CC BY
5DOI: 10.29141/2658-5081-2023-24-3-7 EDN: LYFSIZ JEL classification: N60, M21, Q01
Andrey S. Vaulin Ural State University of Economics; Ural Optical
and Mechanical Plant named after E. S. Yalamov, Ekaterinburg, Russia
Economic effect from digital integration: The case of a mechanical engineering enterprise
Abstract. Production digitalisation is a significant factor in the development of mechanical engineering that triggers countries and regions' economic growth. Implementing digital integration at industrial enterprises implies that production operations are linked through a corporate information system. The paper aims to develop and test a method for assessing the economic effect from digital integration at mechanical engineering enterprises. Methodologically, the study rests on the theories of production organisation and integration as well as the main postulates of the concept of industrial digitalisation. The study applies methods of econometric modelling. The evidence base is the information about the activities of one of the Ural macroregion's largest mechanical engineering enterprise - Production Association "Ural Optical and Mechanical Plant named after E. S. Yalamov" (UOMZ). The paper systemitises ideas about the essence of the economic effect from digital integration. The case of the enterprise is used to identify the level of specific costs in this process and to forecast their changes depending on different factors using econometric modelling. The value of the findings consists in broadening the scientific and practical understanding of the economic effects brought about by industrial digitalisation.
Keywords: digital integration; production digitalisation; economic effect; digitalisation costs; digitalisation efficiency; industrial enterprise; mechanical engineering.
For citation: Vaulin A. S. (2023). Economic effect from digital integration: The case of a mechanical engineering enterprise. Journal of New Economy, vol. 24, no. 3, pp. 136-154. DOI: 10.29141/2658-5081-2023-24-3-7. EDN: LYFSIZ. Article info: received April 25, 2023; received in revised form June 14, 2023; accepted June 28, 2023
Introduction
In the context of the digital economy and sanctions against the Russian industry,
one of the priority scientific and practical goals is increasing the economic efficiency of mechanical engineering enterprises. Achieving this goal is closely related to the digital integration at industrial enterprises, which is uniting multiple production processes into a common digital environment to shorten the production cycle, reduce the costs and fulfill production and sales plans.
However, the said results can be neutralised by specific costs due to the broken connectivity of production processes in a corporate information system. In this regard, it is required to constantly analyse these costs in order to minimise them and ensure a certain economic effect for an industrial enterprise. At the same time, the theoretical and methodological foundations of the corresponding assessment are not well developed at present.
The purpose of the study is developing and testing a method to calculating the economic effect from digital integration at mechanical engineering enterprises.
To achieve this purpose, the following objectives were set:
1) to determine the essence of the economic effect and digital integration costs for mechanical engineering enterprises;
2) to develop and apply a method for assessing the economic effect from digital integration to identify its actual level during a certain time period;
3) to test an econometric method for assessing the effect from digital integration using the case of a mechanical engineering enterprise in order to forecast its value depending on changes in various factors.
Theoretical review
The study rests on the theories of integration, organisation of production and efficiency in the context of industrial digitalisation. Their analysis made it possible to clarify the economic essence of the effect and costs of digital integration at an industrial enterprise.
We analysed the concept "integration" in relation to industrial enterprises' activities, and in particular considered the definitions of economic, logistical [Tsygankov, 2021, pp. 321-322], financial [Kurbanov, 2015, pp. 107-111], corporate [Polukhin, 2007, pp. 1-11] and information [Vichugova et al., 2012, pp. 113-117] integration, as well as such related concepts as industrial [Golovina, Levchenko, Yurchenko, 2021, pp. 227-228] and digital scientific and technical cooperation [Ibid., p. 229]. Based on the review we found the following.
Regardless of its type, integration is understood as the state of connectivity of individual different parts or functions of an industrial enterprise. In addition to the listed types of integration, the term "digital integration" has appeared in science relatively recently, due to the active spread of the digital economy and digital transformation. It
is worth noting that the objects to apply this term are multiple - not only individual industrial enterprises and production processes, but also inter-organisational associations at the level of industries and the state.
Kutsenko [2019, p. 171] presents the digital integration of the Eurasian Economic Union member states, which includes automating processes, end-to-end digitalisa-tion of all physical assets and their inclusion in the digital ecosystem based on a digital platform or sets of digital platforms. In our opinion, this understanding of the term is also applicable to industrial enterprises. Other researchers agree with this point of view [Shahatha Al-Mashhadani et al., 2021, p. 2945]. However, we need to clarify which specific processes should be subject to digitalisation, because they are the key source of economic benefits from digital integration [Kalogiannidis et al., 2022, p. 349].
Taking into account the life cycle of mechanical engineering enterprises and their products (machines, assembly units, parts), the following production processes were identified:
- scientific and technical (design and technical preparation of production, including the provision of production equipment and tooling, as well as production scheduling);
- production and technological (foundry, mechanical, finishing, assembly operations, supply of raw and other materials, semi-finished products, assembly and packaging of products, technical control of product quality and logistics) [Smyshlyaeva, 2019, p. 352].
Due to the ambiguity in scientific research, we clarified what can be considered an indicator of digital integration at an industrial enterprise. In a general, an indicator is understood as a certain state of an object, for instance, its optimisation, i.e., ensuring a more efficient state of production processes within the framework of their interaction with each other in order to achieve progressive internal and external development of an industrial enterprise, increase profits by reasonably reducing costs [Tkachenko, Kizikov, 2011, p. 31].
Considering the specified definition as well as the generally accepted definition of integration, we will understand the indicator of digital integration as a state of connectivity of information flows about selected production processes. This is the main goal of organising a corporate information system at an industrial enterprise [Batkovskiy, Fomina, 2020, pp. 98-99].
Failure to achieve this indicator means that the digital integration of production processes does not deliver the satisfactory result and an industrial enterprise may suffer from additional costs due to the violation of these processes' connectivity.
Synthesising the presented propositions and definitions, we interpreted the essence of digital integration for mechanical engineering enterprises.
Digital integration of production processes is the provision of connectivity between scientific, technical and production and technological operations and works based on the combination of information flows caused by them, including data about the involved digital technologies, economic assets and other types of resources owned by an industrial enterprise, which is carried out using a corporate information system.
Further, we revealed the economic effect's essence of the digital integration of production processes based on studies on efficiency assessment [Meyer, 2004, p. 198; Li-mareva, Limarev, 2014, p. 7; Dubrovskiy, Ivanova, Chuprakova, 2019, p. 92; Orekhova, Misyura, Kislitsyn, 2020, p. 50; Plakhin, Blinkov, 2022, p. 141] and the research on the specifics of digital transformation [Piller, Moeslein, Stotko, 2004, p. 440; Li, Merenda, Venkatachalam, 2009, p. 52; Kuusisto, 2017, p. 341; Vogelsang, Brink, Packmohr, 2020, p. 25; Phuyal et al., 2020, p. 18; Reis et al., 2020; Fremont, 2021, p. 94; Haijia, Cailin, 2021, p. 24; Sledziewska, Wloch, 2021, p. 282; Verhoef et al., 2021, p. 900; Bhatia, Kumar, 2022, p. 2439; Brink, Packmohr, 2022, p. 4849; Guo, Chen, 2023, p. 3124].
We found that the economic effect from digital integration of production processes comes from minimising specific costs - additional operating costs arising when the connection between scientific-technical and production-technological processes of an industrial enterprise is disrupted due to insufficient connectivity of digital technologies with each other or their interaction with other resources of an industrial enterprise.
To assess this effect in practice, we classified digital integration costs of production processes, and the most significant ones are the following:
- administration costs, which are additional time spent by personnel on entering data into the corporate information system (hereinafter referred to as CIS), updating and monitoring the correctness of this data;
- production asymmetry costs arising from untimely updating of data in CIS (for example, in terms of norms and standards of labour intensity, piece rates, technological process operations, etc.);
- qualification costs associated with additional time spent by personnel on production tasks due to lack of knowledge about ways to process big data;
- infrastructure costs resulting from unforeseen downtime due to the suspension of CIS for various reasons.
Further, a method for their assessment was developed, taking into account the essence of every type of digital integration costs for an industrial enterprise.
Materials and methods
Object of study. We chose the largest mechanical engineering enterprise of the defence industry in the Ural macroregion as the object of study. It is Ural Optical and Mechanical Plant named after E. S. Yalamov (UOMZ), which is located in Ekaterinburg.
The Ural macroregion is the center of the Russian industry - more than a dozen types of all-Russian and world-class industries are located there. It can serve a catalyst for renewing the entire country's economy to enter a new stage of development. The defence industry is a unique driver located in its territory, because it creates high-tech products for special purposes.
The intensive development of industry in the Urals is due to the basic sectors of the regional economy, the mechanical engineering industry in particular. This development manifests itself in the creation of new technological industries, high innovative potential, and the active use of digital technologies.
Digitalisation in the Sverdlovsk oblast exceeds the all-Russian level by most indicators, which was especially evident in 2019 (the digitalisation index in the Sverdlovsk oblast was 32, in the Russian Federation it was 29) [Silin, Kokovikhin, 2021, p. 13]. There is an increase in the use of digital technologies at the Sverdlovsk oblast's enterprises, which indicates a rise in the availability of the relevant infrastructure and the readiness of enterprises to perform entrepreneurial activities in the digital economy. Thus, over 2005-2019, the share of enterprises using server hardware grew from 10 to 60 %, and the share of organisations operating cloud servers went up from 14 to 30 % [Silin, Kokovikhin, 2021, p. 13].
The said reasons served as the basis for choosing the object of study: the active digitalisation of production processes there makes it possible to test the method we propose.
A method for assessing the economic effect from digital integration at an industrial enterprise. The digitalisation of production processes accelerates the search for the ways to analyse its results. With this in mind, we propose a method that includes the following steps:
1) applying a set of methods of economic analysis, including the calculation of absolute, relative and average values, statistical evaluation of relationships;
2) stepwise study of digital integration's economic effect, including administration, production asymmetry, qualification and infrastructure costs that arise during the commercial operation of CIS;
3) systemic econometric measurement of the effect and costs caused by digital integration in order to predict them based on predictor variables.
At the first stage, we propose to calculate digital integration costs of production processes. To subsequently analyse their dynamics, the measurement was carried out in physical units (hours) in order to exclude inflationary factors. The following formulas were used for calculations.
Administration costs calculation:
DIC(p1) = AN x Ca x WTF,-, (1)
where DIC(pi) is administration costs for the period, hour; AN is average number of employees at an industrial enterprise for the period, people; Ca is administration coefficient; WTF, is the actual work time fund per person for the period, hour.
Hereinafter, the average number of employees and the average actual work time fund are determined using generally accepted methods that do not need to be introduced. However, the administration coefficient requires special consideration:
ET
C = (2)
a WTF , (2)
vv 1 ¿(day)
where Ca is administration coefficient; ET,(day) is actual time of entering data into CIS per person per day, hour; WTF^y is actual work time fund per person per day, hour.
The specified coefficient represents the share of the actual work time of employees for entering work data into CIS. The smaller its value is, the more efficient the process is, because the duration of system administration is shorter. The problem of labour-intensive entering and updating data in CIS substantiates the validity of the chosen calculation method. This issue is typical for large mechanical engineering enterprises - manufacturers of high-tech products, characterised by complex manufacturing processes and product composition.
Production asymmetry costs calculation:
DIC(pa) = ANpr x Cpa x WTF,, (3)
where DIC(p2) is production asymmetry costs for the period, hour; ANpr is average number of key production workers for the period, people; Cpa is production asymmetry coefficient; WTF, is actual work time fund per person for the period, hour.
The formula below determines the production asymmetry coefficient:
r
TT _ WTF
C _ KM i(mon) ;f/-> ^ n.
Sa - WTp » 11 Sa - U>
yv i(mon)
Cpa = 0,ifCpa <0,
(4)
where Cpa is production asymmetry coefficient; LIi(mon) is actual labour intensity of operations performed per one production worker per month, hour; WTFi(mon) is actual work time fund per production worker per month, hour.
This coefficient shows how much the actual labour intensity of operations performed according to CIS exceeds the actual work time fund per one production worker according to the time sheet per month. The closer the value of this coefficient to zero is, the smaller the deviation is, because the degree of production asymmetry in CIS is lower. If the value of the coefficient is negative, the actual work time fund, according to the time sheet, exceeds the complexity of the operations
performed in the CIS. Then there are not production asymmetry costs and, accordingly, the coefficient is equal to zero. Qualification costs calculation:
DIC(p3) = ANkw x CSg x WTF, (5)
where DIC(p3) is qualification costs for the period, hour; ANkw is actual average number of knowledge workers for the period, people; Csg is skills gap coefficient; WTF; is actual work time fund per person for the period, hour. The skill gap coefficient is calculated by the formula:
where Csg is skill gap coefficient; ANkwt is actual average number of knowledge workers for the period trained in big data analysis, people; ANkw is actual average number of knowledge workers for the period, people; N is the standard time spent unnecessarily on the performing work tasks due to a lack of knowledge in the processing of big data (determined by an expert, in this paper, it is 30 %).
The skill gap coefficient represents the share of unproductive work time of knowledge workers due to a lack of knowledge in big data processing. The lower the value of this coefficient is, the lower the loss of work time is. Infrastructure costs calculation:
DIC(pJ = ANwpc x C x WTFi, (7)
where DIC(p4) is infrastructure costs for the period, hour; ANwpc is the actual average number of employees involved in working with a personal computer for the period, people; Q is infrastructure coefficient; WTF; is average actual work time fund per person for the period, hour.
The infrastructure coefficient is calculated using the formula:
WTF - WTF
p _ YY 1 ^iptmon) VV ia(mon) , Qs
'" WTF ' (8)
vv ip(mon)
where C, is infrastructure coefficient; WTFip(mon) is planned work time fund per month per one employee involved in working with a personal computer, hour; WTFia(mon) is actual work time fund per month per one employee involved in working with a personal computer, minus downtime due to failure of CIS, hour.
The lower the value of this coefficient is, the lower the proportion of lost work time is.
The second stage involves the calculation of the total digital integration costs of production processes:
DIC = DIC(pO + DIC(p2) + DIC(p3) + DIC(p4), (9)
where DIC is digital integration costs of production processes for the period, hour; DIC(p1) is administration costs per period, hour; DIC(p2) is production asymmetry costs for the period, hour; DIC(p3) is qualification costs for the period, hour; DIC(p4) is infrastructure costs for the period, hour.
In order to further analyse the dynamics of indicators and exclude the dependence of the indicator on changes in the number of employees, the calculation of the average value was applied:
do
where DIC is average digital integration costs of production processes for the period, hour/person.; DIC is digital integration costs of production processes for the period, hour; AN is average number of employees at an industrial enterprise for the period, people.
At the third stage, the economic effect from digital integration is calculated: - in physical units:
EDIn = DIC1 - DIC0, (11)
where EDIn is economic effect from digital integration of production processes in physical terms, hour; DIC1 is digital integration costs of production processes for the reporting period, hour/people; DIC0 is digital integration costs of production processes for the previous similar period, hour/people; - in monetary units:
EDIc = EDIn x HWR1, (12)
where EDIc is economic effect from digital integration of production processes in monetary terms, rubles/people; HWR1 is hourly labour cost of an employee at an industrial enterprise for the reporting period, rubles.
Based on the calculation results of the listed indicators, they are evaluated in dynamics. The data obtained form the basis for further forecasting the effect from digital integration based on econometric analysis.
An econometric approach to evaluating the effect from digital integration at an industrial enterprise. Constantly changing business landscape, the aggressive influence of external factors on industrial enterprises may result in disruption of the internal connectivity of production processes due to their transformation. This explains
the relevance of identifying the current state of their digital integration and predicting its future development.
In this regard, we conducted a correlation and regression analysis of digital integration costs of production processes, which consists of several stages.
1. Determining the response variables of the econometric model.
The average value of the i-th type of digital integration costs of production processes was chosen as the response variable:
DIC,
where y is the response variable of the econometric model; DIC t is average value of the i-th type of digital integration costs of production processes for the period, hour/ person; DIC; is i-th type of digital integration costs of production processes for the period, hour; AN is average number of employees at an industrial enterprise for the period, people.
Taking into account the content of digital integration costs, the following types of response variables were identified:
- average administration costs for the period, hour (y1);
- average production asymmetry costs for the period, hour (y2);
- average qualification costs for the period, hour (y3);
- average infrastructure costs for the period, hour (y4).
2. Selecting predictor variables that influence the response variables of the econometric model.
On the basis of the pair correlation method, each i-th type of costs has its own predictor variable (Table 1).
Table 1. Predictor and response variables of the econometric model
Response variable (y) Predictor variable (x)
Average administration costs for the period (yx) Duration of the product administrative cycle for the period (xi)
Average production asymmetry costs for the period (y2) Growth rate of actual hourly labour cost of an employee at an industrial enterprise for the period (x2)
Average qualification costs for the period (y3) Average time spent on training staff in digital technologies for big data analysis over the period (x3)
Average infrastructure costs for the period (y4) Average recovery time of a corporate information system (x4)
The choice of predictor variables listed in Table 1 is due to the following.
A. The duration of the product administrative cycle (x1) is chosen as a variable defining the average administration costs (y1). It is calculated by the formula:
WTF
x1 = APC = ^, (14)
where x1 is the predictor variable of the econometric model; APC is the duration of the product administrative cycle for the period, hour/piece; WTFtot is the total actual work time fund for an industrial enterprise for the period, hour; PV is actual production volume of conditional products for the period, piece.
This indicator shows how much total work time, on average, is spent on the administration of one conditional product. The latter is understood as the basic product, which units are used to measure each product of an industrial enterprise for the purpose of a comparable analysis of labour productivity. The shorter the cycle time is, the less time is required to administer the products, which leads to a reduction in the considered costs.
B. The average production asymmetry cost (y2) is affected by the growth rate of actual hourly labour cost of an employee at an industrial enterprise for the period (x2), which is determined by the formula:
HC
x2 = GRhc = 1^, (15)
where x2 is predictor variable of the econometric model; GRhc is growth rate of the actual hourly labour cost of an employee at an industrial enterprise for the period; HCa is actual hourly labour cost of an employee at an industrial enterprise for the period, rubles; HCm is market hourly labour cost of an employee at an industrial personnel for the period, rubles.
The hourly cost is determined by taking into account insurance premiums, district coefficient, incentive payments and other allowances.
This indicator demonstrates how many times the actual hourly labour cost of an employee at an industrial enterprise exceeds the market one. The lower the actual hourly labour cost of an employee (under full load) than the market wages is, the less interest there is in ensuring the reliability of production data in CIS and, accordingly, the higher the costs are.
C. The average qualification costs (y3) depends on the average time spent on training personnel in digital technologies for big data analysis (x3). This indicator is determined by the formula:
ANkwt x B0
*3 = TS= (16)
kw
where x3 is predictor variable of the econometric model; TS is average time spent on training staff in digital technologies for big data analysis for the period, hour/people; ANkwt is actual average number of personnel trained during the period, people; B0 is average duration of one training course, hour; ANkw is actual average number of knowledge workers for the period, people.
The more extensive the training course is, the deeper knowledge and competencies employees acquire, it speeds up their tasks fulfillment and reduces digital integration costs of production processes.
D. The predictor variable for the average infrastructure costs (y4) is the average recovery time of CIS (x4), determined by the formula:
WTF - WTF
x _ J^p _ VV 1 ip(mon) VV 1 ^(mon) ( 17)
where x4 is predictor variable of the econometric model; RT is average recovery time of CIS, hour/piece; WTFip(mon) is planned work time fund per month per one employee involved in working with a personal computer, hour; WTFia(mon) is actual work time fund per month per one employee involved in working with a personal computer, minus the downtime due to failure of CIS, hour; NF is average number of CIS failures, pieces.
The shorter the CIS recovery is, the less staff downtime is, which means more efficient use of labour resources and high-quality digital infrastructure.
3. Analysing the presence and strength of relationship.
At this stage, the pair correlation coefficient was calculated, and then statistically verified. The strength of the relationship was assessed using the Chaddock scale.
4. Determining the mathematical model of the relationship.
Since the direct influence of the predictor variables on the response ones was revealed, the linear regression analysis was used. The significance of equations and regression coefficients was assessed using well-known statistical methods [Shorokhova, Kislyak, Mariev, 2015].
Research results
The proposed method for assessing the economic effect from digital integration was tested using the case of UOMZ. The enterprise is one of the largest representatives of mechanical engineering in the defence industry of the Ural macroregion. Its advantage against similar enterprises in the industry is its enormous experience in the industrial operation of modern corporate information systems, integrating the whole variety of scientific, technical, production and technological processes.
Note that in this paper, the initial data for calculations cannot be presented due to the high requirements for the economic and information security of defence
enterprises. For the same reason, the above calculations were partially corrected, which, however, did not affect the final research conclusions.
Taking into account these circumstances, the implementation of the developed method produced the following scientific and practical results.
1. Calculation of the economic effect from reducing digital integration costs of production processes (Table 2).
Table 2. Economic effect from reducing digital integration costs at UOMZ, 2020-2022
Year Value, rubles/people (adjusted data)
2020 -22,642
2021 -20,538
2022 -27,532
The data in Table 2 show a stable reduction of costs in 2020-2022, which indicates the achievement of the economic effect from digital integration in the enterprise under consideration.
Figure 1 shows the dominance of administration and qualification costs - 45-48 and 26-49 %, respectively. This confirms the need to find and implement ways to reduce the duration of data entry into CIS, as well as the relevance of improving the skills of employees in processing big data.
DIC(pi) DIC(p2) DIC(p3) DIC(p4)
Fig. 1. Structure of digital integration costs of production processes at UOMZ,
2021-2022 (adjusted data), %: DIC(p ]) is average administration costs; DlC(p2) is average production asymmetry costs; DIC(p3) is average qualification costs; DIC(p4) is average infrastructure costs
Further analysis was supplemented by the search for factors that affect digital integration costs of production processes through the calculation of the pair correlation coefficients.
2. Calculation of pair correlation coefficients (Table 3).
Table 3. Pair correlation coefficients
Response variable (y) Predictor variable (x,) Pair correlation coefficient (r)
Average administration costs for the period (yi) Duration of the product administrative cycle for the period (x1) 0.948
Average production asymmetry costs for the period (y2) Growth rate of actual hourly labour cost of an employee at an industrial enterprise for the period (x2) -0.917
Average qualification costs for the period (y3) Average time spent on training staff in digital technologies for big data analysis over the period (x3) -0.935
Average infrastructure costs for the period (y4) Average recovery time of a corporate information system (x4) 0.844
According to the Chaddock scale, digital integration costs of production processes at UOMZ are strongly affected by the duration of the product administrative cycle, the degree of relative deviation of the actual hourly labour cost of an employee from the market level, the average time spent on training personnel in digital technologies for big data analysis (pair correlation coefficient is between 0.9 and 0.99). Moreover, the CIS recovery time has a strong influence (pair correlation coefficient is between 0.7 and 0.9).
The first and fourth correlation coefficients are positive (Table 3). This suggests that the higher the duration of the product administrative cycle or the average CIS recovery time is, the greater digital integration costs of production processes the enterprise has. At the same time, the values of the second and third coefficients are negative, so the higher the growth rate of hourly labour cost of an employee and average time spent on training employees are, the lower the digital integration costs of production processes are.
Based on the results of identifying the correlation between the response variables and predictor variables, it is possible to proceed to the next stage and construct econometric models.
3. Construction of econometric models.
3.1. Between the value of the product administrative cycle (x1) and the average value of administration costs (y1):
y1 = 0.02 x1 - 332.9.
This relationship is shown in Figure 2.
Duration of the product administrative cycle
Fig. 2. Relationship between the duration of the product administrative cycle and average administration costs at UOMZ, 2020-20221
The obtained relationship indicates that the shorter the product administrative cycle is, the lower the administration costs at the object under study are.
3.2. Between the growth rate of the actual hourly labour cost of an employee (x2) and the average production asymmetry costs (y2):
y2 = 251.1 -241.92 x2. Graphically this relationship is shown in Figure 3.
Growth rate of actual hourly labour cost
Fig. 3. Relationship between the growth rate of actual hourly labour cost and average production asymmetry costs at UOMZ, 2020-2022
1 Hereafter, the graphs do not show the actual values of the indicators due to the increased requirements for the economic and information security of defense industry enterprises.
The obtained relationship shows that the higher the growth rate of the actual hourly labour cost of an employee is, the lower the average production asymmetry costs are.
3.3. Between the average time spent on training staff in digital technologies for big data analysis (x3) and the average value of qualification costs (y3):
y3 = 210.74 - 1.95 x3. Graphically, this relationship is shown in Figure 4.
o u
ö
0
• »—I
id
<j
•»—I
13 &
& >H
1
Average time spent on training personnel in big data analysis technologies
Fig. 4. Relationship between the average time spent on training personnel in big data analysis technologies and average qualification costs at UOMZ, 2020-2022
The presented relationship indicates that the longer the staff training course is, the lower the qualification costs are.
3.4. Between the average CIS recovery time (x4) and the average infrastructure costs (y4):
y4 = 4.81 + 30.28 x4. Graphically, this relationship is shown in Figure 5.
CO
O o
a
B
<j
>H
CO
at
<L> 60 rt >H
I
• __
Average CIS recovery time
Fig. 5. Relationship between the average CIS recovery time and average infrastructure costs at UOMZ, 2020-2022
This relationship shows that the higher the average CIS recovery time is, the greater the infrastructure costs are.
In addition, it is worth noting that all the listed equations and coefficients included in them have passed a general test for significance, which indicates the possibility of their application.
Thus, the practical implementation of the developed approach allowed assessing the economic effect from the digital integration of production processes at UOMZ and identifying the factors affecting its value.
Conclusion
In our study, we proposed and implemented a method for assessing the economic effect from the digital integration of production processes. The scientific results are the following:
1) the essence of the economic effect from digital integration for mechanical engineering enterprises is determined: it consists in minimising specific costs at the stage of industrial operation of a corporate information system. This proposition became the basis for subsequent empirical analysis;
2) a method for evaluating the economic effect from digital integration has been proposed and tested. This allowed identifying the actual level of the effect in a certain period of time for a particular industrial enterprise;
3) the effect from digital integration in case of a machine-building enterprise has been assessed based on the presented econometric method, which allowed predicting the level of the effect in dynamics depending on changes in predictor variables.
The theoretical and methodological significance of this study lies in expanding scientific ideas concerning the methods of economic analysis of the digital integration of production processes.
Testing the proposed methodological toolkit on the example of several mechanical engineering enterprises is a promising direction for further research. The results obtained will become the basis for the development of universal recommendations for increasing the economic effect from digital integration of production processes.
References
Batkovskiy A. M., Fomina A. V. (2020). Information support of automated control systems at the enterprises of the military-industrial complex. Ekonomicheskie issledovaniya i razrabotki = Economic Development Research Journal, no. 6, pp. 97-104. (In Russ.)
Vichugova A. A., Vichugov V. N., Dmitrieva E. A., Tsapko G. P. (2012). Methods and means of integrating information systems within the framework of a single information space of design. Vestnik nauki Sibiri = Siberian Journal of Science, no. 5(6), pp. 113-117. (In Russ.)
Golovina A. N., Levchenko R. Yu., Yurchenko K. P. (2021). New contours digital scientific and technical cooperation. Ekonomika: vchera, segodnya, zavtra = Economy: Yesterday, Today, Tomorrow, vol. 11, no. 2-1, pp. 226-237. DOI: 10.34670/AR.2021.20.86.028. (In Russ.)
Dubrovskiy V. Zh., Ivanova E. M., Chuprakova N. V. (2019). Problems of assessing defence contractors' effciency. Journal of New Economy, vol. 20, no. 5, pp. 92-107. DOI: 10.29141/2658-50812019-20-5-6. (In Russ.)
Kurbanov R. A. (2015). Economic integration: Concept, theory and types. Problemy ekonomiki i yuridicheskoy praktiki = Economic Problems and Legal Practice, no. 2, pp. 107-111. (In Russ.)
Kutsenko N. A. (2019). Digital integration is an important vector for the development of the digital economy. Strategiya predpriyatiya v kontekste povysheniya ego konkurentosposobnosti = Enterprise Strategy in the Context of Increasing its Competitiveness, no. 8, pp. 169-172. (In Russ.)
Limareva Yu. A., Limarev P. V. (2014). Evolution of the category "efficiency" in economic science. Universum: Ekonomika i yurisprudentsiya: elektronnyy nauchnyy zhurnal = Scientific electronic journal UNIVERSUM JURIS, no. 4 (5), pp. 7-12. (In Russ.)
Meyer M. V. (2004). Assessment of business efficiency. Moscow: Vershina Publ. 272 p. (In Russ.)
Orekhova S. V., Misyura A. V., Kislitsyn E. V. (2020). Managing the increasing returns of a hightech business model in industry: Classic and ecosystem effects. Upravlenets = The Manager, vol. 11, no. 4, pp. 43-58. DOI: 10.29141/2218-5003-2020-11-4-4. (In Russ.)
Plakhin A. E., Blinkov I. O. (2022). Effects of intersubject interaction of participants in the industrial ecosystem. Proc. 5th Int. Sci.-Prac. Conf. "Management and Entrepreneurship in the Paradigm of Sustainable Development" (pp. 140-142). Ekaterinburg: Ural State University of Economics. (In Russ.)
Polukhin P. Yu. (2007). Quantitative methods for evaluating the effectiveness of corporate integration. Rossiyskiy ekonomicheskiy internet-zhurnal = Russian Economic Internet Journal, no. 3, pp. 1-11. (In Russ.)
Silin Ya. P., Kokovikhin A. Yu. (2021). Workforce' digital competencies development according to the "smart specialization" of the region concept. Human Progress, vol. 7, no. 4, 13. DOI: 10.34709/ IM.174.13. (In Russ.)
Smyshlyaeva E. G. (2019). Organisation of the production process as part of an industrial enterprise development strategy. Byulleten nauki i praktiki = Bulletin of Science and Practice, vol. 5, no. 3, pp. 350-356. https://doi.org/10.33619/2414-2948/40/43. (In Russ.)
Tkachenko I. N., Kizikov I. V. (2011). Business process optimization in integrated holding structures. Upravlenets = The Manager, no. 3-4 (19-20), pp. 40-46. (In Russ.)
Tsygankov A. A. (2021). Logistic integration in the context of digital globalization. In: Scientific essays "Management and marketing: Experience and problems" (pp. 321-322). Minsk: Belarus State Economic University. (In Russ.)
Shorokhova I. S., Kislyak N. V., Mariev O. S. (2015). Methods of statistical analysis. Ekaterinburg: Ural Federal University. 300 p. (In Russ.)
Bhatia M. S, Kumar S. (2022). Critical success factors of industry in automotive manufacturing industry. IEEE Transactions on Engineering Management, vol. 69, issue 5, pp. 2439-2453.
Brink H., Packmohr S. (2022). Analyzing barriers to digital transformation in the German engineering industry - a comparison of digitalized and non-digitalized enterprises. Proc. 55th Hawaii Int. Conf. on System Sciences (pp. 4849-4858).
Fremont V. (2021). The digital transformation of the manufacturing industry. Metamorphic changes and value creation in the industrial network. Digital comprehensive summaries of Uppsala dissertations from the faculty of science and technology. Uppsala: Acta Universitatis Upsaliensis. 124 pp.
Guo X., Chen X. (2023). The impact of digital transformation on manufacturing-enterprise innovation: Empirical evidence from China. Sustainability, vol. 15, issue 4, 3124. https://doi.org/10.3390/ su15043124.
Haijia L., Cailin Ya. (2021). Digital transformation of manufacturing enterprises. Procedia Computer Science, 187, pp. 24-29, https://doi.org/10.1016Aj.procs.2021.04.029.
Kalogiannidis S., Kalfas D., Chatzitheodoridis F., Kontsas S. (2022). The Impact of digitalization in supporting the performance of circular economy: A case study of Greece. Journal of Risk and Financial Management, vol. 15, issue 8, 349. https://doi.org/10.3390/jrfm15080349.
Kuusisto M. (2017). Organizational effects of digitalization: A literature review. International Journal of Organization Theory and Behavior, vol. 20, issue 03, pp. 341-362. DOI:10.1108/IJOTB-20-03-2017-B003.
Li J., Merenda M., Venkatachalam A. R. (2009). Business process digitalization and new product development: An empirical study of small and medium-sized manufacturers. International Journal of E-Business Research, vol. 5, issue 1, pp. 49-64. DOI:10.4018/jebr.2009010103.
Phuyal S., Bista D., Izykowski J., Bista R. (2020). Design and implementation of cost-efficient SCADA system for industrial automation. International Journal of Engineering and Manufacturing, vol. 10, issue 2, pp. 15-28. DOI: 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, vol. 15, issue 4, pp. 435444. DOI: 10.1080/0953728042000238773.
Reis J., Amorim M., Melao N., Cohen Y., Rodrigues M. (2020). Digitalization: A literature review and research agenda. Proc. 25th Int. Joint Conf. on Industrial Engineering and Operations Management - IJCIEOM (pp. 443-456). Cham: Springer. https://doi.org/10.1007/978-3-030-43616-2_47.
Shahatha Al-Mashhadani A. F., Qureshi M. I., Hishan S. S., Md Saad M. S., Vaicondam Y., Khan N. (2021). Towards the development of digital manufacturing ecosystems for sustainable performance: Learning from the past two decades of research. Energies, vol. 14, issue 10, 2945. https://doi. org/10.3390/en14102945.
Sledziewska K., Wioch R. (2021). The economics of digital transformation. London: Imprint Rout-ledge. 304 p.
Verhoef P. C., Broekhuizen T., Bart Ya., Bhattacharya A., Dong J. Q., Fabian N., Haenlein M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research, vol. 122, pp. 889-901, https://doi.org/10.1016/j.jbusres.2019.09.022.
Vogelsang K., Brink H., Packmohr S. (2020). Measuring the barriers to the digital transformation in management courses - A mixed methods study. In: Buchmann R. A., Polini A., Johansson B., Karagiannis D. (eds.) Perspectives in business informatics research (pp. 19-34). Cham: Springer. https://doi.org/10.1007/978-3-030-61140-8_2.
Information about the author
Andrey S. Vaulin, Applicant for Candidate Degree of Enterprises Economics Dept. Ural State University of Economics, Ekaterinburg, Russia; Deputy Director for Economy and Finance. Ural Optical and Mechanical Plant named after E. S. Yalamov, Ekaterinburg, Russia. E-mail: Andrey50396@gmail.com
© Vaulin A. S., 2023
