ulas D. (2019). Digital transformation process and SMEs. Procedia Computer Science, 158: 662-671. https://doi.org/10.1016/j.procs.2019.09.101.

Wang X.V., Wang L. (2020). Safety strategy in the smart manufacturing system: A human robot collaboration case study. In: Proceedings of the ASME 2020 15th International Manufacturing Science and Engineering Conference, 2: Manufacturing Processes; Manufacturing Systems; Nano/Micro/Meso Manufacturing; Quality and Reliability. September 3. V002T07A026. ASME. https://doi.org/10.1115/MSEC2020-8427.

The authors declare that there are no conflicts of interest present.