CC BY
4DOI: 10.29141/2658-5081-2022-23-4-5 EDN: LNOZGX JEL classification: D24, D25
Svetlana V. Orekhova Ural State University of Economics, Ekaterinburg, Russia
Ivan A. Butakov OOO MMC-Steel, Verkhnyaya Pyshma, Sverdlovsk oblast;
Ural State University of Economics, Ekaterinburg, Russia
Allocation of a resource portfolio in a group of industrial enterprises
Abstract. Efficient resource allocation is the key to ensuring the economic growth. Business integration generates both coordination and distribution effects of resource allocation. The former reflects the efficiency of the whole group in terms of resources distribution, the latter illustrates benefits and/or costs of its individual participants obtained due to resources dispensed to them. The paper develops and tests a method for assessing distributive allocative characteristics of resources in a group of enterprises. Neoclassical and new institutional economics constitute the methodological basis of the research, including the theories of production possibilities and industrial organisation, and the contract theory. The 2014-2021 data on economic performance of OOO MMC-Steel, a local group of enterprises in control of the Ural Mining Metallurgical Company, one of the largest holdings in Russia, are studied using general (system analysis and synthesis, typology) and economic statistical methods. According to the findings, there are seven types of resource allocation, which impact on the coordination and distribution effects in a group of industrial enterprises. The study develops a method for assessing three types of distributive allocation of resources: factor- and product-based, intra-organisational. The testing of this toolkit in three groups of resources (production, material, and labour) demonstrated that OOO MMC-Steel features a significant heterogeneity, and the level of allocation of resource portfolio is substantial. The proposed approach enables the creation of a resources strategy that takes into account the solutions to the problem of resources' allocative efficiency in a group of industrial enterprises.
Keywords: resource allocation; resources; holding; industrial group; resources strategy; industrial enterprise.
For citation: Orekhova S. V., Butakov I. A. (2022). Allocation of a resource portfolio in a group of industrial enterprises. Journal of New Economy, vol. 23, no. 4, pp. 87-120. DOI: 10.29141/2658-5081-2022-23-4-5. EDN: LNOZGX. Article info: received June 29, 2022; received in revised form August 20, 2022; accepted August 29, 2022
Introduction
In times of turmoil, creating rigid forms of integration leading to active consolidation of resources is a sound business strategy [Butakov, 2021]. In most cases, the reasons behind this strategy are often associated with minimisation of transaction costs and optimisation of resources through the effective interaction of all its participants.
Taken broadly, a resource strategy is a set of decisions about the volume and quality of necessary resources and about the behaviour of the enterprise in the resource market. These decisions are based on two groups of rules (institutions), these are coordination and distribution rules [Shastitko, 2010, p. 137], which, in turn, lead to the consistent effects. Coordination rules deal with the issues of optimal allocation of resources within the group, that is, they provide for assessing the efficiency of the group as a single object. Distribution rules illustrate the efficiency of individual members of the group, and hence their motivation for intra-group interaction.
According to Stiglitz, the rules chosen determine not only the efficiency of resources, but also their distribution; it is impossible to consider these aspects in isolation [Stiglitz, 2002, p. 503]. The purpose of the article is to theoretically comprehend and methodologically support the assessment of the allocative characteristics of resources owned by a group of industrial enterprises and determined by the distribution rules.
To achieve the purpose, three objectives are to be accomplished: firstly, to identify the main types of allocative characteristics of industrial groups' resources; secondly, to develop methodological tools for measuring the level of resource allocation determined by the distribution rules; thirdly, to carry out a structural assessment of the resource portfolio of the group of industrial enterprises OOO MMC-Steel and interpret the results.
Resource allocation: The theoretical framework
According to economic theory, allocation (from Latin al ("near") + locatio ("location")) is a distribution of scarce (limited) resources in line with the set goals [Neza-maikin, 2006, p. 72].
The genesis of research on resource allocation was within the framework of various economic theories and approaches.
Neoclassical economics considers enterprise as a system, whose activity builds on transforming resources into products. At that, there are restrictions in terms of the technology used, consumers, competitors, suppliers and other counterparties. An important nuance in this theory is the assumption that in any possible situation a firm chooses a feasible option for its functioning that maximises its profit [Strizha-kova, 2016, p. 232]. An efficient combination of resources is such a combination that
allows for the production of goods with minimal opportunity costs. Hence, from the viewpoint of the neoclassicists, the choice of resources and their distribution depend on two main factors - technology and prices of the factors of production. Thus, allocation of the factors of production (hereinafter referred to as factor-based allocation) is a certain ratio between the factors of labour (number of jobs or costs per employee) and capital (investment in technology and, roughly speaking, automation of jobs).
According to opportunity costs theory and production possibilities theory derived from it, the production of various goods is alternative to each other due to the use of limited resources. In other words, the enterprise should decide on what types of products and in what quantity it can produce given the limited resources. This approach determines the so-called product-based allocation of resources.
In a group of industrial enterprises, the issue of resource allocation among business units is of fundamental importance. What is meant here is the so-called intra-organisational allocation.
Researchers categorise resources into two groups: 1) public goods, i.e., resources that can be used in several projects (divisions) simultaneously and without coming into conflict. These include trademarks, technologies, best management practices; 2) private goods, i.e., resources that are more difficult to manage due to competition between departments (for example, investment in equipment) [Collis, Montgomery, 2008, p. 37].
The problem of intra-organisational allocation arises precisely in relation to a group of private goods and can be explained by three factors.
Firstly, in business structures, in addition to formally unified goal-setting, there are different interests of participants (individual enterprises and their managers), which lead to the problem of initial distribution and further redistribution of resources. If malicious intent is not the case, the inefficiency of distribution can be caused by differences in management strategies, 'target functions' of business units, understanding the success of projects and the functioning of the entire group of enterprises.
Furubotn and Richter highlight that "In effect, the market value of an asset and its allocation are "controlled" by its supply and demand. <.. .> In this sense, the competitor is the best supervisor of the use of resources a society can find. Each resource will go to the particular owner who expects the resource to yield the highest value" [Furubotn, Richter, p. 96]. Thus, intra-organisational coordination in hierarchical structures predetermines the emergence of an institutional trap, because the bundle of property rights to resources is split, and this shifts the real incentives for participants in economic interactions. From this perspective, managing a group of enterprises has the effect of an internal monopoly on the resource, which distorts the principles of efficient property management [Eucken, 1952, p. 275].
In case if malicious intent is obvious, there arises X-inefficiency [Leibenstein, 1978] conditioned by a significant discrepancy between the goals of agents and principals and the incompleteness of contracts. Due to opportunistic behaviour, managers of enterprises tend to minimise their labour cost to the detriment of the goal of profit maximisation, which is set by the owners. If the contract relations between the customer and the contractor are not clearly defined, this gives the agent extra room for opportunism. Difficulties in the efficient allocation of resources can also emerge as a result of informational problems, i.e., imperfect foresight and informational asymmetry [Furubotn, Richter, 2005, p. 108]. Thus, the study of resource allocation is reflected in the principal-agent problem and contract theory.
Secondly, determining internal transfer prices is a central problem in finding the optimal level of allocation between the enterprises of the industrial group. If an enterprise produces a standard good, the theoretically correct solution would be to base the price on the market value. Otherwise, the problem of double marginalisation arises (the term of Paul Milgrom, John Roberts [1999, p. 325]), and the previous company in the supply chain will tend to charge too high a price, allowing not only to redistribute income in its favour, but also to reduce the total profit of the business structure.
Thirdly, among the significant disadvantages of complex hierarchical structures involving a large number of enterprises are organisational inertia (for more details, see [Zenger, Felin, Bigelow, 2011]), the supremacy of shareholders' interests, bureaucratic management methods, and the impact of particular social groups [Friedkin, Jia, Bullo, 2016]. However, as Victor Dementiev puts it, when the operating conditions of a number of business units are close to venture forms, it is possible to create rather viable business models, since the rigidly hierarchical structure of a group of enterprises turns into a network structure [Dementiev, 2019, p. 374].
Within the framework of the regional economy, the idea of the efficient territorial distribution of productive forces is being developed through the creation of industrial (production) complexes [Orekhova, Azarov, 2020]. An analysis of the existing territorial distribution of resources makes it possible to single out the so-called spatial-logistical allocation.
The institutional aspect of allocation implies granting the rights to utilise resources. The fundamental issue of economic theory - how prices are determined - is the question of how property rights should be established and under what conditions they should be exchanged [Alchian, 1967, pp. 2, 3]. Nobel laureates John Hicks [Hicks, 1939] and Kenneth Arrow [Arrow, 1985] proposed a general equilibrium model, which helped them come to the conclusion that it is the efficient allocation of resources (Pareto-efficient) that affects the equilibrium of market prices (as long as there is "the lack of markets for externalities" [Furubotn, Richter, 2005, p. 118]).
The issue of ownership of resources is closely intertwined with the problem of resources exchange and the performance of market agents (enterprises). The expediency of cooperation is explained by transaction costs rather than transformation ones [Orekhova, Zarutskaya, 2019, p. 558], and incentives to own assets are determined by their specificity. Relation-specific investments are interpreted as investments aimed at a specific partner and are of less value when interacting with alternative partners [Agamirova, Dzagurova, 2016, p. 123]. According to this definition, resources are categorised into non-specific, specific, and idiosyncratic (an asset of extreme specificity).
Considering the specific activities of industrial enterprises, this aspect correlates with industrial organisation theory and explains why industrial businesses seek to concentrate ownership rights in a single centre, creating a rigid form of integration. Thus, back in 1985, Joscow proved that production which required interspecific investment in assets was most likely to lead to vertical integration [Joscow, 1985]. This was also confirmed by Williamson, who believed that "unified ownership is the preponderant response to an asset specificity" [Williamson, 1996, p. 167].
Intertemporal allocation is a redistribution of resources by investors in order to minimise risk and create stable conditions for generating income, that is, associated with an investment strategy [Platko, Krass, 2012, p. 44]. For instance, when modelling the behaviour of economic entities in the natural resource market, Solow argues that at the initial stage of the market functioning it would be economically efficient to develop a low-cost deposit [Sollow, 1974]. The main argument behind that is the idea about the marginal utility growing with a decrease in the stock of a limited resource. By the time the low-cost deposit is exhausted, the marginal utility of the resource will grow to the extent that the price of the resource formed in accordance with this utility will make it profitable to develop the high-cost deposit.
To sum up, a generalised classification of resource allocation types for a group of enterprises is presented in Table 1.
Table 1. Classification of resource allocation types
Types of allocation Characteristics Economic concepts describing the given type of allocation
Factor-based Distribution of resources between the main factors of production, in practice -between labour and capital (technology) Neoclassical economic theory
Product-based Distribution of resources between the types of products manufactured Neoclassical economic theory
Intra- organisational Distribution of resources between business divisions (business processes, projects) Organisational theory. Principal-agent problem. Contract theory
Table 1 (concluded)
Types of allocation Characteristics Economic concepts describing the given type of allocation
Spatial-logistical Distribution of resources across territories (especially important when managing natural resources) Spatial economics. Location theory
Asset specificity Choice between specific and nonspecific resources Contract theory. Transaction cost theory
Institutional Allocation of resources according to property rights (more simply, owned or not by the organisation) Theory of property rights
Time-based Choice of time for resource investment Project management. Corporate finance theory
Some of the resource allocation types are driven by distribution institutions, while others - by coordination ones. Let us dwell on the distributive types of resource allocation.
Assessing resource allocation of a group of industrial enterprises:
Methodological tools
Researchers in the domain of applied economics pay special attention to the issue of resource allocation at the macroeconomic and regional levels. A number of authors highlight the serious problem of the impact of non-optimal resource allocation and incomes of the national economy on the asymmetric growth of its individual sectors (for example, [Bashmachnikova, 2013; Perepelkin, Perepelkina, 2015; Astafyeva, She-myakina, 2021]). At regional level, the main pool of works is devoted to the analysis of the territorial distributive allocation of production subsystems (for example, [Lipina, Kreydenko, 2016; Briskin et al., 2016]). There are practically no studies on resource allocation at the level of enterprises and their groups.
The general algorithm of the methodology for calculating the level of resource allocation for a group of industrial enterprises is shown in Figure 1. The basic indicators for structural analysis are divided into three groups: distribution centre, data dispersion, and structural shifts. To comprehensively analyse resource allocation of a group of industrial enterprises, these indicators should be used in combination. Distribution centre indicators establish the composition of the data, identify the intervals in which the data vary, and evaluate the characteristics of the 'centre' of the data. Given that many of them are similar in meaning, we single out the major ones, i.e., arithmetic mean, mode, median, the maximum and minimum values. Data scatter indicators allow assessing the level of data heterogeneity in the sample. The most representative indicators among them are the standard deviation and the coefficient of variation.
Fig. 1. Algorithm for assessing resource allocation in a group of industrial enterprises 2022 • Vol. 23 • No. 4 ^HUHnaiBfMeWIECMnMmm^^^^^^^^^^M
The most popular tool of structural analysis used in the Russian school of economics is indicators of structural shifts [Sukharev, 2022] that reflect qualitative change in the interrelations of subjects due to the unevenness of their resources and development. The absolute (structural shift mass) and relative (structural shift index) indicators demonstrate the greatest clarity and completeness of the results.
Based on the resource allocation types (see Table 1), this methodology involves calculating factor-, product-based and intra-organisational allocation. It is of use to assess the spatial-logistical allocation if the territorial diversity of the group's enterprises is significant (not calculated in the current study).
As mentioned above, some of the resources have the form of 'public goods'; therefore, only those types of resources are subject to calculation that can be attributed to a particular enterprise:
• production resources (fixed assets) calculated as a set of assets that reflect the technical-technological potential of a particular industrial enterprise as part of one of the following groups: equipment, production premises and facilities, tools and complex measuring and laboratory instruments, etc.;
• material resources (working capital) calculated as a set of main and auxiliary inventories, raw materials, semi-finished products, containers, etc.;
• labour resources calculated as a set of employee-related costs borne by an enterprise in the group; for a more accurate assessment of labour resources, we will use the share of labour costs in the output cost price.
Factor-based allocation is the level of distribution between the main factors of production (production, material and labour resources indicated above).
When calculating inter-organisational allocation, the level of resource distribution is found individually for each type:
• for production resources:
ItCFAi-FAf
aip = ^a-, _ (1)
where FAi is the cost of fixed assets for the i-th division, FA is the average cost of fixed assets for all divisions, n is the number of divisions; • for material resources:
AIM = -, (2)
where Hi is the cost of working capital of the i-th division, H is the average cost of working capital for all divisions, n is the number of divisions; • for labour resources:
IffPFi PF V
/M Ct c )
1 Tl
ail = ---, (3)
PF_
C
where PFz is the payroll fund of the z'-th division, Ci is the cost price of the z'-th division, n is the number of divisions.
According to the theory of statistics, the degree of data scatter is determined by the value of the coefficient of variation on an established four-interval scale: less than 10 %, from 10 to 20 %, from 20 to 33 %, more than 33 %. This scale forms the basis of the resulting coefficient of the resource portfolio of intra-organisational (and later on, product-based) allocation (Table 2). The higher the coefficient, the more uneven is the distribution of resources at the enterprises of the group.
Table 2. Interpretation of the resulting coefficients of intra-organisational and product-
based resource allocation
Coefficient value Level of allocation Interpretation
Less than 0.1 No allocation No variability of the observed values, even distribution of this type of resource across all departments
0.1-0.2 Insignificant Low variability of the observed values, almost even distribution of this type of resource
0.2-0.33 Moderate Moderate variability of the observed values, even distribution of this type of resource but the amount of resources in several divisions differs significantly
0.33-1.0 Significant High variability of the observed values, uneven distribution of this type of resource and some divisions have significantly more resources than the others
1.0-3.0 Uneven Observed values are heterogeneous, divisions vary widely in size and availability of resources
More than 3.0 Hyperallocation Observed values are extremely heterogeneous, the size of the divisions directly affects the amount of resources. Large departments have many times more resources than small ones
Product-based allocation allows evaluating the level of resources distribution between the product types of a group of industrial enterprises. In practice, to carry out such an assessment, calculations and cost estimates are used. Such calculations can be significantly distorted and do not characterise the real allocation due to management accounting policy, tax optimisation, etc. Moreover, within each enterprise, not all resources can be attributed to the production of a particular type of product, especially if the product range is extensive.
The solution to the identified problem lies in the introduction of an additional indicator p, which reflects the share of a particular resource in the total cost of the enterprise:
Rj
PX = T, (4)
where p is the indicator calculated, Ci is the cost price of the i-th type of product, Rj is the cost of resources owned by the j-th division that manufactures the i-th product, x is the type of resources (production, material or labour).
The resulting coefficients of the product-based allocation are also calculated individually for the types of resources:
I m
Z(J>i-p)
Ap = V"1 m , (5)
where m is the number of product types manufactured.
Once calculations are done, these coefficients are summarised in accordance with Table 3.
The presented methodology will make it possible to comprehensively assess the distribution of resources between enterprises, draw primary economic conclusions and make management decisions in order to equalise the efficiency of all structural units of the group.
Empirical study of the level of resource allocation: The case of the group of industrial enterprises OOO MMC-Steel
To test the proposed methodology, we used the data of OOO MMC-Steel, a local group of industrial enterprises within the holding PAO Ural Mining and Metallurgical Company (Table 3), which is one of the largest producers of ferrous and non-ferrous metals in the Russian Federation.
The Tyumen Metallurgical Plant Elektrostal of Tyumen was excluded from part of calculations as it is legally part of AMC OOO MMC-Steel.
Within the first step of the methodology, the following data were collected for all the enterprises of OOO MMC-Steel between 2014 and 2021: revenue, cost price, fixed assets, working capital, and payroll fund.
The second step of the methodology suggests determining the level of factor-based allocation of resources for the group of industrial enterprises. The results of calculating the indicators of the distribution centre and data scatter are summarised in Table 4.
Table 3. Description of the enterprises of OOO MMC-Steel
Enterprises Role of the enterprise in the group Locations Key products Comments
AMC OOO MMC-Steel Asset management company Sverdlovsk oblast, Verkhnaya Pyshma Since 2020, the main activity is making sales and managing the group
Tyumen Metallurgical Plant Elektrostal of Tyumen (Tyumen Electrosteel) Main manufacturing enterprises Tyumen Iron and steel scrap, cast billets, wire and wire rod, rolled ferrous metal products, pig iron, electrical steel ingots Part of the legal entity OOO MMC-Steel
PAO Nadezhdinski Metallurgical Plant (PAO NMP) Main manufacturing enterprises Sverdlovsk oblast, Serov Liquid argon, axle billet, technical liquid oxygen, iron vitriol, pipe billets, pig iron, granulated blast-furnace slag Lhe driving force of the group
OOO STROMOS-S Auxiliary manufacturing enterprises Sverdlovsk oblast, Serov Iron scrap, blast furnace additive, welding slag, steel slag mixture, slag rubble Insignificant production volumes
OOO Metresurs-S Raw materials enterprises Six production sites in the cities of Sverdlovsk oblast (Serov, Nevyansk, Verkhnyaya Pyshma, Ekaterinburg, Bogdanovich, Krasnoufimsk) Iron and steel scrap Supplier for PAO NMP and Tyumen Electrosteel
OOO Metresurs-P Raw materials enterprises Two production sites (the cities of Perm and Kungur) Iron and steel scrap Supplier for PAO NMP. Insignificant production volumes
AO Bogoslovsk Mining Plant Administration Raw materials enterprises Sverdlovsk oblast, Krasnotury-insk Iron ore concentrate, iron concentrate, copper cathodes, crushed stone, precious metals production (gold, silver), copper-magnetite iron ore Key supplier for PAO NMP
AO Metmash Other enterprises Sverdlovsk oblast, Serov Assets letting Beyond the production business processes of the group
Source: own compilation based on internal reporting data of OOO MMC-Steel
Table 4. Indicators of the distribution centre and data scatter of the factor-based resource allocation for the group of industrial enterprises OOO MMC-Steel, 2014-2021
Indicators 2014 2015 2016 2017
Indicators of the distribution centre
Arithmetic mean, thousand rubles 12,702,466.33 12,931,390.33 12,527,382.00 13,761,859.33
Median, thousand rubles 8,145,131.00 9,039,067.00 8,828,079.00 13,426,017.00
Standard error 7,659,174.34 7,439,828.54 6,977,983.21 6,193,991.93
Minimum, thousand rubles 2,315,753.00 2,440,088.00 2,723,145.00 3,205,415.00
Maximum, thousand rubles 27,646,515.00 27,315,016.00 26,030,922.00 24,654,146.00
Indicators of data scatter
Standard deviation 13,266,079.11 12,886,161.04 12,086,221.46 10,728,308.72
Variance 175,988,854,880,497.00 166,053,146,449,704.00 146,076,749,184,789.00 115,096,607,932,234.00
Asymmetry 1.36 1.23 1.24 0.14
Range 25,330,762.00 24,874,928.00 23,307,777.00 21,448,731.00
Coefficient of variation 1.04 0.10 0.97 0.78
Indicators 2018 2019 2020 2021
Indicators of the distribution centre
Arithmetic mean, thousand rubles 13,410,262.33 12,878,718.67 15,547,554.67 18,503,946.33
Median, thousand rubles 12,908,314.00 12,018,854.00 21,333,847.00 21,514,617.00
Standard error 5,815,944.94 5,529,813.54 6,009,646.13 7,285,867.59
Minimum, thousand rubles 3,597,108.00 3,759,725.00 3,530,996.00 4,651,405.00
Maximum, thousand rubles 23,725,365.00 22,857,577.00 21,777,821.00 29,345,817.00
Indicators of data scatter
Standard deviation 10,073,512.13 9,577,917.99 10,409,012.43 12,619,492.83
Variance 101,475,646,561,514.00 91,736,513,187,212.00 10,834,753,9870,250.00 159,251,599,403,781.00
Asymmetry 0.22 0.40 -1.73 -1.01
Range 20,128,257.00 19,097,852.00 18,246,825.00 24,694,412.00
Coefficient of variation 0.75 0.74 0.67 0.68
According to the data from Table 4, the use of resources by OOO MMC-Steel is rather heterogeneous. For instance, in 2014 production resources in value terms exceeded material ones 3.4 times and labour ones 11.9 times. However, by 2021, the cost of material resources was higher than production resources. Over the period under study, the level of factor-based allocation fell by 35 %, which may be due both to the enterprises' industry specificity (heavy industry is typically capital-intensive) and to periods of investing in production assets.
Table 5 presents the results of calculating the structural shifts in factor-based resource allocation.
Table 5. Indicators of structural shifts in the factor-based resource allocation of the group of industrial enterprises OOO MMC-Steel, 2015-2021
Resources 2015 2016 2017 2018 2019 2020 2021
Structural shift mass
Production -0.021 -0.033 -0.128 -0.136 -0.134 -0.259 -0.338
Material 0.019 0.021 0.111 0.107 0.097 0.244 0.315
Labour 0.002 0.012 0.017 0.029 0.037 0.015 0.023
Structural shift index
Production -0.029 -0.045 -0.177 -0.187 -0.185 -0.356 -0.466
Material 0.090 0.099 0.521 0.501 0.455 1.140 1.473
Labour 0.035 0.192 0.278 0.471 0.601 0.246 0.379
It is noteworthy that the structural shift mass in labour resources increased 10.8 times, while in terms of production resources it decreased 15.8 times. These trends are also confirmed by changes in the indices of structural shifts. The special emphasis is put on material resources with the structural shift index of 1.473 in 2021, which is 16.4 times higher than at the beginning of the period under consideration. This can also be explained by periods of investing in equipment and other types of production assets.
In general, ООО MMC-Steel tends to lower the level of factor-based allocation, which is confirmed by the indicators of all the three groups.
The third step in assessing the level of resource allocation of the group of industrial enterprises is to determine the type of intra-organisational allocation. To calculate the indicators, data on the fixed assets value for 2014-2021 were used (Appendix 1). The indicators of the distribution centre and data scatter are presented in Table 6.
Over the period under study, the mean value of fixed assets fluctuated slightly, from 3.1 to 3.9 billion rubles, but the median ranged between 26 and 90 million rubles. Changes in distribution are associated primarily with the shifts in the fixed assets value. At the same time, by the end of 2021, this value for small enterprises increased significantly (for example, for OOO Metresurs-P it enhanced 13.6 times). For AMC
Table 6. Indicators of the distribution centre and scatter of intra-organisational allocation data on production resources
of the group of industrial enterprises OOO MMC-Steel, 2014-2021
Indicators 2014 2015 2016 2017
Indicators of the distribution centre
Arithmetic mean, thousand rubles 3,949,502.14 3,902,145.14 3,718,703.14 3,522,020.86
Median, thousand rubles 90,701.00 61,885.00 42,133.00 26,636.00
Standard error 3,341,153.84 3,338,528.78 3,182,015.33 2,996,627.83
Minimum, thousand rubles 802.00 579.00 473.00 397.00
Maximum, thousand rubles 23,797,373.00 23,761,738.00 22,643,152.00 21,328,802.00
Indicators of data scatter
Standard deviation 8,839,862.14 8,832,916.90 8,418,821.23 7,928,332.01
Variance 78,143,162,715,229.00 78,020,420,924,707.00 70,876,550,906,419.00 62,858,448,464,435.00
Asymmetry 2.54 2.56 2.56 2.55
Range 23,796,571.00 23,761,159.00 22,642,679.00 21,328,405.00
Coefficient of variation 2.24 2.26 2.26 2.25
Indicators 2018 2019 2020 2021
Indicators of the distribution centre
Arithmetic mean, thousand rubles 3,389,337.86 3,265,368.14 3,111,117.29 3,073,516.71
Median, thousand rubles 43,380.00 61,061.00 66,626.00 77,188.00
Standard error 2,831,550.80 2,664,686.29 2,496,204.13 2,350,371.34
Minimum, thousand rubles 1,062.00 5,806.00 5,284.00 10,268.00
Maximum, thousand rubles 20,184,560.00 19,026,984.00 17,822,162.00 16,799,127.00
Indicators of data scatter
Standard deviation 7,491,579.24 7,050,097.25 6,604,335.34 6,218,498.06
Variance 56,123,759,463,711.00 49,703,871,190,117.00 43,617,245,328,945.00 38,669,718,114,333.00
Asymmetry 2.53 2.50 2.47 2.39
Range 20,183,498.00 19,021,178.00 17,816,878.00 16,788,859.00
Coefficient of variation 2.21 2.16 2.12 2.02
ООО MMC-Steel and OAO Metmash, the difference between the minimum and maximum value of fixed assets in 2021 amounted to 16.7 billion rubles, which alone indicates a high level of production resources allocation.
Thus, despite the fact that the resulting value of the coefficient of intra-organisa-tional allocation of production resources over the past 8 years has decreased by 10 %, there is a high level of heterogeneity, that is, enterprises differ significantly in the amount of resources.
Structural shifts in intra-organisational allocation of production resources of the group of industrial enterprises ООО MMC-Steel for 2015-2021 are given in Figures 2, 3.
0.06
0.04 0.02
S
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-0.04 -0.06 -0.08
u
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о <и s о сл о се о се % PQ s
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2015 И2016
2017
2018
2019
2020
2021
Fig. 2. Mass of structural shifts for assessing intra-organisational allocation of production resources of the group of industrial enterprises ООО MMC-Steel, 2015-2021
S
2.0 1.5 1.0 0.5
0.0
.ill ...
-0.5 -1.0
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о и +-» s о со о се Ён о сд Он S m О с
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■ 2015 И2016 ■ 2017 2018 ■ 2019 ■ 2020 2021
Fig. 3. Index of structural shifts for assessing intra-organisational allocation of production resources of the group of industrial enterprises ООО MMC-Steel, 2015-2021
The structural shift indicators have undergone major changes in virtually every company of the group. If OOO MMC-Steel demonstrates a decrease in both the mass and the index of structural shifts by 0.089 and 0.104, respectively, then for the other enterprises, on the contrary, these indicators are growing, i.e., the level of intra-or-ganisational allocation of production resources is in decline.
To examine the intra-organisational allocation of material resources, data on the volume of working capital for 2014-2021 were used (Appendix 1). The results of calculating the indicators of the distribution centre and scatter of intra-organisational allocation data on material resources are presented in Table 7.
The mean value is 4 times higher than the median. This indicates that material resources owned by more than half of the group's enterprises are several times lower in value than the part owned by the other enterprises. This is also evidenced by the range indicator, which has increased 2.5 times over 8 years. At that, the level of intra-organisational allocation of material resources decreases insignificantly, since the coefficient of variation for the period under study fell by 0.563.
Structural shifts in the intra-organisational allocation of material resources of the group of industrial enterprises OOO MMC-Steel for 2014-2021 are shown in Figures 4, 5.
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Table 7. Indicators of the distribution centre and scatter of intra-organisational allocation data on material resources
of the group of industrial enterprises OOO MMC-Steel, 2014-2021
Indicators 2014 2015 2016 2017
Indicators of the distribution centre
Arithmetic mean, thousand rubles 1,018,141.38 1,129,883.38 1,103,509.88 1,678,252.13
Median, thousand rubles 335,174.50 477,102.50 463,266.00 401,053.00
Standard error 1,885,191.10 1,530,803.11 1,482,883.33 2,219,700.20
Minimum, thousand rubles 3,262.00 3,780.00 4,371.00 4,904.00
Maximum, thousand rubles 5,576,553.00 4,385,917.00 4,249,958.00 5,713,256.00
Indicators of data scatter
Standard deviation 1,885,191.10 1,530,803.11 1,482,883.33 2,219,700.20
Variance 3,553,945,487,071.00 2,343,358,170,769.00 2,198,942,967,352.00 4,927,068,990,758.00
Asymmetry 2.58 1.60 1.58 1.09
Range 5,573,291.00 4,382,137.00 4,245,587.00 5,708,352.00
Coefficient of variation 1.85 1.36 1.34 1.32
Indicators 2018 2019 2020 2021
Indicators of the distribution centre
Arithmetic mean, thousand rubles 1,613,539.25 1,502,356.75 2,666,730.88 4,192,259.57
Median, thousand rubles 266,830.50 415,416.50 536,379.50 1,049,740.00
Standard error 2,302,984.53 2,208,078.76 3,772,831.54 5,400,509.03
Minimum, thousand rubles 5,363.00 5,867.00 6,496.00 40,599.00
Maximum, thousand rubles 6,342,729.00 6,344,605.00 10,166,917.00 13,970,969.00
Indicators of data scatter
Standard deviation 2,302,984.53 2,208,078.76 3,772,831.54 5,400,509.03
Variance 5,303,737,727,886.00 4,875,611,814,987.00 14,234,257,807,985.00 29,165,497,773,422.00
Asymmetry 1.47 1.82 1.38 1.15
Range 6,337,366.00 6,338,738.00 10,160,421.00 13,930,370.00
Coefficient of variation 1.43 1.47 1.42 1.29
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Fig. 5. Index of structural shifts for assessing the intra-organisational allocation of material resources of the group of industrial enterprises ООО MMC-Steel, 2015-2021
The distribution of material resources is characterised by fundamentally different trends if compared with the distribution of production resources. The share of material resources of AMC ООО MMC-Steel, the group's largest enterprise, is growing every year, as evidenced by the indicators of the mass and the index of structural shifts. This entails an increase in the level of intra-organisational allocation. To assess it in terms of labour resources on the basis of the payroll fund and the production cost price, the share of labour costs in the output cost price was calculated for each enterprise of the group ООО MMC-Steel (Appendix 2). Indicators of the distribution centre and scatter of intra-organisational allocation data on labour resources of the group of industrial enterprises ООО MMC-Steel for 2014-2021 are presented in Table 8.
Over the period under study, the mean value ranged between 13 and 23 %, but the median (actual) value was in the range of 10-13 %. The level of allocation for this type of resource is impressive as well, since, for example, the share of labour costs in the output cost price of OOO STROMOS-S is 27 times higher than that of OOO Metresurs-S. It is also worth noting that, in general, the gap between the maximum and minimum values of labour resources has more than doubled (from 11.26 in 2014 to 27.32 in 2021).
Table 8. Indicators of the distribution centre and scatter of intra-organisational allocation data on labour resources of the group of industrial enterprises OOO MMC-Steel, 2014-2021
Indicators 2014 2015 2016 2017 2018 2019 2020 2021
Indicators of the distribution centre
Arithmetic mean, thousand rubles 0.190 0.231 0.227 0.218 0.199 0.205 0.144 0.128
Median, thousand rubles 0.107 0.108 0.125 0.107 0.107 0.131 0.116 0.100
Standard error 0.060 0.078 0.074 0.075 0.066 0.065 0.051 0.053
Minimum, thousand rubles 0.038 0.041 0.038 0.026 0.022 0.025 0.022 0.014
Maximum, thousand rubles 0.425 0.553 0.550 0.536 0.435 0.431 0.364 0.377
Indicators of data scatter
Standard deviation 0.158 0.207 0.198 0.200 0.174 0.171 0.136 0.141
Variance 0.025 0.043 0.039 0.040 0.030 0.029 0.018 0.020
Asymmetry 0.533 0.653 0.711 0.661 0.353 0.308 0.846 1.160
Range 0.388 0.511 0.511 0.510 0.413 0.406 0.343 0.363
Coefficient of variation 0.834 0.899 0.869 0.915 0.872 0.834 0.950 1.102
Structural shifts in the intra-organisational allocation of labour resources of the group of industrial enterprises ООО MMC-Steel are presented in Figures 6, 7.
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Fig. 7. Index of structural shifts for assessing intra-organisational allocation of labour resources of the group of industrial enterprises ООО MMC-Steel, 2015-2021
In terms of labour resources, there is a tendency towards reducing the level of intra-organisational allocation, which is due to a change in the structure of resource allocation in favour of small enterprises. On the contrary, the mass and the index of structural shifts in AMC ООО MMC-Steel and PAO NMP somewhat decreased over the observation period. However, the indicated trend is extremely unstable.
The resulting coefficients of intra-organisational allocation are generalised in Figure 8.
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Fig. 8. Coefficients of intra-organisational allocation for ООО MMC-Steel, 2014-2021
Thus, ООО MMC-Steel is characterised by an uneven distribution of resources (primarily production and material ones) between the group's enterprises. AMC ООО MMC-Steel (including Tyumen Electrosteel), being the largest subdivision, possesses significantly more production resources than the other subdivisions.
The fourth and final step in assessing the level of resource allocation of the group of industrial enterprises is to calculate product-based allocation. The cost price of ООО MMC-Steel output by product range is presented in Appendix 3. To calculate the product-based allocation coefficients, the indicator p was computed for production, material and labour resources individually. Indicators of the distribution centre and data scatter for each type of resources are presented in Tables 9-11.
Table 9. Indicators of the distribution centre and scatter of product-based allocation data on production resources of the group of industrial enterprises OOO MMC-Steel, 2014-2021
Indicators 2014 2015 2016 2017
Indicators of the distribution centre
Arithmetic mean, thousand rubles 53,541.04 33,938.80 44,709.40 53,124.34
Median, thousand rubles 20.79 30.73 19.33 63.83
Standard error 53,176.95 33,451.91 44,219.53 52,494.48
Minimum, 0 0 0 0
thousand rubles
Maximum, thousand rubles 1,276,602.78 803,316.38 1,105,964.38 1,312,975.83
Indicators of data scatter
Standard deviation 260,512.78 163,880.24 221,097.63 262,472.36
Variance 67,866,907,586.00 26,856,732,167.00 48,884,164,473.00 68,891,747,768.00
Asymmetry 4.90 4.90 4.99 4.99
Range 1,276,602.78 803,316.38 1,105,964.38 1,312,975.83
Coefficient of variation 4.87 4.83 4.95 4.94
Indicators 2018 2019 2020 2021
Indicators of the distribution centre
Arithmetic mean, thousand rubles 14,168.50 2,161.08 2,390.08 1,285.77
Median, thousand rubles 94.11 68.83 245.72 168.79
Standard error 13,521.96 1,486.37 1,521.12 651.06
Minimum, 0 0 0 0
thousand rubles
Maximum, thousand rubles 352,164.67 38,793.57 42,385.53 18,033.14
Table 9 (concluded)
Indicators 2018 2019 2020 2021
Indicators of data scatter
Standard deviation 68,948.75 7,579.01 8,049.01 3,445.07
Variance 4,753,930,795.00 57,441,415.00 64,786,507.00 11,868,516.00
Asymmetry 5.10 4.88 4.89 4.58
Range 3,52,164.67 38,793.57 42,385.53 18,033.14
Coefficient of variation 4.87 3.51 3.37 2.68
Table 10. Indicators of the distribution centre and scatter of product-based allocation data on material resources of the group of industrial enterprises ООО MMC-Steel, 2014-2021
Indicators 2014 2015 2016 2017
Indicators of the distribution centre
Arithmetic mean, thousand rubles 2,654.83 1,351.60 1,128.14 906.97
Median, thousand rubles 115.47 98.31 72.37 129.29
Standard error 2,094.52 1,012.13 851.61 566.95
Minimum, thousand rubles 0.006 0.72 0.006 0.26
Maximum, thousand rubles 46,365.16 19,444.01 18,043.64 11,336.98
Indicators of data scatter
Standard deviation 9,824.17 4,411.76 3,902.59 2,535.50
Variance 96,514,404.00 19,463,663.00 15,230,171.00 6,428,737.00
Asymmetry 4.60 4.26 4.48 4.06
Range 46,365.15 19,443.29 18,043.63 11,336.71
Coefficient of variation 3.70 3.26 3.46 2.80
Indicators 2018 2019 2020 2021
Indicators of the distribution centre
Arithmetic mean, thousand rubles 646.01 909.68 577.32 546.04
Median, thousand rubles 132.09 92.84 48.98 109.04
Standard error 312.12 596.16 328.10 209.24
Minimum, thousand rubles 0.26 0.31 0.008 0.02
Maximum, thousand rubles 6,192.24 12,611.49 8,797.44 4,857.96
Indicators of data scatter
Standard deviation 1,430.31 2,731.97 1,704.84 1,066.93
Variance 2,045,778.00 7,463,640.00 2,906,478.00 1,138,347.00
Asymmetry 3.41 4.33 4.67 3.15
Range 6,191.98 12,611.18 8,797.43 4,857.95
Coefficient of variation 2.21 3.00 2.95 1.95
Table 11. Indicators of the distribution centre and scatter of product-based allocation data on labour resources of the group of industrial enterprises ООО MMC-Steel, 2014-2021
Indicators 2014 2015 2016 2017
Indicators of the distribution centre
Arithmetic mean, thousand rubles 5,258.38 2,997.10 2,726.85 2,552.19
Median, thousand rubles 143.65 168.03 78.51 194.76
Standard error 4,598.53 2,485.10 2,320.93 2,072.91
Minimum, thousand rubles 0.01 0.10 0.02 0.50
Maximum, thousand rubles 101,641.56 47,626.48 49,050.28 41,820.56
Indicators of data scatter
Standard deviation 21,569.04 10,836.19 10,635.83 9,270.32
Variance 465,223,495.00 117,422,919.00 113,120,954.00 85,938,889.00
Asymmetry 4.66 4.32 4.55 4.43
Range 101,641.55 47,625.48 49,050.26 41,820.05
Coefficient of variation 4.10 3.62 3.90 3.63
Indicators 2018 2019 2020 2021
Indicators of the distribution centre
Arithmetic mean, thousand rubles 1,725.23 1,140.60 1,496.08 1,765.73
Median, thousand rubles 251.30 181.00 203.20 134.80
Standard error 1,277.83 699.93 719.44 876.39
Minimum, thousand rubles 0.53 0.61 0.10 0
Maximum, thousand rubles 27,134.80 14,882.04 15,643.30 22,219.10
Indicators of data scatter
Standard deviation 5,855.75 3,207.49 3,738.32 4,637.41
Variance 34,289,822.00 102,87,969.00 13,975,067.00 21,505,608.00
Asymmetry 4.50 4.32 3.23 3.74
Range 27,134.27 14,881.42 15,643.20 22,219.99
Coefficient of variation 3.39 2.81 2.50 2.63
Indicators of structural shifts in product-based allocation were also calculated for each type of resource. The calculations of the structural shifts mass are not informative, since almost all values of the indicator are close to 0. The results of calculating the index of structural shifts are presented in Appendix 4.
A product-based hyperallocation, i.e., extremely uneven distribution of resources by types of products, was typical of the entire period from 2015 to 2021. At that, if the indicators of the distribution centre and data scatter illustrate an upward trend in the balanced use of resources, then in structural shifts, on the contrary, there are significant changes, especially in the period between 2018 and 2021. The resulting coefficients of product-based allocation for each type of resource are shown in Figure 9.
5.4
—•— Product-based allocation for production resources —•— Product-based allocation for labour resources —•— Product-based allocation for material resources
Fig. 9. Coefficients of product-based allocation for OOO MMC-Steel, 2014-2021
The results of the empirical study indicate a high level of all the types of resource allocation (Table 12).
Thus, OOO MMC-Steel is characterised by a high level of factor-, product-based and intra-organisational allocation of resources. Factor-based allocation is a hyperallocation. Resources are unevenly distributed among the individual divisions of the holding, which is confirmed by the high level of intra-organisational allocation of production and labour resources. Product-based allocation also exhibits all the signs of hyperallocation.
Table 12. Results of the empirical part of the study
Types of allocation Resource allocation Resulting level of allocation
Distribution centre Data scatter Structural shifts
Factor-based Reducing the gap between the mean and median, i.e., decreasing the level of allocation Reducing the values of indicators, i.e., reducing the level of allocation There are structural shifts in all resources, but the share of material and labour resources is growing, while that of production resources is falling High, a downward trend is observed
Intra-organisational A huge gap between the mean and median in production resources, i.e., hy-perallocation. The gap in material and labour resources is narrower but still high Heterogeneous allocation in production resources, and significant allocation in material and labour resources Structural shifts in production resources indicate a slight reduction in the level of allocation, in material resources - an increase in allocation. It is impossible to determine trends in labour resources Significant level for all types of resources. Trends towards reducing allocation in production resources and increasing it in material ones
Product-based The level of allocation is significant, the highest in labour resources Hyperallocation in all the resources, tendencies towards reducing its level Upward trend in allocation Hyperalloca-tion. No obvious trends
Conclusion
Transformation of economic systems leads to structural, strategic and behavioural changes in industrial business models and to redistribution of resources.
The reasons for the inefficient distribution of resources in large industrial groups can be both objective and subjective. Objective factors are related to slow business response to changing market signals, previous projects, strategies and commitments. In the context of turbulence, this problem is most acute.
At the same time, there are a number of subjective factors in inefficient distribution and use of resources resulting from the problems of institutional efficiency at large and the principal-agent problem, in particular. This is due to the fact that the complexity of resource management caused by their diversity, cross-department distribution, lack of motivation to create a competitive (high-quality) product at
intermediate stages due to its guaranteed purchase by the upper links of the technological chain may outweigh the potential benefits from maximum cooperation of resources.
Lack of a universal tool for assessing the level of resource distributive allocation for complex hierarchical structures has led to the need to develop an original methodology. This technique allows one to evaluate various types of resource allocation step by step, which makes it possible to further calculate the allocative resource efficiency of a group of industrial enterprises and develop an optimal resource strategy based on it.
Appendix 1. Value of fixed assets and working capital for the industrial enterprises of OOO MMC-Steel in 2014-2021, thousand rubles
Enterprises 2014 2015 2016 2017 2018 2019 2020 2021
Fixed assets value
OOO MMC-Steel 23,797,373 23,761,738 22,643,152 21,328,802 20,184,560 19,026,984 17,822,162 16,799,127
PAO NMP 3,409,204 3,161,635 3,044,961 2,998,111 3,097,291 3,254,102 3,406,904 3,934,510
OOO STROMOS-S 20,251 23,201 20,787 18,746 13,309 11,281 6,857 14,444
OOO Metresurs-S 90,701 61,885 42,133 26,636 43,380 61,061 66,626 77,188
OOO Metresurs-P 802 579 473 397 1,062 5,996 5,284 10,937
AO Bogoslovsk Mining Plant Administration 320,334 298,547 272,397 274,842 379,555 492,347 464,576 668,143
AO Metmash, Serov 7,850 7,431 7,019 6,612 6,208 5,806 5,412 10,268
Working capital value
MMC-Steel 1,059,808 1,993,640 1,863,285 4,046,897 3,136,797 2,629,746 10,166,917 13,970,969
PAO NMP 5,576,553 4,385,917 4,249,958 5,713,256 6,342,729 6,344,605 6,086,727 8,359,317
OOO STROMOS-S 11,902 37,721 42,474 52,403 58,582 62,650 69,891 56,891
OOO Metresurs-S 132,478 117,067 119,710 233,314 96,861 147,107 160,679 229,821
OOO Metresurs-P 22,082 23,921 28,992 45,978 60,398 18,444 25,458 40,599
AO Bogoslovsk Mining Plant Administration 537,871 837,138 806,822 568,792 436,800 683,726 912,080 1,049,740
AO Metmash, Serov 3,262 3,780 4,371 4,904 5,363 5,867 6,496 -
Appendix 2. The share of labour costs in the output cost price for the enterprises of OOO MMC-Steel, 2014-2021
Enterprises 2014 2015 2016 2017 2018 2019 2020 2021
OOO MMC-Steel 0.07 0.11 0.12 0.11 0.11 0.13 0.03 0.02
PAO NMP 0.11 0.10 0.11 0.11 0.10 0.11 0.12 0.10
OOO STROMOS-S 0.43 0.43 0.39 0.40 0.37 0.39 0.36 0.38
OOO Metresurs-S 0.04 0.04 0.04 0.03 0.02 0.03 0.02 0.01
OOO Metresurs-P 0.05 0.04 0.04 0.03 0.03 0.03 0.03 0.02
AO Bogoslovsk Mining Plant Administration 0.33 0.34 0.33 0.32 0.33 0.32 0.29 0.27
AO Metmash, Serov 0.30 0.55 0.55 0.54 0.44 0.43 0.15 0.10
Appendix 3. The cost price of OOO MMC-Steel output by product range, 2014-2021
Products Division 2014 2015 2016 2017
Iron and steel scrap MMC-Steel 6,205.08 - 11,061.44 -
Cast billets MMC-Steel 55,976,474.64 - - -
Wire and wire rod MMC-Steel 774,077,431.40 - - -
Rolled ferrous metal products MMC-Steel 3,391,250,225.00 1,053,281,339.00 989,929,995.60 1,361,370,249.00
Pig iron MMC-Steel - - - -
Electrical steel ingots MMC-Steel 731,274.66 - - -
Scrap metal Metresurs-S 757,417.00 854,879.00 812,846.00 1,266,448.00
Iron ore concentrate BMPA 1,367,577,087.00 1,384,085,565.00 1,377,134,140.00 1,507,490,088.00
Iron concentrate BMPA - - - -
Copper cathodes BMPA - - - -
Crushed stone BMPA 130,389,273.00 82,298,030.00 133,013,610.00 181,337,026.00
Precious metals (gold) BMPA - - - -
Precious metals (silver) BMPA - - - -
Copper-magnetite iron ore BMPA - - 4,529,200.00 45,608,700.00
Liquid argon GOST 10157-2016 NMP 8,552,000.00 7,083,820.00 3,212,436.00 1,504,240.00
Axle billet OS kr 280 NMP 932,894,076.56 267,696,492.41 723,144,110.12 1,685,455,696.53
Technical liquid oxygen GOST 6331-78 NMP 5,015,258.52 5,112,477.48 1,996,086.00 1,743,066.00
Iron vitriol GOST 6981-94 NMP 2,516,580.00 3,158,378.30 3,211,700.00 4,657,386.00
Pipe billets nik 40HN krl60 NMP 767,112,197.84 2,121,783,576.50 1,477,855,040.99 2,530,531,634.39
Pig iron NMP 520,731,784.15 768,486,836.22 744,691,226.93 1,019,191,832.50
Granulated blast-furnace slag GOST 3476-2019 NMP 25,053,233.77 35,639,198.96 39,774,842.57 51,635,701.12
Iron and steel scrap Metresurs-P 1,023,835,427.00 465,120,182.00 523,121,152.00 521,251,404.00
Iron scrap (slag heap processing contract) STROMOS-S 2,391,818.34 48,551,253.00 30,458,305.00 35,677,161.00
Blast furnace additive (slag heap processing contract) STROMOS-S 3712272.375.00 52,271,748.00 55,046,406.00 73,226,232.00
Scrap iron (slag heap processing contract) STROMOS-S 183236.13 6,903,060.00 3,073,734.00 3,738,280.00
Welding slag (slag heap processing contract) STROMOS-S - 2,557,892.00 1,747,540.00 1,460,374.00
Steel slag mixture (slag heap processing contract) STROMOS-S - - - -
Slag rubble (slag heap processing contract) STROMOS-S 45,078,673.16 397,450.00 401,724.00 510,899.00
Slag rubble resale STROMOS-S 6,478,000.00 10,650,995.00 16,603,153.00 15,567,534.00
Assets letting Metmash 3,540.00 3,087.00 1,987.00 1,806.00
Appendix 3 (concluded)
Products Division 2018 2019 2020 2021
Iron and steel scrap MMC-Steel - - 88,280.00 246,940.00
Cast billets MMC-Steel - - 3,468,555,972 6,995,997,128
Wire and wire rod MMC-Steel - - - -
Rolled ferrous metal products MMC-Steel 1,631,256,682.24 904,022,326.40 11,054,987,275.00 25,509,505,792.00
Pig iron MMC-Steel - - 286,056,661.30 795,557,795.00
Electrical steel ingots MMC-Steel - - 9,211,278.50 10,585,769.50
Scrap metal Metresurs-S 1,626,239.00 1,539,603.00 1,817,791.00 3,313,645.00
Iron ore concentrate BMPA 1,292,172,911.00 1,581,498,530.00 1,941,974,449.00 2,670,904,257.34
Iron concentrate BMPA - - - 92,788,388.75
Copper cathodes BMPA - - - 698,960,668.55
Crushed stone BMPA 162,385,014.00 306,410,742.00 141,537,988.00 136,130,880.48
Precious metals (gold) BMPA - - 387,688,000.00 816,473,013.17
Precious metals (silver) BMPA - - 22,672,000.00 46,606,780.74
Copper-magnetite iron ore BMPA 143,819,091.00 187,896,300.00 137,896,125.00 -
Liquid argon GOST 10157-2016 NMP 1,993,480.00 4,636,520.00 6,787,140.00 11,480,619.94
Axle billet OS kr 280 NMP 2,890,996,291.61 4,066,748,204.31 1,537,801,706.63 3,480,589,059.01
Technical liquid oxygen GOST 6331-78 NMP 1,638,004.00 2,000,796.00 10,924,274.60 5,442,087.69
Iron vitriol GOST 6981-94 NMP 437,088,036.18 4,350,622.00 1,867,152.18 5,238,786.09
Pipe billets nik 40HN krl60 NMP 1,419,320,259.91 2,510,971,250.01 1,713,903,496.27 1,285,817,120.87
Pig iron NMP 837,794,344.15 589,002,763.40 1,621,727,589.65 1,324,390,186.15
Granulated blast-furnace slag GOST 3476-2019 NMP 70,073,115.07 71,299,645.08 38,642,049.91 17,277,400.07
Iron and steel scrap Metresurs-P 373,998,882.00 232,606,265.00 223,965,150.00 197,228,459.00
Iron scrap (slag heap processing contract) STROMOS-S 32,519,771.00 30,026,726.00 30,237,101.00 26,615,987.00
Blast furnace additive (slag heap processing contract) STROMOS-S 63,019,170.00 56,454,259.00 54,433,756.00 46,929,619.00
Scrap iron (slag heap processing contract) STROMOS-S 3,264,570.00 3,114,849.00 3,423,124.00 2,869,980.00
Welding slag (slag heap processing contract) STROMOS-S 1,429,521.00 1,381,015.00 793,769.00 888,657.00
Steel slag mixture (slag heap processing contract) STROMOS-S 211,475.00 1,017,050.00 2,308,289.00 -
Slag rubble (slag heap processing contract) STROMOS-S 417,893.00 346,565.00 331,734.00 345,502.00
Slag rubble resale STROMOS-S 33,019,164.00 30,371,833.00 22,059,916.00 33,658,207.00
Assets letting Metmash 1,768.00 1,809.00 1,818.00 2,583.00
Appendix 4. Structural indicators of product-based allocation for OOO MMC-Steel, 2015-2021
Table 4.1. Index of structural shifts in product-based allocation for production resources
Products 2015 2016 2017 2018 2019 2020 2021
Iron and steel scrap -1.00 1.15 -1.00 -1.00 -1.00 372.98 2,166.86
Cast billets -1.00 -1.00 -1.00 -1.00 -1.00 1,627.81 6,807.19
Wire and wire rod -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00
Rolled ferrous metal products -0.51 -0.65 -0.57 0.98 6.71 84.69 408.76
Electrical steel ingots -1.00 -1.00 -1.00 -1.00 -1.00 330.11 787.55
Scrap metal 1.61 1.66 4.51 14.68 68.79 63.32 196.69
Iron ore concentrate 0.71 0.36 0.24 1.79 16.39 18.28 35.01
Crushed stone 0.07 0.38 0.57 2.67 34.34 13.73 18.25
Liquid argon GOST 10157-2016 0.41 -0.52 -0.81 -0.10 12.13 14.63 43.73
Axle billet OS kr 280 -0.51 0.00 0.99 10.91 104.56 31.47 123.32
Technical liquid oxygen GOST 6331-78 0.73 -0.49 -0.62 0.26 8.66 41.91 35.16
Iron vitriol GOST 6981-94 1.14 0.64 1.04 666.76 40.86 13.62 68.36
Pipe billets nik 40HN krl60 3.71 1.48 2.63 6.11 78.26 43.01 54.85
Pig iron 1.51 0.84 1.15 5.19 26.39 60.35 83.74
Granulated blast-furnace slag GOST 3476-2019 1.42 1.04 1.27 9.75 67.92 29.38 21.98
Iron and steel scrap -0.01 0.00 0.00 -0.04 -0.30 -0.35 -0.46
Scrap (slag heap processing contract) 26.96 13.26 14.59 71.26 519.89 733.99 598.95
Blast furnace additive (slag heap processing contract) 18.39 15.61 19.62 89.23 630.00 851.51 680.57
Scrap iron (slag heap processing contract) 50.88 17.79 20.33 93.69 704.33 1085.13 843.45
Slag rubble (slag heap processing contract) -0.99 -0.99 -0.99 -0.95 -0.68 -0.57 -0.59
Slag rubble resale 1.26 1.87 1.51 26.09 193.54 196.99 279.13
Assets letting 0.45 -0.28 -0.41 1.21 14.97 13.66 20.45
Table 4.2. Index of structural shifts in product-based allocation for material resources
Products 2015 2016 2017 2018 2019 2020 2021
Iron and steel scrap -1.00 1.50 -1.00 -1.00 -1.00 4.81 13.05
Cast billets -1.00 -1.00 -1.00 -1.00 -1.00 24.30 43.13
Wire and wire rod -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00
Rolled ferrous metal products -0.62 -0.59 -0.66 -0.28 -0.67 0.33 1.66
Electrical steel ingots -1.00 -1.00 -1.00 -1.00 -1.00 4.14 4.11
Scrap metal 1.91 1.93 2.08 11.98 4.69 6.75 10.74
Iron ore concentrate 0.48 0.66 2.38 4.14 1.83 2.28 3.66
Crushed stone -0.08 0.68 3.26 5.78 4.75 1.51 1.49
Liquid argon GOST 10157-2016 1.40 0.22 -0.44 -0.09 0.48 1.85 3.17
Axle billet OS kr 280 -0.17 1.51 4.71 11.05 10.91 4.91 10.59
Technical liquid oxygen GOST 6331-78 1.96 0.29 0.10 0.27 0.09 6.82 2.37
Iron vitriol GOST 6981-94 2.64 3.14 4.85 6.74 3.72 1.66 5.46
Pipe billets nik 40HN krl60 7.02 5.24 9.43 6.19 7.94 7.02 4.20
Pig iron 3.28 3.64 5.19 5.25 2.09 10.17 6.90
Granulated blast-furnace slag GOST 3476-2019 3.12 4.15 5.52 9.87 6.78 4.53 1.14
Iron and steel scrap -0.04 -0.04 -0.21 -0.41 -0.15 -0.26 -0.51
Scrap (slag heap processing contract) 13.61 7.81 9.97 11.21 6.41 7.43 9.84
Blast furnace additive (slag heap processing contract) 9.13 9.26 13.51 14.25 7.98 8.78 11.31
Scrap iron (slag heap processing contract) 26.11 10.61 14.01 15.01 9.04 11.46 14.25
Slag rubble (slag heap processing contract) -0.99 -0.99 -0.99 -0.99 -1.00 -1.00 -0.99
Slag rubble resale 0.18 0.77 0.77 3.58 1.77 1.27 4.06
Assets letting 0.72 0.03 0.10 0.34 -0.12 0.01 -1.00
Table 4.3. Index of structural shifts in product-based allocation for labour resources
Products 2015 2016 2017 2018 2019 2020 2021
Iron and steel scrap -1.00 2.48 -1.00 -1.00 -1.00 28.41 52.84
Cast billets -1.00 -1.00 -1.00 -1.00 -1.00 127.11 168.10
Wire and wire rod -1.00 -1.00 -1.00 -1.00 -1.00 -1.00 -1.00
Rolled ferrous metal products -0.35 -0.43 -0.24 0.06 -0.14 5.74 9.18
Electrical steel ingots -1.00 -1.00 -1.00 -1.00 -1.00 25.04 18.59
Scrap metal 0.98 0.87 1.71 3.52 5.08 3.76 5.44
Iron ore concentrate 0.87 0.76 0.94 1.36 2.83 1.75 1.53
Crushed stone 0.17 0.78 1.45 2.11 6.78 1.10 0.35
Liquid argon GOST 10157-2016 0.82 -0.15 -0.55 -0.17 1.78 1.34 1.88
Axle billet OS kr 280 -0.37 0.75 3.67 9.98 21.37 3.87 6.99
Technical liquid oxygen GOST 6331-78 1.24 -0.10 -0.10 0.16 1.05 5.43 1.33
Iron vitriol GOST 6981-94 1.75 1.89 3.78 6.14 7.87 1.19 3.46
Pipe billets nik 40HN krl60 5.07 3.36 7.52 5.55 15.80 5.60 2.59
Pig iron 2.24 2.24 4.05 4.70 4.81 8.20 4.45
Granulated blast-furnace slag GOST 3476-2019 2.12 2.59 4.32 8.91 13.61 3.56 0.48
Iron and steel scrap -0.05 -0.02 -0.07 -0.14 -0.28 -0.55 -0.73
Scrap (slag heap processing contract) 20.53 10.69 10.99 14.39 23.08 15.66 9.92
Blast furnace additive (slag heap processing contract) 13.94 12.61 14.86 18.21 28.17 18.33 11.40
Scrap iron (slag heap processing contract) 38.96 14.40 15.40 19.16 31.60 23.62 14.37
Slag rubble (slag heap processing contract) -0.99 -0.99 -0.99 -0.99 -0.99 -0.99 -0.99
Slag rubble resale 0.74 1.35 0.93 4.77 7.99 3.49 4.10
Assets letting 1.10 0.36 0.43 0.98 2.09 1.05 2.73
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Information about the authors
Svetlana V. Orekhova, Dr. Sc. (Econ.), Associate Prof., Prof. of Information Technologies and Statistics Dept. Ural State University of Economics, Ekaterinburg, Russia. E-mail: bentarask@list.ru
Ivan A. Butakov, Chief Accountant. OOO MMC-Steel, Verkhnyaya Pyshma, Sverdlovsk oblast, Russia; Postgraduate of Enterprises Economics Dept. Ural State University of Economics, Ekaterinburg, Russia. E-mail: butakov@steel.ugmk.com
© Orekhova S. V., Butakov I. A., 2022
