International Conference on Advanced and Competitive Manufacturing Technologies

The article considers methodology for assessing eciency of digital transformation of industrial company production activity in a multi- techno-economic structure. The method allows forming, assessing and comparing a whole set of permitted alternative options of innovative solutions taking into account the set of technological repartitions of the business system of the industrial enterprise. It is shown that the imbalance of the multistructure of technological repartitions in industry is a serious barrier to its digital development. The massive introduction of cyberphysical systems into production, qualifying as the Industry 4.0 concept, is a global trend in the development of the technosphere. This concept involves the integration of modern information technology and communication networks with production equipment to organize, control and manage the entire chain of value creation processes in the product life cycle. The basis of the developed methodology is a complex of mathematical models for the formation of means of equipping production processes with digital technology; a total of partial criteria to assess the eciency of the company production activity as a result of digital technology; searching for an optimal problem solution. A general system for the assessment include partial criteria of two classication groups: characterizing the manufacturing process and characterizing the production activity output. The process of digital transformation is presented in the form of the algorithm for the assessment of digital technology introduction eciency within the already existing or being developed corporate information system. The automation of the developed method is conducted with software tools.


Introduction
The current state of industry is characterized by the complexity, hierarchy and multi-nomenclature of the production environment [1][2][3][4][5].
The imbalance of the multistructure of production redistribution in industry is a serious barrier to its digital development due to the dominance of physically and morally obsolete means of equipping technological processes, which prevents the total digitization of all production components [6,7].
The effective development of equipment and production technology of redistribution is based on the laws of mechanical engineering technology, and plans for their changes and maintenance are identified through technological auditing and digital controlling [8][9][10][11].
The massive introduction of cyberphysical systems into production, qualifying as the Industry 4.0 concept, means the digital transformation of the industrial environment and is a global trend in the development of the technosphere. This concept involves the integration of modern information technology and communication networks with production equipment and automation to organize, control and manage the entire chain of value creation processes in the product life cycle [12][13][14][15].
However, as the review and analysis of available sources has demonstrated, the Russian market currently does not have a set of tools needed to create and select industrial digital transformation programs, which makes the decision-making process much harder and, as a result, reduces its quality [16,17].
In this regard, the formation of a set of necessary and sufficient conditions for the implementation of a digital model of the industrial enterprises work, based on the state of the existing production and technological base, is relevant.
Digital transformation of innovative digital production in the Russian Federation is quite developed. The production stage is the most time-consuming form of digitalization due to the high cost of equipment and production infrastructure facilities (equipment, warehouses, engineering systems of functional support). It has conservative forms of formality and is difficult to adapt these means of production to digital technology. Here, the level of digitization of the individual attributes of future cyberphysical systems formed in different technological structures throughout the entire complex of enterprise redistributions and the presence of informational interconnection of their local components are important.

Conceptual Modeling of Industrial Enterprises
To simulate the industrial systems and formulate plans for digital transformations a set of key attributes has been formed. These attributes determines the dominant form of the impact on the human labor of the key attributes of production systems, such as matter (M), energy (E), information (I) and management (M2) -MEIM2.
For example, attribute (E) implies energy equipment of the production environment through a complex of energy sources and technologies used to carry out production or ensure the functioning of technological means (pneumatic, electro-, physicochemical, laser, plasma, water, energy sources, etc.).
The human factor (bio-attribute) in the form of personnel is taken out of the framework of being fit, as it is always present at all stages of the development cycle of human civilization. The degree of human participation in machine technologies is differentiated by the development of the latter and tends to reduce the time of physical interaction of the worker with the material and energy components of the production system (M, E) as the most dangerous, and increase intellectual activity through IT tools (I), developed in recent decades (M2).
The basic attributes of a digital lifestyle are knowledge and cognitive technology for managing them. The code of corporate knowledge is formed in standards (requirements and norms) and current regulations that describe organizational and process systems in the form of a business architecture. [27,28] The increase in the level of fit is associated with the degree of automation of processes and the merging of the existing production and technological base with information technology by retrofitting engineering components with specialized digital devices, as well as the use of materials, equipment and technologies of new generations. The balanced state of the manageable factors of packing MEIM2 and their synergistic interaction with each other in terms of functionality and target requirements, allows for the evolutionary and cheapest transition to a new structure.

Mathematical Modeling of Industrial Enterprises in Digital Transformation
The developed mathematical model of industrial enterprises digital transformation from the application of digital technology and manufacturing (DTM) is based upon a multi-layer graph-model providing for the generation of options of sets of components DTM by multiple alternative routes, see Fig. 1 [29,30].
Here is how segments (layers) of the hypergraph are identified.
In the general case, the minimum set of production redistributions of the techno-economic structure of the business system of an industrial enterprise, which constitutes the main stream of creating enterprise value, is determined by a combination of ten redistributions.
At the same time, redistribution refers to a set of supporting or transforming processes and means of equipping them, associated with changes in the status, properties, shape and territorial location of production items or delivered products that have a separate stage in the product life cycle.
Each redistribution (points of the graph-model t ij ) is determined by the set of key attributes of the MEIM2: t 11 , t 12 , . . . , t 10T 10 .
Multiple possible options of innovative solutions are simulated with a set of the graph alternative routes formed at successive transitions between the layers from the edge Ds containing the reference information to the edge Ct characterizing the selection of the optimal innovative solution option. The condition for including the arcs ej to the alternative routes is formed as: Due to the fact that the problem does not have strict restrictions on the correspondence of the pointes of different lavels to each other, the total number of alternatives of the management decision of digital transformations will be equal to the total number of edges of an N -partial p -uniform hypergraph formed in accordance with conditions (2): The formation of the set of alternatimanagerial decision, in accordance with the proposed methodology, is modeled by the formation of the set of edges of an Npartite N -uniform l -vertex hyperves of the graph constructed in accordance with conditions (2). The number of generated alternatives in this case can be determined by the formula (3): The choice of the optimal solution is made by analyzing the vector objective function of the form: Here F i (x) is the maximized or the minimized of the partial preference criterion.
A set of production and economic preference criteria was considered as evaluation criteria in the work [29,30]. A comprehensive assessment of the effectiveness of the assessment was carried out by the method of multicriteria convolution.
The developed methodology allows one to formulate, evaluate and compare the whole set of acceptable alternative options for innovative solutions for digital transformation taking into account the totality of production conversions of the multi-techno-economic structure of the business system of an industrial enterprise.

Planning of the Digital Transformation for industrial company production activity in the multi-techno-economic structure
In terms of the structure, the process of production transformations with DTM use is presented in the form of the algorithm for assessing the effectiveness of the introduction of digital information technology within the framework of an existing or developing corporate information system.
At the first stage the bulk of reference data for each redistribution is formed. Then the initial information is grouped in the following areas: -Technical direction: smart machines (adaptive technology and engineering and energy infrastructure, executive information-conversion elements and devices -machine tool component base), smart products, smart items of production, total sensors (sensors, chips), built-in hardware and devices for mon-itoring / controlling objects and objects of discrete production) -Information direction (hardware (PC, workstations, communication modules, memory blocks) and IT landscape, platform software, information security, network infrastructure) -Organizational direction (business architecture, predictive simulation of business processes of the life cycle in virtual reality) -The process area (process controlling the implementation of operational activities, including on crossprocess services and machine-to-machine interaction) -Regulatory direction (digital codification of corporate standards and knowledge, adoption of digitalization standards and network communication interfaces) -Management area (transparency of decision-making technologies by management and remote control of processes, programs for the introduction and development of digitalization as a strategic investment project) -Human resources (continuous training, teaching the basics of human-machine interaction in digital technologies).
At the next stage, on the basis of the developed graph model, the interconnections of the formed data groups with the key attributes of production systems (MEIM2) are established for all redistributions of the enterprise.

Method implementation
When implementing the developed theoretical basis the authors use the software products MS Excel, MS Access, MS Project, C#.
Selecting Microsoft Excel as one of the tools is explained by the availability of built-in functions and algorithms of the solution search, high accessibility and application visibility. MS Access was used as a main tool for forming data bases of reference and regulatory information on project indicators. MS Project was used as a main tool of the project calendar planning, tracking the as-built schedules and calculations of economic indicators of the innovative projects. The algorithm for assessing the efficiency of DTM introduction is implemented in the form of design software in the algorithmic language C#.
The developed models and software tools were applied for evaluating of the company production activity in the real sector of economy.
Industrial testing of the methodology showed its efficiency in solving problems of comparative evaluation of the efficiency of the digital transformation for the multi-techno-economic structures of the enterprises.

Results
As a result of the conducted research the authors developed a method for planning and enterpricing of the Digital Transformation for industrial company production activity in the multi -techno -economic structure.
The technique is automated using a computer.

Discussion
The technique allows forming, assessing and comparing a whole set of permitted alternative options of innovative solutions on the introduction of digital technology taking into account the set of technological repartitions of the business system of the industrial enterprise at the stages of product life cycle and production development.
The basis of the methodology is a complex of mathematical models for the formation of characteristics of means of equipping production processes; a set of particular criteria for assessing the efficiency of the enterprise's production activities as a result of the introduction of digital technologies and productions; searching for an optimal problem solution.
The industrial approbation of the technique showed its efficiency at the solution of tasks related to DTM introduction in a multi-techno-economic structure of industrial company.
Further development of the technique is provided the assessment of the effectiveness of production interaction between digital industrial enterprises within the sector of the national economy. Compliance with ethical standards all authors certify that they have no affiliations with or involvement in any organization or entity with any nancial interest or non-nancial interest in the subject matter or materials discussed in this manuscript.
The authors give their consent for publication. Competing interests the authors declare that they have no competing interests.
Funding this research has not received particular funding.
Consent to participate not applicable. Consent to publish not applicable. Availability of data and material not applicable.