**3. Industry 4.0 and its role in implementation of the Smart City concept**

#### **3.1. Industry 4.0 as the fourth industrial revolution and the prospect for sustainable development of automobile industry**

the number of fatal accidents by 50%. The transition from the creation of driver assistance systems to the development of semi-autonomous unmanned vehicles is a global trend, and it is explained by the desire of developers to ensure the sustainability and the safety

However, it should be understood that the emergence of new types of vehicles with fundamentally new control systems could cause problems of security and interaction with other road users. It is especially true in connection with the development of the "livable cities" concept that is aimed at encouraging the use of non-motorized transport, such as walking or cycling. On the one hand, streets need to be adapted, with safe walkways, crossings and cycling lanes, as well as transport junctions need to be established to create safe connection points between different transport modes. On the other hand, it is necessary to identify potential risks of the use of autonomous vehicles, to predict the likelihood of the traffic conflicts (between autonomous vehicles and pedestrians and cyclists, first of all) and to determine the possible consequences. In addition, the ways to prevent risk situations and to reduce the severity of the consequences in case of risk situations should

Automobile mode of transport is the main one in urban lands, and in the case of unreasonable transport management, it can cause significant problems for other road users. In addition, road transport is the main source of negative influence on the environment, so it needs quali-

The main idea of Smart City is that the city can be "smart" only if the management of all its subsystems is built according to the same rules. If we talk about road transport, then it actually means the management of the vehicle's life cycle as a separate component of the vehicle fleet (**Figure 3**), and at a higher level—the management of the vehicle fleet as a whole. Along with it, all processes at all stages of the life cycle should be intellectualized. At the same time, the orientation to customer needs should be one of the main factors that should be taken into account when planning and implementing these processes. The main directions are creation

of the elemental base of intelligent systems and software development.

**Figure 3.** Negative impact on the environment throughout the life cycle of the vehicle.

of the transport system [11].

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be developed.

tative management.

The current state of technics and technologies allows us to create tools and methods not only for managing technical and organizational and technical systems but also devices for analyzing the state of human functional systems and affecting them. This makes it possible to correct and optimize human activity both indirectly, using the recommended loads and parameters, and in real time, which allows creating a comfortable working environment, as well as increasing the safety and efficiency of labor, increasing the efficiency of production systems and product quality.

Real and virtual worlds are now beginning to merge in production that is why we are talking about "Industry 4.0"—the Siemens term for fourth Industrial Revolution. Increasing digitalization and networking is changing the entire industrial production chain, and the volume of data worldwide is exploding. Before analyzing and using the huge amount of data, systems that enable us to understand their content have to be developed. The first step is to get knowledge on what kinds of sensor and measurement technology can be used to collect necessary data and to understand operational principles of systems and devices.

The implementation of the concept Industry 4.0 (**Figure 4**) provides for the formation of cyberphysical systems (CPS), where all elements of the system are active objects that are involved in the exchange of information and make appropriate decisions. Continuous interchange of information in such cyber-physical systems is realized between its elements through the Internet of Things.

The Road Map developed by the group "TechNet" [12] provides creation of new generation of the modern digital productions—"Factory of the future" (**Figure 5**) that is a completely new production environment that is formed by the network of people, things and machines connected to each other. The proposed strategy is based on assumption that replication and scaling of advanced production technologies will determine further development.

Implementation of the "Factory of the future" concept will provide a significant reduction of the time placing on the market of the highly intelligent products by using digital design technologies throughout their life cycle.

Industry 4.0 is aimed at the process optimization, because it covers the entire life cycle, that is, each manufacturer is responsible for his product from the beginning of design and development to disposal.

Classical methods of production organization mean that the flow method can be used only for large quantities of goods. Thanks to the new principles of production processes organization, it becomes possible to manufacture also single products in an industrial way. Industry 4.0, thanks to its flexibility and adaptability provided by cyber-physical systems, can help to realize the mass production of individual orders, which will reduce the price of the product.

For production, the ability of various components to communicate through the network opens incredible prospects. In "smart factories," machines will understand their environment and will be able to communicate on a single network protocol among themselves, as well as with the logistics and business systems of suppliers and consumers. The production equipment, receiving information about the changed requirements, will be able to make adjustments to the technological process. As a result, production systems will become capable of self-optimization and self-configuration, the equipment will perform self-diagnostics and further flexibility and

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Chrysler's plant in Toledo is an example of the application of cyber-physical systems in manufacturing. Every day it produces more than 700 Jeep Wrangler's bodies. This involves 259 German robots KUKA, which "communicate" with 60,000 other devices and machines. Data interchange and its storage are organized with the use of cloud computing. Modern solutions

This will also cause the change of the service concept, because the manufacturer will be interested in creating a branded service network that will provide him with implementation of the principle of responsibility for his product throughout the life cycle—from design to disposal (**Figure 6**). This is especially true for modern trucks, which, in contrast to cars, are almost impossible to service in small auto repair workshops. In addition, thanks to the availability of its own service system, the manufacturer will have all information on the features of opera-

The production stage of the life cycle is one of the most important, because exactly at this stage ideas and projects turn into finished products. Besides, the quality of the product depends on the quality of manufacturing. It means that at this stage, it is determined if the targeted audience is large enough, if the product is competitive in the market and how effective and safe

have significantly increased productivity and flexibility of the factory.

tion, maintenance and repair of a particular car and the whole park.

**3.2. The scope and means of implementing the smart industry concept**

individualization of products will occur.

are the stages of operation and service.

**Figure 6.** The use of smart technologies at the life-cycle stages.

**Figure 4.** The technologies to implement the concept Industry 4.0.

**Figure 5.** Factory of the future and the product's life cycle.

For production, the ability of various components to communicate through the network opens incredible prospects. In "smart factories," machines will understand their environment and will be able to communicate on a single network protocol among themselves, as well as with the logistics and business systems of suppliers and consumers. The production equipment, receiving information about the changed requirements, will be able to make adjustments to the technological process. As a result, production systems will become capable of self-optimization and self-configuration, the equipment will perform self-diagnostics and further flexibility and individualization of products will occur.

Chrysler's plant in Toledo is an example of the application of cyber-physical systems in manufacturing. Every day it produces more than 700 Jeep Wrangler's bodies. This involves 259 German robots KUKA, which "communicate" with 60,000 other devices and machines. Data interchange and its storage are organized with the use of cloud computing. Modern solutions have significantly increased productivity and flexibility of the factory.

This will also cause the change of the service concept, because the manufacturer will be interested in creating a branded service network that will provide him with implementation of the principle of responsibility for his product throughout the life cycle—from design to disposal (**Figure 6**). This is especially true for modern trucks, which, in contrast to cars, are almost impossible to service in small auto repair workshops. In addition, thanks to the availability of its own service system, the manufacturer will have all information on the features of operation, maintenance and repair of a particular car and the whole park.

#### **3.2. The scope and means of implementing the smart industry concept**

The production stage of the life cycle is one of the most important, because exactly at this stage ideas and projects turn into finished products. Besides, the quality of the product depends on the quality of manufacturing. It means that at this stage, it is determined if the targeted audience is large enough, if the product is competitive in the market and how effective and safe are the stages of operation and service.

**Figure 6.** The use of smart technologies at the life-cycle stages.

**Figure 4.** The technologies to implement the concept Industry 4.0.

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**Figure 5.** Factory of the future and the product's life cycle.

The terms "Smart Factory," "Smart Manufacturing," "Intelligent Factory" and "Factory of the Future" all describe a vision of what industrial production will look like in the future. Digital technologies will make factories more efficient, intelligent, flexible and dynamic. In connected Industry, everything from design to manufacturing is done through interaction between products and machines and collaborative effort between machines themselves.

to optimize component deliveries to the plant for the production of motor vehicles has been

of lorries used for transportation and by increasing the lot sizes and the speed of the transportation and loading process. The impact of the type of forklift engine (diesel, gas, electric) on the nature of the impact on the environment was also assessed [19]. It was concluded that electric forklifts are more effective from an environmental point of view; however, the research did not take into account economic and technological factors (cost of forklifts, down-

Analysis of the studies shows that a systemic approach to the solution of the problem is needed. This is especially true because once all of the complex subsystems of the production process and their interactions are taken into account, positive synergistic effect can be

Simulation models are used to determine the optimal parameters of technological processes when changing internal or external parameters of production. Input data for the development of the simulation model of technological process are typical manufacturing processes and Teamcenter database (data on assemblies, products, equipment, tools and environment). The structure of individual technological process is adjusted in accordance with the composition and structure of the unified technological process by analyzing the need for each operation and the technological transition with the consistent refinement of all solutions. Technological design consists in the development of standard technological processes, from which in the future it is possible to assemble various methods of assembling cars. This makes it possible to significantly reduce labor input and the time required for their introduction into

Mass conveyor production is based on the principle of the flow organization of technological

• the division of the assembly process into a series of assembly operations, sequentially ar-

• the use of special transport devices to move the assembled units between assembly devices

• the use of special transport devices for supplying parts and assemblies to the main assem-

• the use of special and unified tools and devices for mechanization and automation of the

When the production is organized in such way, assembly of the entire vehicle on the main assembly conveyor is carried out from the finished assembled units and aggregates, connected together by fasteners. The open architecture of the Teamcenter system allows you to connect to

• mechanical machining of parts and assembly of units in machine-assembly shops.

ranged in time and space, performed by the operators-assemblers;

emissions by 3% was simply achieved by lowering the number

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developed [18]. Reducing CO2

achieved.

production.

bly conveyor;

technological process;

assembly processes, providing:

and to ensure a given assembly rate;

time for battery charging of forklift, etc.).

*3.3.2. Optimization of technological processes on the assembly line*

Manufacturing in a Smart Factory will be more intelligent, flexible and dynamic in comparison with today's industries. It is so because all production processes and functions (product development; resource planning; logistics; factory and production planning and executing; monitoring, control and management functions, etc.) will be closely interconnected. At the same time, machinery and equipment will have the ability to improve processes through selfoptimization and autonomous decision-making.

#### **3.3. Examples of the processes control intellectualization in production systems**

#### *3.3.1. Methods and models to improve the assembly line production*

In the production process of products with the great amount of components, several problems may occur: stock storage limit, limits of functional zoning, creation of a balanced flow, minimizing the component delivery time to the assembly line positions, etc. Nowadays, in an open and competitive market, companies cannot afford to waste time and resources for work that can be done in a better and faster way with advanced solutions. In Ref. [13], implementing production monitoring systems (PMS) in order to support product lifecycle management (PLM) system with historic knowledge regarding the state of machinery, correctness of assembly operations, etc. is suggested. A module to analyze collected information and predict the future performance of the monitored component is thus needed. In numerous studies, the use of models for this purpose and simultaneous reduction of costs that arise at different stages of production and technological process are suggested. Hence, one of the most important tasks of the organization of the assembly line production is line balancing problem. To balance unilateral and bilateral flow lines, in Ref. [14], a model that takes into account zoning and priority limits, synchronous and positional constraints, buffer time have been developed. The objective function of the model maximizes line efficiency and minimizes index of smoothness and total cost per unit of product. As an example, bilateral assembly line of chassis production of motor vehicles lowering the cost per unit by 42% has been provided.

In the market of automotive components, competitiveness is ensured by high-quality products and low cost. This requires manufacturers to search for methods to minimize costs at all stages of production. To meet the challenges of balancing assembly lines with considering costs exact methods, heuristic and metaheuristic approaches can be applied [15–17]. The developed approaches combine heuristic models and exact algorithms based on "taboo" search in order to minimize short-term operating costs, capital investments, costs of labor and work in progress.

Inefficient production and suboptimal in-plant logistics contribute significantly to environmental degradation. Hence, large number of studies aiming at optimizing the planning of technological transport in view of its negative impact on the environment. Thus, a concept to optimize component deliveries to the plant for the production of motor vehicles has been developed [18]. Reducing CO2 emissions by 3% was simply achieved by lowering the number of lorries used for transportation and by increasing the lot sizes and the speed of the transportation and loading process. The impact of the type of forklift engine (diesel, gas, electric) on the nature of the impact on the environment was also assessed [19]. It was concluded that electric forklifts are more effective from an environmental point of view; however, the research did not take into account economic and technological factors (cost of forklifts, downtime for battery charging of forklift, etc.).

Analysis of the studies shows that a systemic approach to the solution of the problem is needed. This is especially true because once all of the complex subsystems of the production process and their interactions are taken into account, positive synergistic effect can be achieved.

#### *3.3.2. Optimization of technological processes on the assembly line*

The terms "Smart Factory," "Smart Manufacturing," "Intelligent Factory" and "Factory of the Future" all describe a vision of what industrial production will look like in the future. Digital technologies will make factories more efficient, intelligent, flexible and dynamic. In connected Industry, everything from design to manufacturing is done through interaction between products and machines and collaborative effort between machines themselves.

Manufacturing in a Smart Factory will be more intelligent, flexible and dynamic in comparison with today's industries. It is so because all production processes and functions (product development; resource planning; logistics; factory and production planning and executing; monitoring, control and management functions, etc.) will be closely interconnected. At the same time, machinery and equipment will have the ability to improve processes through self-

In the production process of products with the great amount of components, several problems may occur: stock storage limit, limits of functional zoning, creation of a balanced flow, minimizing the component delivery time to the assembly line positions, etc. Nowadays, in an open and competitive market, companies cannot afford to waste time and resources for work that can be done in a better and faster way with advanced solutions. In Ref. [13], implementing production monitoring systems (PMS) in order to support product lifecycle management (PLM) system with historic knowledge regarding the state of machinery, correctness of assembly operations, etc. is suggested. A module to analyze collected information and predict the future performance of the monitored component is thus needed. In numerous studies, the use of models for this purpose and simultaneous reduction of costs that arise at different stages of production and technological process are suggested. Hence, one of the most important tasks of the organization of the assembly line production is line balancing problem. To balance unilateral and bilateral flow lines, in Ref. [14], a model that takes into account zoning and priority limits, synchronous and positional constraints, buffer time have been developed. The objective function of the model maximizes line efficiency and minimizes index of smoothness and total cost per unit of product. As an example, bilateral assembly line of chassis pro-

**3.3. Examples of the processes control intellectualization in production systems**

duction of motor vehicles lowering the cost per unit by 42% has been provided.

In the market of automotive components, competitiveness is ensured by high-quality products and low cost. This requires manufacturers to search for methods to minimize costs at all stages of production. To meet the challenges of balancing assembly lines with considering costs exact methods, heuristic and metaheuristic approaches can be applied [15–17]. The developed approaches combine heuristic models and exact algorithms based on "taboo" search in order to minimize short-term operating costs, capital investments, costs of labor and

Inefficient production and suboptimal in-plant logistics contribute significantly to environmental degradation. Hence, large number of studies aiming at optimizing the planning of technological transport in view of its negative impact on the environment. Thus, a concept

optimization and autonomous decision-making.

120 Sustainable Cities - Authenticity, Ambition and Dream

work in progress.

*3.3.1. Methods and models to improve the assembly line production*

Simulation models are used to determine the optimal parameters of technological processes when changing internal or external parameters of production. Input data for the development of the simulation model of technological process are typical manufacturing processes and Teamcenter database (data on assemblies, products, equipment, tools and environment).

The structure of individual technological process is adjusted in accordance with the composition and structure of the unified technological process by analyzing the need for each operation and the technological transition with the consistent refinement of all solutions. Technological design consists in the development of standard technological processes, from which in the future it is possible to assemble various methods of assembling cars. This makes it possible to significantly reduce labor input and the time required for their introduction into production.

Mass conveyor production is based on the principle of the flow organization of technological assembly processes, providing:


When the production is organized in such way, assembly of the entire vehicle on the main assembly conveyor is carried out from the finished assembled units and aggregates, connected together by fasteners. The open architecture of the Teamcenter system allows you to connect to the PLM environment systems such as Matlab/Simulink and Rhapsody. In order to work with data in the usual formats, we can use the capabilities of dynamic integration with Microsoft Office software package. The obtained solutions are stored in the knowledge base and can be used in similar production situations.

The specificity of robotic production is that mistakes and failures in the technological processes are not attributable to mistakes of operators. Quality control of the equipment in this case can be carried out by comparison with the model of production system (i.e., system of virtual production). It is possible to identify the causes of errors by using imitation of the real system processes. In addition, simulation models allow to test new production concepts and agree with each other that all the subsystems on the design phase of production. It is also possible to optimize and to modernize virtually the existing complex of production with the aim, for example, to test the transition to the new product. Such systems allow to optimize the process of equipment maintenance, taking into account condition and features of the real system. It is shown in the research paper [23] that maintenance scheduling, quality control and production scheduling influence one another and, therefore, need to be considered jointly for improving the system performance. A model has been created to integrate maintenance scheduling and process to develop a policy of decision management. It provided optimal parameters of preventive maintenance as well as a chart of control intervals, which minimize expected cost per time unit. Subsequently, the optimal interval of preventive maintenance is integrated with the production schedule in order to determine the optimal batch sequence,

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The authors of the article [24] offer the method of multi-criterial classification of critical equipment (MCCE), which allows to classify equipment objectively according to its importance. They assume that according to such service approach the most critical equipment will not fail or, at least, all appearing failures will be rapidly detected and corrected in a minimum possible amount of time. To provide this information, the consequences of any failure in the

The article [25] is devoted to analyze mistakes and false operations, which require to carry out service operations. The authors designed two models to describe an ideal situation: the first one, in which a false-positive alarm implies the renewal of protection system and the second one: not. In the first situation, imperfect inspection is manifested in the scenario, where a false alarm implies an additional cost for the system owner; in the second situation, a false alarm does not imply renewal of the protection system. In both cases, a false-negative inspection can appear, in

which the system is considered to be in a good condition, when it does not work in fact.

A condition-based preventive maintenance approach that is developed as a software service located in a "cloud" is presented in the paper [26]. It acquires and processes data from the shopfloor of machine tools using the technique of information fusion. The authors consider that benefits from the combination of monitoring and maintenance techniques under the umbrella of Cloud and mobile communication have not been still exploited sufficiently. At the same time, advanced maintenance methods, which cover and process the shop-floor information, can reduce costs and increase the sustainability of the enterprise. An operator reports through mobile devices the following data: status of machine tool (for example, available, busy, etc.), current running task, cutting-tool availability and appearance of failures. Combining inputs from the machine tool operator and the sensory system, an actual machining time of machine tools and cutting tools is calculated. The controlling data are processed through the technique of

which allows to minimize penalty costs due to schedule delay.

appropriate equipment are analyzed for a particular company.

The use of simulation models allows you to isolate operations that need optimization, determine the required number of employees and optimize the working load. Obtained solutions can allow reduction of the assembly time at the conveyor positions, and, accordingly, the cycle time by 6%, while the optimal loading of personnel decreases the number of errors.

Implementation of the proposed method was carried out during the development of the Decision Support System (DSS) for automotive company KAMAZ (of Naberezhnye Chelny, Russia) [20]. To optimize production processes, special documentation was developed. The documentation is integrated in production system for shared use [21].

Since the interdependent processes are modeled in parallel, program modules share the information for operational adjustment processes. Optimization of the conveyor in order to reduce delays is performed in two directions: alignment of operations on the assembly line positions and the operational management of the supply of components to the position. Optimization of technological transport includes providing the conveyor positions by necessary components with minimum cost (the number of forklifts and work time).

#### *3.3.3. Monitoring and managing equipment efficiency*

#### *3.3.3.1. Literature review*

One of the most important conditions of the successful operation for any industrial plant is to ensure uninterrupted operation of the equipment. That is why the indicators that characterize the quality of the equipment's use can be used as the objective function when modeling of technological processes [22]. In so doing, it should be taken into account not only the actual time of the equipment use and its performance but also the share of goods without any defects in the overall product output.

In the case of robotic production, the equipment efficiency depends significantly on the quality of its service, which affects indicators of availability, performance and quality of the final product. As a rule, a system to support the workability of equipment (maintenance and repair) is developed to ensure its efficient use. Frequency of service is determined depending on the equipment characteristics and is assigned by the manufacturer. To exclude catastrophic failure, the methods to predict and improve reliability exist.

Since there are different categories of losses, for monitoring the equipment condition, it is necessary to foresee methods for their control. The adjustment of the equipment maintenance system must be performed in accordance with the criteria of its efficiency. Furthermore, the method of complex multidimensional assessment of the performance indicators allows to raise the efficiency of production system management and, at the same time, to increase its stability as well as to reduce unplanned downtime.

The specificity of robotic production is that mistakes and failures in the technological processes are not attributable to mistakes of operators. Quality control of the equipment in this case can be carried out by comparison with the model of production system (i.e., system of virtual production). It is possible to identify the causes of errors by using imitation of the real system processes. In addition, simulation models allow to test new production concepts and agree with each other that all the subsystems on the design phase of production. It is also possible to optimize and to modernize virtually the existing complex of production with the aim, for example, to test the transition to the new product. Such systems allow to optimize the process of equipment maintenance, taking into account condition and features of the real system.

the PLM environment systems such as Matlab/Simulink and Rhapsody. In order to work with data in the usual formats, we can use the capabilities of dynamic integration with Microsoft Office software package. The obtained solutions are stored in the knowledge base and can be

The use of simulation models allows you to isolate operations that need optimization, determine the required number of employees and optimize the working load. Obtained solutions can allow reduction of the assembly time at the conveyor positions, and, accordingly, the cycle time by 6%, while the optimal loading of personnel decreases the number of errors.

Implementation of the proposed method was carried out during the development of the Decision Support System (DSS) for automotive company KAMAZ (of Naberezhnye Chelny, Russia) [20]. To optimize production processes, special documentation was developed. The

Since the interdependent processes are modeled in parallel, program modules share the information for operational adjustment processes. Optimization of the conveyor in order to reduce delays is performed in two directions: alignment of operations on the assembly line positions and the operational management of the supply of components to the position. Optimization of technological transport includes providing the conveyor positions by necessary compo-

One of the most important conditions of the successful operation for any industrial plant is to ensure uninterrupted operation of the equipment. That is why the indicators that characterize the quality of the equipment's use can be used as the objective function when modeling of technological processes [22]. In so doing, it should be taken into account not only the actual time of the equipment use and its performance but also the share of goods without any defects

In the case of robotic production, the equipment efficiency depends significantly on the quality of its service, which affects indicators of availability, performance and quality of the final product. As a rule, a system to support the workability of equipment (maintenance and repair) is developed to ensure its efficient use. Frequency of service is determined depending on the equipment characteristics and is assigned by the manufacturer. To exclude catastrophic fail-

Since there are different categories of losses, for monitoring the equipment condition, it is necessary to foresee methods for their control. The adjustment of the equipment maintenance system must be performed in accordance with the criteria of its efficiency. Furthermore, the method of complex multidimensional assessment of the performance indicators allows to raise the efficiency of production system management and, at the same time, to increase its

documentation is integrated in production system for shared use [21].

nents with minimum cost (the number of forklifts and work time).

*3.3.3. Monitoring and managing equipment efficiency*

ure, the methods to predict and improve reliability exist.

stability as well as to reduce unplanned downtime.

*3.3.3.1. Literature review*

in the overall product output.

used in similar production situations.

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It is shown in the research paper [23] that maintenance scheduling, quality control and production scheduling influence one another and, therefore, need to be considered jointly for improving the system performance. A model has been created to integrate maintenance scheduling and process to develop a policy of decision management. It provided optimal parameters of preventive maintenance as well as a chart of control intervals, which minimize expected cost per time unit. Subsequently, the optimal interval of preventive maintenance is integrated with the production schedule in order to determine the optimal batch sequence, which allows to minimize penalty costs due to schedule delay.

The authors of the article [24] offer the method of multi-criterial classification of critical equipment (MCCE), which allows to classify equipment objectively according to its importance. They assume that according to such service approach the most critical equipment will not fail or, at least, all appearing failures will be rapidly detected and corrected in a minimum possible amount of time. To provide this information, the consequences of any failure in the appropriate equipment are analyzed for a particular company.

The article [25] is devoted to analyze mistakes and false operations, which require to carry out service operations. The authors designed two models to describe an ideal situation: the first one, in which a false-positive alarm implies the renewal of protection system and the second one: not. In the first situation, imperfect inspection is manifested in the scenario, where a false alarm implies an additional cost for the system owner; in the second situation, a false alarm does not imply renewal of the protection system. In both cases, a false-negative inspection can appear, in which the system is considered to be in a good condition, when it does not work in fact.

A condition-based preventive maintenance approach that is developed as a software service located in a "cloud" is presented in the paper [26]. It acquires and processes data from the shopfloor of machine tools using the technique of information fusion. The authors consider that benefits from the combination of monitoring and maintenance techniques under the umbrella of Cloud and mobile communication have not been still exploited sufficiently. At the same time, advanced maintenance methods, which cover and process the shop-floor information, can reduce costs and increase the sustainability of the enterprise. An operator reports through mobile devices the following data: status of machine tool (for example, available, busy, etc.), current running task, cutting-tool availability and appearance of failures. Combining inputs from the machine tool operator and the sensory system, an actual machining time of machine tools and cutting tools is calculated. The controlling data are processed through the technique of information fusion to identify the status of machine tool and, consequently, its actual machining time. On the basis of this information, the maintenance department is able to schedule the maintenance of machine tool according to its actual wear, not in fixed intervals.

Along with it, it should be borne in mind that while the transition to a smart factory, there will

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The product/process design is the first of these challenges. It covers all the tools and engineering services supporting the design of parts, finished products, processes, production lines and factories. This category is evolving from a separate design towards one that is largely mod-

The virtual factory means simulating the production lines from start to end and enabling you to anticipate potential sources of additional costs or poor quality and preset the machinery and equipment and control of the productive facilities. This integrates the control system (digital control, interconnection with the factory's upstream and downstream, planning and centralized control of the production line), traceability (sensors of production conditions, monitoring of individual parts being manufactured) and the management of physical flows

Manufacturing operations (at the heart of processing) were identified as the third challenge, with two performance criteria: precision (the optimization of existing technologies, such as high-speed machining and laser cutting, and smart self-correcting machines) and flexibility

The fourth challenge covers services related to the productive facilities. It includes the integration services of the various components of the production line and the installation and

The fifth challenge is the newcomer; it includes the digital technologies behind the upheaval. Hard to imagine the factory of tomorrow without the contribution of cloud computing, whether to store data, work with remote desktops or use SaaS software; without Big Data Analytics, which will improve production through predictive remote maintenance or will increase energy efficiency and without the Manufacturing Internet of Things, these autono-

Finally, work organization is the last challenge. Examples include the establishment of orga-

When developing a strategy for transition to a smart factory, it should be borne in mind that modern industrial automation systems are composed of several clearly separated levels:

• Level of business processes management (computers for data processing, MES systems)

Each of these levels is relatively well structured and individual devices can be clearly mapped

• Manufacturing level (server, where MRP I, II, III, ERP systems, etc. are located).

eled and simulated, and most importantly, joint between product and process.

(automation of internal logistics and interconnection of external logistics).

(multi-device multimedia additive manufacturing machines).

mous cyber-objects capable of making local decisions.

nizations that empower operators or can learn.

*3.3.3.3. Examples of existing cyber-physical systems*

• Data collection level (sensors and actuators)

to one of the levels.

• Level of control (operator terminals and control devices)

maintenance of production machinery.

be a number of problems that need to be solved.

As it is seen from the above review, the authors believe that to improve the efficiency and sustainability of the production system it is necessary to improve service. For these purposes, we offer different methods, but the general is the simulation of systems. A model is suggested to be constructed using statistics of failures and malfunctions of equipment. Reliable operation of the equipment is based on information about its failures, unplanned stops and failures recorded in the complaint acts and stored in databases. The analysis of such information allows us to identify the causes of emergencies and warn them. For the operational management and adjustment of a service system, intelligent control systems, including cloud technology, are offered.

#### *3.3.3.2. Challenges in transition to a smart factory*

In our opinion, the existing platforms to create a unified information space are the most effective way of both strategic and operational management stages of the life cycle of the product. The concentration of heterogeneous data, semantic linking and providing access to them through search interfaces is a topic which today is engaged in many companies. Many longterm benefits of implementing management systems and product lifecycle (PLM) cannot be achieved without having a comprehensive digital manufacturing strategy, which allows the simulation of production processes aimed at reuse of existing knowledge and optimizes processes before products are manufactured. In addition, digital production allows you to get feedback from actual manufacturing operations and to incorporate it into the design process of the product, so businesses already can solve production and technological problems at the design stage. Among the initiatives for the development of systems to support digital manufacturing is improving user interaction by providing information in the context of the problem being solved, thanks to which engineers can make the right decisions faster. Measures are taken to ensure direct communication with technological equipment, such as programmable logic controllers (PLCs), machine controllers, numerical control (CNC), etc. Single platforms are created to manage the information stored in the PLM system and manufacturing execution systems (Manufacturing Execution systems, MES).

The creation of DSS is especially important when creating flexible production systems, which are based on robotic systems. However, it is necessary to consider that the flexible automated manufacturing (FAM) operates on the basis of a solitary technology, so the work of all production components is coordinated as a whole multi-level control system which ensure the change in program, fast tuning technologies when changing production facilities.

The decision on the design and optimization of manufacturing from Tecnomatix is parametric 3D smart objects that can be used for quick and efficient planning of the enterprise. The use of 3D smart objects for planning of the enterprise also enables to detect design errors not on the factory floor but even in the planning stage. The flow of materials, transportation, logistics and auxiliary operation can be optimized using material flow analysis and modeling events.

Along with it, it should be borne in mind that while the transition to a smart factory, there will be a number of problems that need to be solved.

The product/process design is the first of these challenges. It covers all the tools and engineering services supporting the design of parts, finished products, processes, production lines and factories. This category is evolving from a separate design towards one that is largely modeled and simulated, and most importantly, joint between product and process.

The virtual factory means simulating the production lines from start to end and enabling you to anticipate potential sources of additional costs or poor quality and preset the machinery and equipment and control of the productive facilities. This integrates the control system (digital control, interconnection with the factory's upstream and downstream, planning and centralized control of the production line), traceability (sensors of production conditions, monitoring of individual parts being manufactured) and the management of physical flows (automation of internal logistics and interconnection of external logistics).

Manufacturing operations (at the heart of processing) were identified as the third challenge, with two performance criteria: precision (the optimization of existing technologies, such as high-speed machining and laser cutting, and smart self-correcting machines) and flexibility (multi-device multimedia additive manufacturing machines).

The fourth challenge covers services related to the productive facilities. It includes the integration services of the various components of the production line and the installation and maintenance of production machinery.

The fifth challenge is the newcomer; it includes the digital technologies behind the upheaval. Hard to imagine the factory of tomorrow without the contribution of cloud computing, whether to store data, work with remote desktops or use SaaS software; without Big Data Analytics, which will improve production through predictive remote maintenance or will increase energy efficiency and without the Manufacturing Internet of Things, these autonomous cyber-objects capable of making local decisions.

Finally, work organization is the last challenge. Examples include the establishment of organizations that empower operators or can learn.

#### *3.3.3.3. Examples of existing cyber-physical systems*

information fusion to identify the status of machine tool and, consequently, its actual machining time. On the basis of this information, the maintenance department is able to schedule the

As it is seen from the above review, the authors believe that to improve the efficiency and sustainability of the production system it is necessary to improve service. For these purposes, we offer different methods, but the general is the simulation of systems. A model is suggested to be constructed using statistics of failures and malfunctions of equipment. Reliable operation of the equipment is based on information about its failures, unplanned stops and failures recorded in the complaint acts and stored in databases. The analysis of such information allows us to identify the causes of emergencies and warn them. For the operational management and adjustment of a service system, intelligent control systems, including cloud

In our opinion, the existing platforms to create a unified information space are the most effective way of both strategic and operational management stages of the life cycle of the product. The concentration of heterogeneous data, semantic linking and providing access to them through search interfaces is a topic which today is engaged in many companies. Many longterm benefits of implementing management systems and product lifecycle (PLM) cannot be achieved without having a comprehensive digital manufacturing strategy, which allows the simulation of production processes aimed at reuse of existing knowledge and optimizes processes before products are manufactured. In addition, digital production allows you to get feedback from actual manufacturing operations and to incorporate it into the design process of the product, so businesses already can solve production and technological problems at the design stage. Among the initiatives for the development of systems to support digital manufacturing is improving user interaction by providing information in the context of the problem being solved, thanks to which engineers can make the right decisions faster. Measures are taken to ensure direct communication with technological equipment, such as programmable logic controllers (PLCs), machine controllers, numerical control (CNC), etc. Single platforms are created to manage the information stored in the PLM system and manufacturing execu-

The creation of DSS is especially important when creating flexible production systems, which are based on robotic systems. However, it is necessary to consider that the flexible automated manufacturing (FAM) operates on the basis of a solitary technology, so the work of all production components is coordinated as a whole multi-level control system which ensure the

The decision on the design and optimization of manufacturing from Tecnomatix is parametric 3D smart objects that can be used for quick and efficient planning of the enterprise. The use of 3D smart objects for planning of the enterprise also enables to detect design errors not on the factory floor but even in the planning stage. The flow of materials, transportation, logistics and auxiliary operation can be optimized using material flow analysis and model-

change in program, fast tuning technologies when changing production facilities.

maintenance of machine tool according to its actual wear, not in fixed intervals.

technology, are offered.

ing events.

*3.3.3.2. Challenges in transition to a smart factory*

124 Sustainable Cities - Authenticity, Ambition and Dream

tion systems (Manufacturing Execution systems, MES).

When developing a strategy for transition to a smart factory, it should be borne in mind that modern industrial automation systems are composed of several clearly separated levels:


Each of these levels is relatively well structured and individual devices can be clearly mapped to one of the levels.

In Industry 4.0, the system structure changes. The data collection level remains a separate dedicated level, as it is now, but the devices will be more intelligent and they will also significantly increase in numbers. All other functions will move to the high speed real-time network consisting of data processing center and cloud computing. The benefits of such a structure are as follows: (1) reduction of diversity of devices and processing hardware that are the most modern in the world; (2) separation of specific functions and (3) the use of augmented and virtual reality. All of it contribute to simplification of the management process, more efficient use of resources and, consequently, cost savings. This approach has not been implemented yet due to the low efficiency, reliability and throughput of communication channels between servers and data collecting devices. However, all these problems will be solved in new and future systems.

At the same time, despite the existing positive experience of introducing intelligent technologies, there are still can appear situations when alleged improvements can lead to losses while vehicles production. There are also a lot of issues identified by analysts that could lead to

Influence of the Motor Transport on Sustainable Development of Smart Cities

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First, it should be noted that operation of any complicated system is always closely connected to the risks. It is especially actual for transport systems. The complexity of transportation systems' risk analysis is due to the fact that an accident potentially may happen in any part of the route and the same events may lead to absolutely different consequences. That is why every decision for the existing transportation system's optimization should also be considered from

**a.** Reduction of operational reliability because of increased complexity of the vehicles'

**a.** The risk of technological disasters if there are cyberattacks or failures in the control

**b.** The risk of increasing negative impact on environment because of expansion of the

**a.** Ambiguity of legal responsibility for causing damage and when organizing transportation.

The most possible risks with the most drastic consequences can be grouped by types:

critical situations while such vehicles operation.

**b.** Increased infrastructure requirements.

**c.** Increased requirements to communication systems.

**a.** Complexity of the movement algorithms for rough terrain.

**c.** Increased requirements to information processing speed.

**d.** Complexity of decision-making in unusual situations.

**a.** The high cost of infrastructure changes.

**b.** The high price of the vehicles.

**b.** The absence of a panoramic view of the streets, impeding the routing.

the perspectives of risk management.

**1.** Technical:

**2.** Ecological:

design.

system.

**3.** Organizational:

**4.** Economic:

**5.** Legal and ethical:

**b.** Loss of privacy.

vehicles' fleet.

Industry 4.0 will be built in cyber-physical systems, which involves the integration of computation, networking and physical processes. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa.

An example of such a system today is the CarTel project at MIT [27] where a fleet of taxis collects real-time traffic information in the Boston area. This information is combined with historical data to calculate the fastest routes for particular times of the day. Another example that you may be familiar with is the Smart Grid. One of its definitions, based on [28], is: "A modernized electrical grid that uses information and communications technology to gather and act on information in an automated fashion … to improve the efficiency, reliability, economics, and sustainability of the production and distribution of electricity."

Finally, an example for a factory [29] is changing systems so that the energy consumption in a vehicle assembly line is reduced when the line does not operate. Today, many production lines continue running during breaks and weekends. Consider laser welding technology that remains powered up over weekends, so it can resume quickly on Monday. This practice consumes up to 12% of total energy consumption of the assembly line. With Industry 4.0 and cyber-physical systems, robots will go into standby mode as a matter of course during short production breaks and power down during longer breaks. Speed-controlled motors that reduce the energy required to run machines will be widespread. Such changes will significantly reduce energy consumption and will be taken into account up front as part of Smart Factory design practices.
