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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.18 No.4 pp.859-871

Implementation of the Blue Ocean Strategy Using Simulation: Firm-Level Evidence from Russian Steel Market

Marina V. Vasiljeva*, Vadim V. Ponkratov*, Nikolay V. Kuznetsov, Maksim S. Maramygin, Irina V. Osinovskaya
Atlantic Science and Technology Academic Press, Boston, USA; Autonomous Non-Profit Organization “Publishing House Scientific Review” (Nauchnoe Obozrenie), Moscow, Russian Federation
Financial University under the Government of the Russian Federation, Moscow, Russian Federation
State University of Management, Moscow, Russian Federation
Ural State University of Economics, Yekaterinburg, Russian Federation
Tyumen Industrial University, Tyumen, Russian Federation
Corresponding Author, E-mail:
June 5, 2019 September 25, 2019 October 8, 2019


This paper reports a methodological approach to quantitatively assess the effectiveness of the Blue Ocean Strategy in the Russian steel market that makes the competition irrelevant. Based on the simulation method, qualitative and quantitative dependencies are determined between firm economic performance and the implementation of the Blue Ocean, Red Ocean, and Monopoly strategies. It is shown by the example of the Russian steel market that Blue Ocean Strategy enables to get a significantly higher level of profit for the enterprise as compared to the implementation of the Red Ocean Strategy, even in a monopolistic market. This is provided by creating innovative value, manifested in the minimization of social losses in the market, ensuring the possibility of establishing a higher market value while reducing production costs. The results of the paper may serve as an aid to the effective development of small and medium-sized businesses by providing a sound quantitative framework to quantify the value of innovation. This article gives an insight into how firms can create blue oceans of uncontested market space to prosper in the future, providing an opportunity to evaluate the effectiveness of the chosen strategy to minimize risks.



    The current international market is characterized by an excess of supply over demand, leading to heightened competition and price wars between manufacturers and commercial intermediaries (Zhang et al., 2018;Vasiljeva, 2017). The prevailing market situation highlights the importance of the effectiveness of traditional corporate strategies based on cost differentiation. The Blue Ocean Strategy was proposed by W. Chan Kim and R. Mauborgne in 2005 (Kim and Mauborgne, 2005a), with the primary goal of creating a new marketplace and elimination of competition. The Blue Ocean Strategy is based on a study conducted for over a decade involving more than 150 strategic moves spanning more than 30 industries over 100 years (Blue Ocean Strategy, 2019a;Randall, 2015). Traditional models have led new businesses to focus more on competition and less on innovation (Cai et al., 2017). However, consumer preferences are consistently changing for two reasons: first, new products keep emerging to distract the consumer and, second, consumers now have access to substantial amounts of information on each of these products, which allows them to be more selective (Friehe et al., 2018;Stoian et al., 2018). Hence, businesses need to shift their focus from evaluating competitors that only exist today, to developing strategies to adapt and remain innovative for the future. Therefore, under the Blue Ocean Strategy, it is argued that firms need to go beyond competing and pursuing basic improvements of products or services in overcrowded industries and instead pursue value innovation to open up new markets and render the competition irrelevant (Lohtander et al., 2017;Kim and Mauborgne, 2005b;Leavy, 2018;Parvinen et al., 2011). While understanding how to compete in the existing market space is clearly important, the Blue Ocean Strategy addresses the arguably more critical challenge of redefining industry boundaries and creating new markets under adverse structural conditions (Bourletidis, 2014). Some companies have already successfully applied this strategy, such as McDonald’s (fast food), Zara (clothing), Southwest Airlines (air travel), Cirquedu Soleil (entertainment), and Ford (automotive), among others (Kasparov, 2010). Moreover, 14% of companies that started their business in the blue ocean accounted for 38% of revenues and 61% of profits, compared to companies that started their business in red oceans as traditional corporate strategies (Kim and Mauborgne, 2005a). Several studies in different countries (Aspara et al., 2008 for Finnish companies; Tabaria et al., 2014 for Iranian companies; Kim, 2016 for the Korean case; Hashem and Joudeh, 2017 for Jordanian banks) reveal profit growth from the implementation of this strategy. As a country with a developing economy, active implementation of the Blue Ocean Strategy seems particularly relevant in the Russian market environment (Plotnikov and Volkova, 2014). The Russian economy is now among the world’s twelve largest economies (The World Bank Group, 2019). However, this large emerging market is unlike the larger and more developed economies that companies have historically looked to and counted on for growth. The world’s developed economies are based in countries with relatively high per capita incomes, while big emerging markets like Russia are the product of very low, though rising, per capita income for a very large population. This makes the importance of low-cost offerings more critical than ever. Nevertheless, low cost alone is not enough (Lee and Chou, 2018) as Russians also have increasing access to global information channels that have raised their level of sophistication and demands. To capture the imaginations and wallets of increasingly savvy customers, Russian companies must use Blue Ocean Strategies to develop offerings that are both differentiated and low-cost.

    However, at present, the practical introduction of Blue Ocean into the business development strategy is missing in Russia. This absence may be explained by economic and political instability; real-income decline, which affects the readiness of entrepreneurs to invest in innovation; along with the emergence of analogues to existing innovations (Poluyan et al., 2017;Mazur et al., 2016). Another major reason for the limited adoption of the Blue Ocean Strategy in Russia is a lack of understanding regarding its essence and its misidentification with a monopoly strategy. The absence of comprehensive methodological developments in diagnosing the effectiveness of the Blue Ocean Strategy for Russian enterprises, in developing reasonable approaches to the peculiarities encountered in its implementation in Russian enterprises, and in articulating its advantages relative to other corporate strategies necessitate the improvement of practical tools for evaluating the effectiveness of strategic planning at Russian enterprises. Thus, the introduction of the Blue Ocean strategy to the strategic management of Russian companies may become the basis for the national and industrial development of the country. We address the following issues in this study: (1) justify the distinctive features of the Blue Ocean Strategy, the Red Ocean Strategy, and the Monopoly Strategy based on the economic content of marginal costs and social losses; (2) develop correlation-regression models to establish dependencies between the performance indicators of the company’s strategies depending on market type; (3) predict promising business performance indicators, depending on the strategy being implemented.

    This study develops a methodological approach to quantitatively assess the effectiveness of the Blue Ocean Strategy in the Russian steel market. Based on the simulation method, qualitative and quantitative dependencies are determined between firm economic performance and the implementation of the Blue Ocean, Red Ocean, and Monopoly strategies. This made it possible to predict the efficiency indicators of various types of strategies implemented by the company (as exemplified by Mechel PJSC) until 2023 based on the concept of system dynamics of the Red Ocean and monopoly strategies. Such an approach allows for the practical demonstration of greater efficiency in implementing the ocean strategy compared to the traditional corporate strategies as the result of the creation of innovative value, manifested in the minimization of social losses in the market, ensuring the possibility of establishing a higher market value while reducing production costs.


    It should be noted that most recent research papers analyze the theoretical basis of the Blue Ocean Strategy and seek to improve the methodological approach for building strategic profiles within the strategy (Alam and Islam, 2017;Gündüz, 2018;Kabukin, 2014;Papazov and Mihaylova, 2016;Priilaid, 2019). The concept was first introduced by management thought leaders Kim and Mauborgne (2005a, 2005b). The idea about the market universe consisting of two sorts of oceans: red oceans and blue oceans was a fundamental concept of the theory (Lohtander et al., 2017). When businesses are setting corporate strategy, they evaluate their competitors and then do what the competitor is doing — with a few tweaks (Jajal, 2018). They enter crowded, shark-ridden red oceans. Red oceans are characterized by existing market space. In traditional economics, this is sometimes described as perfect competition: multiple firms offer the same product(s), because of which the price is set by consumers or by the market, not by each individual firm (Lindič et al., 2012). Meanwhile, blue oceans are characterized by untapped market space, demand creation, and the opportunity for highly profitable growth (Jajal, 2018). The main advantages of this strategy are: the lack of competition (Burke et al., 2009;Hashem and Joudeh, 2017;Alam and Islam, 2017), and the fact that the stronger value innovation is, the harder it is for the imitator store to create the product (Dehkordi et al., 2012); ease of use (Papazov and Mihaylova, 2016); the possibility of increasing company profits (Aspara et al., 2008;Tabaria et al., 2014;Kim, 2016;Hashem and Joudeh, 2017); the possibility of creating new demand without significant investment (Kabukin, 2014); innovation development (Pushchina, 2016;Alam and Islam, 2017); and development of creative skills (Tabaria et al., 2014). In modern, highly competitive markets, business profitability has slowed down due to the steady growth in marketing expenditures and other expenses associated with maintaining market share (Gwartney et al., 2014). Blue Ocean is not a monopoly strategy per se, but instead is a strategy for dealing with a market that is yet to be discovered by a wider audience, thus explaining its “Create. Don’t compete” slogan. It is described as a blue ocean because there is nobody there to compete and the competition ocean is, so to speak, crystal clear. To find this blue ocean, one must go beyond red (i.e., crowded) oceans (Lohtander et al., 2017). Moreover, the Blue Ocean Strategy is not based on the development of technological innovation alone (Kim, 2005). To capture a commercially compelling blue ocean, companies need a strategy that can align their values, profit, and people with the pursuit of both differentiation and low cost. When organizations fail to register the difference between value innovation and innovation per se, they often end up with an innovation that breaks new ground but is unable to reach a massive buyer base, keeping them by and large restricted to the red ocean (Bourletidis, 2014). A company can also create blue oceans with or without new technology. For example, Cirque du Soleil, JC Decaux, or Starbucks (Blue Ocean Strategy, 2019b) developed their blue ocean strategic moves without depending on new technologies. Even in blue-ocean cases that involved technology, Apple’s iPhone, Intuit’s Quicken, or, their success hinged less on technology per se than on the fact that buyers find these offerings simple, easy to use, fun, and productive (Blue Ocean Shift | Strategy, 2015). Therefore, when technology is involved, it is essential to link it to value by asking how the product or service offers a leap in terms of productivity, simplicity, ease of use, convenience, fun, and/or environmental friendliness (Bourletidis, 2014;Ivlev et al., 2016). The Blue Ocean Strategy is about being the first to get it right by linking innovation to value. It is especially advisable to implement this corporate strategy in emerging markets such as Russia, as the country has attracted many global competitors (Lee and Chou, 2018). As Russian businesses (entrepreneurs) are vying for market share, they struggle at times due to overly complex and costly offerings designed for their home markets. Local success often requires creating more relevant value at a lower cost. Due to the abstract nature of the approach and little experience implementing this strategy in Russia, it seems appropriate to apply the methods of microeconomic and mathematical analysis to justify the difference between the monopoly market and the “blue ocean” market and quantify the effectiveness of adapting the Blue Ocean Strategy. The Blue Ocean Strategy is similar to a monopoly strategy in terms of microeconomic analysis, as it enables producers to obtain higher margins and sell goods at a fixed price. Indeed, by creating new industries and taking a leading position by expanding the boundaries of the market, the company that adopts the Blue Ocean Strategy can set a higher price. The benefit to consumers is based on the concept of “innovation value.” In traditional microeconomic analysis, the interests of producers and consumers are considered antagonistic, while in the blue ocean context they are seen as convergent.

    This means that the Blue Ocean Strategy is based on value innovation that creates a unique offer for consumers and maximizes the company’s profits. However, numerous scholars have also identified some disadvantages of the Blue Ocean Strategy as opposed to the competitive market strategy: the abstract, utopian nature of the “Blue Ocean” concept (Krasin, 2009;Kraaijenbrink, 2012;Gündüz, 2018) and identification of this term with the concept of “monopoly”, that is, the situation when there is only one manufacturer in the market (Poluyan et al., 2017).

    Considering the inconsistency of scientific views regarding the advantages of the Blue Ocean Strategy, as well as the Red Ocean Strategy and the Monopoly Market Strategy, it seems necessary to justify their distinctive characteristics in the pricing system—the ratio of price and cost, and the formation of social losses.


    The main difference between the competitive, monopoly and the blue ocean markets lies in the pricing system, namely, the price-to-cost ratio and the formation of social losses. In competitive markets, the criterion for profit maximization is the equality of marginal costs and marginal revenue or price of the goods. In the case of a monopoly, it is assumed that price is an inverse function of demand. Thus, the optimal monopoly price is always higher than the competitive market price (Gwartney et al., 2014). The condition of Blue ocean strategy may be interpreted as stating that, with growing output, there is no increase in costs per each additional unit of output. Along with an increase in the output, which is not limited to any value, the growth of marginal costs becomes negative. Regarding prices, they increase if the product or benefit becomes more useful, which, in this strategy, is achieved through value innovation. A competitive market is characterized by the most efficient allocation of resources: the entire demand is satisfied with minimal costs to consumers because of there is a wide choice of goods at different prices. Therefore, deadweight loss tends to zero in competitive markets. In case of a monopoly, social losses are incurred because the cost of consuming a commodity exceeds its marginal utility due to the lack of price choices for the goods. The Blue Ocean Strategy not only reduces costs, but also increases the value of the goods to the consumer and the profit to the producer while reducing the deadweight loss of the common good by monopolizing the new industry.

    According to the law of demand, sales in the market are inversely proportional to the price and prices are inversely related to the volume supplied by the market (Gwartney et al., 2014). This model can be represented in a simplified manner as a linear relationship between price and demand. Comparing the models of the competitive and the monopoly market with the blue ocean model, it becomes evident that the differences in the dependency functions lie in the price elasticity due to the supply volumes— the value of the coefficient of the variable Q (Table 1, dependence function P = f(Q)). The least elastic price in the competitive market model corresponds to the case where the product is massive and the lack of production volumes of one company is compensated for by other companies. Price is more elastic due to the volumes of supply in the monopoly market but the company’s behavior displays the highest level of elasticity when implementing the Blue Ocean Strategy. Price (P) as a function of supply was obtained by constructing regression models based on data for 2010–2018 for the following companies: Mechel PJSC (2019) to account for company behavior in a competitive market; Severstal PJSC (2019) to account for the behavior of a monopolist company; PJSC Magnitogorsk Iron and Steel Works (2019) as an example of a company implementing the Blue Ocean Strategy.

    Labor and capital are the main factors of production. Therefore, when simulating the output value, indicators of capital investment (K) by companies and labor costs (L) are considered. The output curves are constructed using the Cobb-Douglas production function, and the elasticities of K and L are obtained by taking the logarithms of production functions and applying regression analysis using the Statistica 10.0 software package.

    Based on the production function, the total costs of the company (C) depend on capital investment (K), labor costs (L). The output rate in terms of value (R) is determined by multiplying the output in physical terms (Q) by the average price of the product (P); profit (Profit) is calculated as the difference between values (R) and costs (C).

    A feature of modeling the behavior of a company when implementing the Blue Ocean Strategy is the fact that this strategy provides for the implementation of an innovative product in the market, which, on the one hand, creates additional advantages in the form of high prices and demand for products, and on the other hand, causes additional costs associated with innovation. These features were taken into account when building the price model (P), in which for the firms implementing the Blue Ocean Strategy the inherent factor Si is the costs of innovation. The price model takes the form P = f (Q; Si). The cost model, with regard to costs of innovation, takes the form C = f (K; L; Q; Si). The assumptions that the innovation factor is the key one in building a model of behavior when implementing the Blue Ocean Strategy, which identifies this strategy among the competitive and monopoly market models, are confirmed by the theoretical framework (Alam and Islam, 2017;Aspara et al., 2008;Bourletidis, 2014;Cai et al., 2017) and by the statistical indicators of the developed models (Table 1).

    Since the indicators have different dimensions and different units of measurement are used, functional dependency models are constructed on the basis of standardized values.

    Table 1 shows the dependency functions that determine the profitability of operation in the three environments considered in the study, namely, competitive, monopoly, and while implementing the Blue Ocean Strategy. The dependence functions are built by regression analysis using the Statistica 10.0 software package.

    The adequacy of the obtained regression models is confirmed by:

    • 1) The coefficient of determination, whose value is 0.84 (competitive market model), 0.89 (monopoly market model), and 0.78 (when implementing the Blue Ocean Strategy), therefore exceeding the threshold significant level of 0.74;

    • 2) The Fisher criterion, whose values for all models exceed those for the 95% significance level;

    • 3) The Student’s t-test values for the variables in all models that exceed the 95% significance level.

    The next step in the research methodology is to develop dynamic simulation models based on the identified system of interdependencies between the factors of production, sales, and company profits (Table 1). The purpose of constructing dynamic simulation models in this study is to simulate the behavior of a company in all three market environments over time. Dynamic simulation models are implemented in the Vensim PLE 5.7 software package.

    Dynamic simulation models made it possible to model the effectiveness of the implementation of a competitive, monopoly and blue ocean strategies in the medium term (5 years) using one company as an example. Mechel PJSC financial indicators for 2018 were taken as the basis for building these dynamic models. Forecast indicators of the strategy implementation effectiveness were obtained by running simulation models taking into account the dependence functions presented in Table 1, and the values of the variable <Time> = 5. The obtained values of the performance indicators correspond to those values of the variables at which equilibrium occurs, that is, the value fluctuation amplitude tends to 0.

    4. DATA

    The object of research in this article is the Russian metallurgical market, a market with multiple opportunities to improve the company value. Only under the possibility of improving company value is the implementation of the Blue Ocean Strategy possible. Studies are constantly conducted by specialists to create lighter grades of steel, steel with increased strength and frost resistance, and other improved properties. The reporting data from metallurgical enterprises for 2010–2018, to which the competitive, monopolistic, and blue ocean strategies are applicable, were used in this study for simulation.

    Since the steel market in Russia is characterized by a high degree of competition, Mechel PJSC was chosen as an example of an enterprise operating in a competitive environment for this study (Mechel, 2019). Severstal PJSC was used to simulate company behavior in a monopoly, as the company has the highest share in the domestic steel market at around 31–35% (Severstal, 2019;Volkova, 2018;Invest in Russia, 2018).

    Magnitogorsk Iron and Steel Works PJSC was the most suitable of the metallurgical companies under consideration to characterize the Blue Ocean Strategy, as it carries out the most vigorous innovative activities in this market and has no competitors in the production of lean alloyed chrome-manganese steel. This company has been awarded a number of diplomas and medals at the international exhibitions of inventions and innovative technologies:

    • - diploma and a gold medal for a line of highstrength and wear-resistant steel grades produced under the MAGSTRONG brand at the Novokuznetsk International Mining Technology Exhibition “Russian Coal and Mining-2019” (18.07.2019);

    • - a diploma and a gold medal for the development and active use of the original MAGSTRONG trademark at the 20th Moscow International Salon of Inventions and Innovative Technologies “Archimedes” (29.05.2017);

    • - diploma and a gold medal for the development of the “Method for the production of highly wearresistant sheet steel” at the 20th Moscow International Salon of Inventions and Innovative Technologies “Archimedes” (29.05.2017);

    • - a diploma and a bronze medal for the development of the “Method for the production of ultra-highstrength sheet steel” and “Method for the production of high-strength sheet steel” at the 20th Moscow International Salon of Inventions and Innovative Technologies “Archimedes” (29.05.2017); (Magnitogorsk Iron and Steel Works, 2019).

    The awards received and the first places in the rating of production of galvanized and color coated rolled products and flat products in Russia, environmental performance ratings indicate product innovativeness, and hence - the possibility of applying the Blue Ocean Strategy.

    The values of the following indicators were calculated for these enterprises:

    • - Average price (P) for rolled steel on the basis of steel output in value and physical terms, expressed in thousand rubles/ton;

    • - Rolled steel output (Q) in physical terms, namely, million tons;

    • - Rolled steel output metal rolling in value terms (R), in million rubles;

    • - Cost (C) represents the cost of rolled steel production, expressed in million rubles, and is calculated as the product of the company’s total cost by the share of rolled steel in the structure of goods sold in value terms;

    • - Capital investment (K) and labor costs (L) were reported in financial statements as capital costs and wages and social security costs, respectively, expressed in million rubles and calculated in proportion to the rolled steel sales, similar to the cost index;

    • - Si stands for innovation spending, in million rubles.

    Based on the financial reports for these companies (Table 2), the production functions, linear regression models and nonlinear dynamic models were constructed to reveal the functions of the studied parameters. In addition, the values of the above indicators for Mechel PJSC were used to construct simulation models for evaluating the effectiveness of the Blue Ocean Strategy in the manufacture of rolled steel products. To arrive at this result, the predicted values of company’s performance under monopoly and the implementation of the Blue Ocean Strategy, the initial state of the model corresponds to the actual values of the indicators of Mechel PJSC for 2018. Predicted values are obtained as a result of simulation modeling with a planning horizon of 5 years.

    5. RESULTS

    The simulation models for Mechel PJSC were developed using the Vensim PLE 5.7 software package, based on deterministic dependencies in the correlationregression models of company behavior depending on the type of market. These simulation models became a tool for determining the most effective strategy for company operation from the point of view of profit maximization and the minimization of deadweight losses.

    Figure 1 shows a simulation model flow diagram for a competitive market situation that displays the relationships between the variables of a theoretical competition model. The figure reflects the relationship between physical output indicators of Mechel PJSC (Company 1) and a conditional competitor firm (Company 2), denoted by q1 and q2, respectively, along with total output Qk calculated as the sum of outputs of Company 1 and 2, output in monetary terms (R1 and R2), production costs (C1 and C2), production potential, determined by capital investment indicators (K1, K2) and labor costs (L1, L2) as described by the Cobb-Douglas production function and Profit (Profit1, Profit2). Under the market conditions of the Red Ocean Strategy (i.e., competitive markets), price equals the marginal cost (p = MC), and the deadweight losses (DL) tend to 0 The competitive market is characterized by the most efficient allocation of resources: the entire demand is satisfied with minimal costs to consumers because of the existing wide choice of goods at different prices. Therefore, deadweight losses in the competitive market tend to zero: DL → 0.

    Unlike the competition model, deadweight losses (DL) are inherent in the monopoly market model and in the Blue Ocean model; these losses depend on the company’s price-to- marginal cost ratio. Deadweight losses (DL) were calculated as a time integral reflecting the output dynamics (i.e., the output integral).

    Figure 2 illustrates the pricing model and the creation of profits for the monopolist company. The model is constructed considering the interdependencies between the indicators described in Table 1. In a monopoly setting, social losses are created as a result of the fact that the cost of commodity consumption exceeds its marginal utility due to the lack of choice, as mentioned in the section “Argumentation of substantive differences between the Blue Ocean Strategy and the Monopoly Market and Perfect Competition Strategies”.

    In regards to the limitations imposed on the cost and price functions due to the implementation of the Blue Ocean Strategy, a new simulation model was constructed (Figure 3), which takes into consideration value innovation and the nonlinear dynamics of creativity and human capital costs.

    The Blue Ocean Strategy is implemented in a monopoly environment; therefore, the monopoly market model (Figure 2), which provides for the availability of one company, is taken as the basis for constructing a model of the company’s behavior when implementing the Blue Ocean Strategy. Revenues of this company depend on the monopoly price set by the company, and demand - sales. The fundamental difference between the Blue Ocean Strategy and the Monopoly Strategy is that the former provides for the existence of a unique product that is created through innovativeness, which is confirmed by the publications of Alam and Islam (2017), Aspara et al. (2008), Bourletidis (2014), Cai et al. (2017). Under such conditions, the most important element of the Blue Ocean Model is the cost of innovation, which provides the fundamental difference between this model (Figure 3) and the competition and monopoly models. Costs of innovation (Si) are a factor that determines the elasticity of supply at the market price, which affects sales volumes in monetary terms, and total costs, which, in addition to the capital and labor costs stipulated by the production function, include costs of innovation.

    Based on simulation modeling, company performance indicators were predicted when implementing the three models as exemplified by Mechel PJSC (Table 3). The indicators were predicted for 2023 based on the reports from 2018. The chosen forecasting period was used to illustrate the results of the Blue Ocean Strategy implementation. Since it relies on value innovation, a certain period is required for consumers to recognize an innovative product and shape their needs before they become customers. Therefore, under these forecasting conditions the medium-term period appears to be the most acceptable. The predicted values for the business performance indicators led to the following conclusions. First, when implementing the Blue Ocean Strategy, maximum company performance is achieved, as Mechel PJSC has the highest profit level (RUB 99,642 million),which is 30.5 times the size of the profit level in a competitive setting and 2.6 times the size of the profit level when implementing the monopoly strategy. Despite the fact that the price level for products is established in the upper range relative to other strategies (36.7 thousand rubles / ton), the output, which can also be described as the volume of new demand, is 2.7 times the size as in the case of a competitive market and 1.3 times the size as in the case of monopoly. Also, the enterprise achieves a significant advantage when employing the Blue Ocean Strategy in reducing the production costs per unit, which are 1.4 rubles per ton lower than for the monopoly setting. Production cost savings are achieved by eliminating and reducing competition factors with other enterprises in the sector.

    The high marginal utility of the product and the demand for it that exceeds supply are the main factors in maximizing the company’s profits when implementing the Blue Ocean Strategy. As a result, there is an increase in the average price and the company’s revenues. The reduction in costs per unit of output as a result of an increase in sales volumes in quantitative terms is a factor contributing to an increase in profit on the part of costs.

    Finally, one of the main advantages of the Blue Ocean Strategy for Mechel PJSC is the minimum level of deadweight losses compared with other types of strategies (≤0), which indicates a high consumer value of the product in the market. That is, the utility of the supply is aligned with the price of the commodity, which can only be achieved in conditions similar to the Blue Ocean Strategy and by effectively assessing the current and future consumer needs as well as the ability of the company to create a relevant innovative product.


    This study presents the qualitative and contextual characteristics that were revealed while exploring the effectiveness of Blue Ocean Strategy in the Russian steel market. It clarifies the main difference between the monopoly market, competitive market, and the “blue ocean” market, as well as identifying several advantages of Blue Ocean strategy, both for the company and for society at large. The following issues were addressed: (1) whether there are differences between the monopoly market and the blue ocean market, (2) whether the scale risk can be eliminated by implementing the Blue Ocean Strategy, (3) whether the strategy can be effectively adopted by Russian businesses. When implementing the Blue Ocean Strategy, maximum company performance is achieved at the highest profit level which is 30.5 times the size of the profit level in a competitive setting and 2.6 times the size when implementing the monopoly strategy, 2.7 times the size as in the case of a competitive market and 1.3 times the size as in the case of monopoly.

    The importance of this research lies in its development of a methodological approach on the basis of actual data from enterprises, and implementing various business strategies in the Russian steel market, thus making it possible to predict the effectiveness of using these strategies according to the criteria of profitability of economic agents and value innovations for consumers. The practical application and universality of the findings seems to be a distinctive feature and advantage of the results obtained, as opposed to the results of Lohtander et al. (2017) and Burke et al. (2009), describing only the theoretical aspect of implementing the Blue Ocean Strategy. Enterprises of those sectors that have the potential to develop innovations can use our methodology to quantify the effectiveness of their future business strategy. That is, based on actual data, enterprises can be convinced of the acceptability and effectiveness of the Blue Ocean Strategy in a highly competitive Russian market. Simulation modelling proves that economies of scale, which may originally be seen as one of the risks of the strategy, should in fact be seen as an advantage that allows minimizing marginal costs. This can be explained by the fact that the adoption of the Blue Ocean Strategy involves expanding the boundaries of the markets, thus creating new demand during the implementation of the strategy. Thus, simulation results suggest that the introduction of a “blue ocean” strategy, in addition to profit growth for a manufacturer, as revealed elsewhere (Aspara et al., 2008;Tabaria et al., 2014;Kim, 2016;Hashem and Joudeh, 2017), allows the manufacturer to reduce costs per unit of production, increase the value of goods for the consumer, and also reduce the deadweight loss of the public good due to the monopolization of the new industry. The reduction of production costs through an increase in the consumer value of the commodity is especially relevant for the emerging Russian market.

    Additionally, the proposed methodological approach to assess the effectiveness of the Blue Ocean Strategy is based on and takes into account factors of both regional and local markets. Such an approach may serve as an addition to the methodology for improving strategic business planning presented in the literature (Chang, 2010;Lindič et al., 2012) exploring the effectiveness of the Blue Ocean Strategy in the global context. The use of this methodology can make it possible to evaluate the effectiveness of a business strategy not only for a large enterprise, but also for small and medium-sized companies with growth potential, manifested in the creativity of the approach to business operations, the correct assessment of the desires and needs of consumers, as the creation of new markets offers substantial benefits for small companies. In addition to quickly realize large profits and, ultimately, sustainable growth, the Blue Ocean Strategy allows making a jump in value both for the company and its customers.

    Coming to the market with value innovations, the company will not have to make the traditional choice between cost advantages and differentiation that enterprises usually must undertake to gain a competitive advantage. It also enables management to determine the demand of new customers, thus making competition in the existing markets irrelevant. This allows the company to operate and rapidly develop as a new market leader, regardless of its size, owing to a business model different from its competitor’s (Fedorenko et al., 2016;Novikova et al., 2016;Chernova et al., 2017).

    However, it should be emphasized that this article studies the effectiveness of implementing the Blue Ocean Strategy only within one sector of the economy. Since each sector has its own peculiarities in terms of innovation potential and consumer characteristics, the ability to create value innovation could vary significantly. These aspects of the analysis along with the algorithm for the practical implementation of the Blue Ocean Strategy have not been investigated by us. These topics will become the priority of further research on the creation of innovative values and the effectiveness of business strategies.


    Our results indicate that the Blue Ocean Strategy is not identical to the monopoly strategy as it does not involve social losses and increases the value of the goods for the consumer, unlike the monopoly strategy. At the same time, and as in the monopoly case, the Blue Ocean Strategy enables the producer to set higher prices, expand the production of goods (services), and make more profit. Nevertheless, in comparison with the competitive strategy, the Blue Ocean Strategy entails no social losses and lowers the costs per unit of production. Thus, the implementation of the Blue Ocean Strategy should be highly useful for both the company and the consumer, as well as for society overall.

    Simulation modeling based on the predicted values of company economic performance (as exemplified by business activities of Mechel PJSC) enabled us to quantitatively demonstrate that in the case in which the Blue Ocean Strategy is implemented, the enterprise profit is highest (99,642 mln rubles). In fact, the profit level in this case is 30.5 times the size of the profit level under the Red Ocean Strategy and 2.6 times the size as in the case of monopoly. This effect is the result of the creation of innovative value, manifested in the minimization of social losses in the market, ensuring the possibility of establishing a higher market value while reducing production marginal costs.

    Product innovativeness, which is assumed when implementing the Blue Ocean Strategy and is its feature in comparison to the Competitive and Monopoly Strategies, provides demand for products, even at a price higher than the monopoly one, due to the high marginal utility of the product for consumers. Even with the additional costs associated with innovation financing, the company maximizes profits by means of low marginal costs and high demand for products that often exceeds supply.

    This study shows that this kind of strategy should be of particular interest for small and medium-sized businesses since it allows for the development of new solutions based on the ones already existing in the market, and which do not require significant additional investment. Future research could include the comparative assessment of the effectiveness of the Blue Ocean Strategy for small, medium, and large businesses.



    Simulation model of company behavior in a competitive market (red ocean strategy).


    Simulation model of the behavior of a monopolist.


    Simulation model of company behavior while implementing the blue ocean strategy.


    Regression models of company behavior by type of market

    Descriptive statistics of the regression models of company behavior depending on strategy type

    Comparative results of simulating the adoption of the Blue Ocean Strategy in the production of rolled steel stock (using Mechel PJSC data) for 2023


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