CC BY
9
UDC 658.1
SEVERAL METHODS FOR EVALUATING THE INVESTMENT ATTRACTIVENESS OF SMALL INNOVATION ENTERPRISES
© 2017 IGNATOVA IU. V., DATSENKO N. V., RUDYK N. V.
UDC 658.1
Ignatova lu. V., Datsenko N. V., Rudyk N. V. Several Methods for Evaluating the Investment Attractiveness of Small Innovation Enterprises
An important factor of impact on the development and living abilities of small and medium-sized innovation enterprises, including startups, is the opportunity to evaluate their investment attractiveness. The main reason for the «failure» of such enterprises is the lack of instrumentarium to forecast the potential number of their customers, and therefore their financial results. The article suggests the number of projected customers as an indicator for evaluation of the investment attractiveness of small innovation enterprises. The authors propose to use a number of mathematical models on the basis of the instrumentarium of descriptive statistics and simulation modeling. The proposed models are built on the basis of the hypothesis of normality of the distribution law of random amounts of income clients and allow forecasting with high accuracy in relation to the day of week, and therefore evaluating the investment risks for potential investors. Keywords: small innovation enterprise, startup, descriptive statistics, density function. Fig.: 5. Tbl.: 5. Formulae: 11. Bibl.: 14.
Ignatova luliia V. - PhD (Economics), Associate Professor of the Department of Economic and Mathematical Modeling, Kyiv National Economic University named after V. Hetman (54/1 Peremohy Ave., Kyiv, 03680, Ukraine) E-mail: u1@ukr.net
Datsenko Nataliia V. - Senior Lecturer of the Department of Economic and Mathematical Modeling, Kyiv National Economic University named after V. Hetman (54/1 Peremohy Ave, Kyiv, 03680, Ukraine) E-mail: d_tashakneu@ukr.net
Rudyk Nataliia V. - PhD (Economics), Assistant of the Department of Finance, Kyiv National Economic University named after V. Hetman (54/1 Peremohy Ave, Kyiv, 03680, Ukraine) E-mail: Nataly_Rudyk@ukr.net
УДК 658.1
1гнатова Ю. В., Даценко Н. В., Рудик Н. В. Деяк методи оцнювання швестицшно)' привабпивостi малих нноеацшних тдприемств
Важливим фактором, що впливае на розвиток та життед'тльшсть малих i середнх ннова^йних тдприемств, зокрема стартапв, е мож-ливкть оцНити х швестицшну привабливкть. Основною причиною «провалу» таких тдприемств е в'дсуттсть 'шструментар'ж для про-гнозування потенцйноi^mmi сво(х тент'в, а отже, i ф'шансових результат'¡в. Як показник оцшювання швестицшно'!' привабливостi малого шновацшного тдприемства у статт'> пропонуеться розгля-дати кльксть прогнозованих тент'ю. З цею метою запропоновано використовувати ряд математичних моделей на основi iнструмен-тарю дескриптивноi статистики та iмiтацiйного моделювання. За-пропоноват модел'> побудовано на основi гпотези про нормальшсть закону розпод'шу випадковоi^mmi надходження тент'в. Вони до-зволяють зд'шснити прогноз високоi точностi залежно вiд дня тижня, а отже, i о^нити iнвестицiйнiризики для потенцшних швестор'в. Ключов'! слова: мале шновацшне тдприемство, стартап, дескриптивна статистика, функ^я щiльностiрозпод'шу. Рис.: 5. Табл.: 5. Формул: 11. Ббл.: 14.
1гнатова Юл'т Володимирiвна - кандидат економiчниx наук, доцент кафедри економко-математичного моделювання, Кивський нацо-нальний економiчний утверситет iм. В. Гетьмана (пр. Перемоги, 54/1, Кшв, 03680, Украна) E-mail: u1@ukr.net
Даценко Наталiя Володимирiвна - старший викладач кафедри еко-ном'шо-математичного моделювання, Кивський нацональний еконо-мiчний утверситет iм. В. Гетьмана (пр. Перемоги, 54/1, Кшв, 03680, Украна)
E-mail: d_tashakneu@ukr.net
Рудик Наталiя Васитвна - кандидат економ'мних наук, асистент кафедри фiнансiв, Ки/вський нацональний економiчний утверситет iм. В. Гетьмана (пр. Перемоги, 54/1, Шв, 03680, Украна) E-mail: Nataly_Rudyk@ukr.net
УДК 658.1
Игнатова Ю. В., Даценко Н. В., Рудик Н. В. Некоторые методы оценивания инвестиционной привлекательности малых инновационных предприятий
Важным фактором, влияющим на развитие и жизнедеятельность малых и средних инновационных предприятий, в том числе стартапов, является возможность оценить их инвестиционную привлекательность. Основной причиной «провала» таких предприятий является отсутствие инструментария для прогнозирования потенциального количества своих клиентов, а значит, и финансовых результатов. В качестве показателя оценивания инвестиционной привлекательности малого инновационного предприятия в статье предлагается рассматривать количество прогнозируемых клиентов. С этой целью предложено использовать ряд математических моделей на основе инструментария дескриптивной статистики и имитационного моделирования. Предложенные модели построены на основании гипотезы о нормальности закона распределения случайного количества поступления клиентов и позволяют осуществить прогноз высокой точности в зависимости от дня недели, а значит, и оценить инвестиционные риски для потенциальных инвесторов. Ключевые слова: малое инновационное предприятие, стартап, дескриптивная статистика, функция плотности распределения. Рис.: 5. Табл.: 5. Формул: 11. Библ.: 14.
Игнатова Юлия Владимировна - кандидат экономических наук, доцент кафедры экономико-математического моделирования, Киевский национальный экономический университет им. В. Гетьмана (пр. Победы, 54/1, Киев, 03680, Украина) E-mail: u1@ukr.net
Даценко Наталия Владимировна - старший преподаватель кафедры экономико-математического моделирования, Киевский национальный экономический университет им. В. Гетьмана (пр. Победы, 54/1, Киев, 03680, Украина) E-mail: d_tashakneu@ukr.net
Рудик Наталия Васильевна - кандидат экономических наук, ассистент кафедры финансов, Киевский национальный экономический университет им. В. Гетьмана (пр. Победы, 54/1, Киев, 03680, Украина) E-mail: Nataly_Rudyk@ukr.net
Б1ЗНЕС1НФОРМ № 4 '2017
www.business-inform.net
Nowadays the issue of the actual state and prospects of start-up technologies occupies a prominent place in economic research in the world and in Ukraine, in particular. Recently the economic situation in Ukraine has imposed some corrections to the prospects of introducing start-up technologies, which leads to the reduction of investment assets and some other problems. However, the realities of 2015-2016 showed that the potential of Ukrainian and international start-ups is sufficient to resist the crisis.
After analyzing a number of literary sources [1; 3; 5; 7; 9; 11-14], we have formulated a simple and clear definition of a start-up. Thus, in our opinion, the start-up can be defined both as an individual innovative project and a company (small or medium) founded for its implementation. The development of the practice of start-ups in Ukraine is at the initial stage and the key performance indicators of such activities are low. In today's world the innovative activity and, as its consequence, innovative projects are not massive. Every new business needs a unique idea and technology to produce a product or service that would not have analogs in the competitive market. Moreover, this project does not have to be expensive and, most importantly, should be popular among consumers. Thus, the current market creates a request for the emergence and development of start-ups in the original, correct sense of understanding, that is, such projects, the idea of which would be unique and have no analogs, and at the same time it would not be as expensive as innovative developments and would not require huge human, energy and financial resources. Therefore, great hopes for improving the economic situation are laid on the development of innovative small businesses.
The main directions of start-ups development in recent years are:
+ hardware - start-up projects in the field of technologies: 3D-printing, development of drones and various devices, etc.; + financial services - start-ups, which enable clients to save time and money when carrying out financial transactions; + medicine - design of devices to monitor the state of health, efficiency of disease treatment methods, etc. For example, applications, which monitor biological parameters of the human body functioning, or a platform for choosing a doctor and receiving advice and treatment in any country of the world. + education - one of the fast developing areas of start-ups. Today platforms, which help remotely or internally examine any scientific field, from IT to foreign languages, are actively created. + Internet of Things (IoT). Over the last year, the most promising in this regard was the home security, for example, Ajax Systems - "cozy house" or Solar-Gaps — smart blinds, which can accumulate solar energy;
+ Big Data - data management and analysis in order to use this information for business development and enterprises growth.
Problem statement. Regardless of the chosen direction, any start-up faces the problem of finding initial financ-
ing. This problem is related to the start-up's potential measurement and forecasting of the financial results.
At the planning stage of a new project the founder is interested in how much labor the project will require, how long it will take to implement it and, which is more important, how much money is needed for this. Incorrect calculation of the project budget can not only lose some revenue but incur significant losses. A preliminary assessment of the project can help to avoid such failures and make a decision on whether to accept or reject the project.
It is also important to identify the risks because a potential investor needs to know what to invest and what it may cost him. There are different methods that help to determine the project risks. They all have their strengths and weaknesses. To select a particular method of assessment of the financial potential and risks of an IT start-up, the founder should be fully aware of all the existing methods.
The availability of some statistical information about some enterprise makes it possible to use this information to predict its profitability of and potential for growth in the market. However, the essential feature of assessing start-ups, first of all, is the lack of financial history, i.e., the absence of any statistics at all. In the latter case, according to [9], it is appropriate to use the method of analogies, which implies the application of "the base of data and knowledge on similar projects that were implemented before" [9, p. 32].
The aim of the research is to develop methods for assessment of the investment attractiveness of small innovative enterprises based on the available statistical information.
According to [4; 11; 12], statistics and mathematics should be instruments helping the investor to ensure a financial payback and return on the projects. For a long time, to analyze investments, in particular in terms of where, when and how to invest, specialists have been using Markowitz portfolio theory [9, p. 150]. However, in our opinion, this theory is ineffective when investing in start-ups because the return on the venture capital is not distributed evenly as it is provided under Markowitz theory.
It is a difficult task to perform a numerical analysis of small and medium innovative enterprises at the initial stage. The assessment of return on capital for start-up differs from traditional methods of liquid assets evaluation.
The careful study of changes in return on investment, as noted by some analysts, including [12], indicates that income of start-ups is likely subject to normal or lognormal distribution law, i.e., the logarithm of random income variable has a normal distribution. The list of typical values considered as lognormal ones could include the following: duration of illness, duration of marriage, income distribution of companies (branches, countries), amount of capital invested in the start-up, amount of reserve funds that will be required for future investments, etc.
The economists Elton and Gruber have proved that Markowitz portfolio theory is also associated with lognormal distribution. If we assume that the return on capital falls under the normal distribution, but in practice it is on the contrary, all previous calculations will have no sense, and the evaluation of return on investment will be irrelevant.
BI3HECIHQOPM № 4 '2017
www.business-inform.net
In its turn, article [10] proposes to use the normal distribution to simulate the number of the start-up clients. However, this approach has its drawbacks, namely, how many clients the start-up attracts also depends on certain factors, which were not taken into account in the model.
Thus, there is the unsolved issue of building a model for assessment of the investment attractiveness of small innovative enterprise that would take into account all previous shortcomings being easy enough to use.
Since the absence of statistics data is the main feature of a vast majority of small innovative enterprises, in order to develop the method of assessing the level of startup investment attractiveness we propose to use the database of an enterprise working in the same sphere (the method of analogies). For instance, let's consider the potential growth of start-ups promoting content on social networks. As an analog of such enterprise let's take a closer look at the well-known start-up Buffer [2].
Among the famous start-ups, Buffer takes a special place as one of the first start-ups providing full statistical information on its activities. Therefore, the development of the method of assessing the potential of this start-up will allow using it for measuring the potential of enterprises working in a similar field but without any statistical records or databases.
In particular, similarly to [10; 12-14], we will consider the number of new clients attracted to using the Buffer ser-
vice as an indicator of the start-up investment attractiveness. In order to forecast the number of new service clients, we have analyzed in detail the historical data constituting the general totality in a time series for the whole period of business activity (January 2012 - October 2016). The last six months (04.04.2016 - 10.30.2016) reflect the latest trend in the number of clients of the start-up. According to this, we can construct a mathematical model of the potential number of clients. Let's analyze the statistical data using the descriptive statistics instruments. The proposed Fig. 1 shows the visualization of time series selected from the representative sample.
As only the number of clients as of the specific date is known, we have an opportunity to visualize the number of clients depending on the day of the week (Fig. 2).
Based on Fig. 2 we can conclude that the number of clients attracted to Buffer at weekends rather differs from that on weekdays. Thus, it is necessary to investigate whether the difference between these two categories is significant before constructing the mathematical model of forecasting the potential number of Buffer's clients.
We divided the selected data into two stratums as follows:
+ weekday stratum - the number of new clients attracted to the start-up on weekdays (from Monday till Friday inclusive); + weekend stratum - the number of new clients attracted to the start-up at weekends.
250 H 20015010050
0
' — S T3 <u c c
= -o <U s S
= -o u c S
-o u C C
H £ H
H £ H
H £ H
H £ H
H £ H
Fig. 1. The total number of clients attracted to Buffer for the period of January 2012 - October 2016 Source: developed by the authors based on [2].
250 200 150 100 50
0
1 3 5 7 9 11 1315171921 23 25 272931 33353739414345474951 5355 57 5961 6365676971 —•— Weekday -----Weekend
Fig. 2. The number of clients attracted to Buffer on weekdays and at weekends for the period of January 2012 - October 2016 Source: developed by the authors based on [2].
<C
CO
CL 1=
LQ O CL
< £
o
u
< m
CO Q_
LQ O Q_
<
S
u
Let's analyze these stratums with regard to the selected sample based on the instruments of the descriptive statistics [6; 8]. In particular, as the main statistical indicators for the analysis carried out by means of stratified sampling we will choose the following ones: a) Mean - the average of all values:
E x
i=1
(1)
income of the
where n - is the number of observations; xi small innovative enterprise; i = 1, 2, 3, ..., n;
b) Median (Me) - the middle observation when values of the variable of income x i are sorted from the smallest to largest ones;
c) The standard deviation (StDev, or a) - the square root of the average of the squared deviations from the mean
o =
E (x -x )2 i=1
(2)
d) The sampling error (SE (x)) - the difference between the point estimate (mean) and the true value of the population parameter being estimated
SE (x) = -j=. -v/n
(3)
Tbl. 1 presents the analysis of the sample and each stratum on the above mentioned indicators.
Based on the results represented in Table 1, it is clear that statistical indicators for each stratum somewhat differ. It is necessary to determine whether this difference is signif-
icant. If the difference is significant we need to construct a mathematical model of forecasting the number of the startup's clients for each stratum. Conversely, if the difference is not significant the model of the forecasting could be constructed for the whole sample.
The confidence intervals for the mean values for each stratum should be constructed in order to analyze these differences. The tests of statistical hypotheses should be also conducted.
We construct confidence intervals for the mean values for each stratum using the following parameters:
a) degrees of freedom, df = n - 1;
b) confidence level;
c) t-value - theoretical value of the Student's test for the selected values of confidence and number of degrees' freedom, which can be found in standard normal distribution tables;
d) Lower limit - lower limit of confidence interval
x — SE (x) • t — value; (4)
e) Upper limit - upper limit of confidence interval
x + SE(x) • t — value. (5)
Based on the aforementioned parameters we obtain the following results presented in Tbl. 2.
Table 2 demonstrates that the average number of clients on weekdays does not get in the confidence interval for the number of clients at weekends and vice versa. So, the question is whether there is a significant difference between the mean values of stratums.
Let's define the difference between the mean values of stratums and range of the confidence interval of the difference. The following parameters of descriptive statistics will be used:
Table 1
Numerical characteristics for new clients attracted to Buffer in general and by stratum
Stratums Mean Median StDev n SE (X)
Sample 114.624 119 37.068 103 3.652
Weekday stratum 138.394 139 25.773 71 3.059
Weekend stratum 68.094 65.5 14.759 32 2.609
Source: calculated by the authors based on [2] using Formulae (1) - (3).
Table 2
Confidence intervals for the mean values for Weekday and Weekend stratums
Indicators Values for Weekday stratum Values for Weekend stratum
Mean 138.394 68.094
Median 139 65.5
StDev 25.773 14.759
n 71 32
SE (x) 3.059 2.609
df 70 31
Confidence level 0.95 0.95
t-value 1.994 2.040
Lower limit 132.294 62.772
Upper limit 144.495 73.415
Source: calculated by the authors based on [2] using Formulae (1) - (5).
n
x
n
n
fl
a) Mean difference, or x^ - the difference between the mean values of the stratums
xdiff — x x~>
x1 — x2; (6) b) StDev, or adiff- the standard deviation of difference of the mean values between the stratums
'diff
(«-1) ■al +(«2-1)-02 «1 + n2 — 2
(7)
where n1 - is the number of observations in the first stratum; n2 - is the number of observations in the second stratum; ff1 - standard deviation of the first stratum; a2 - standard deviation of the second stratum;
c) df - the number of degrees of freedom for the difference of the mean values, n1 + n2 - 2;
d) SE (x) - the standard error for the difference of the mean values
SE(X)-adiff -J- + —
1 1
(8)
lcsl SE(X) ' g) the number of stratums i = 1, 2 . Based on the aforementioned parameters we obtain the following numerical values presented in Tbl. 3.
Table 3
Estimated indicators of differences of the mean values of Weekday and Weekend stratums
Indicators Value
Mean 70.301
StDev 22.962
SE (X) 4.889
df 101
Confidence level 0.95
t-value 1.984
Lower limit 60.6024
Upper limit 79.999
Source: calculated by the authors based on [2] using Formulae (6) - (9).
Tbl. 3 shows that the confidence interval for the mean difference is rather broad. It is necessary to verify the statistical hypotheses on the significance of this difference. So, let us put forward the null and alternative hypotheses about significance of the mean difference.
The implementation of the null hypothesis (H0) will
indicate that x1 — x2 — 0. It means that this difference is
not significant and the whole sample can be used for forecasting.
The implementation of the alternative hypothesis (Ha) will indicate that x1 — x2 > 0. In this case, the forecast should be built separately for weekdays and weekends.
The results of statistical hypotheses testing are presented in Tbl. 4.
Table 4
Results of the statistical hypotheses testing for the mean difference
Indicators Value
H0 x1 — x2 — 0
Ha x — x2 > 0
Mean difference 70.301
SE (X) 4.889
df 101
t-test 14.379
Ratio of sample var 3.050
p-value 0.002
e) t-value - the Student's test critical value for the selected level of confidence and the number of degrees of freedom;
f) t-test - the actual value of the Student's t-test
(9)
Source: calculated by the authors based on [2] using Formulae (6) - (9).
Since p-value - the probability of accepting H0, - is significantly less than 5 %, the alternative hypothesis of the significance of mean differences is taken into account. It means that the forecast of the number of new clients should be made separately for each stratum.
Thus, based on the statistical data of the Buffer startup, let's construct a mathematical model of the forecast number of new clients. For this purpose, we use the methodology [10] taking into account its drawbacks and make a forecast separately for the clients attracted on weekdays and at weekend.
In accordance with the law of large numbers, if we take a set of parameters from any sample and put them together, then the distribution of these amounts has the normal distribution. The more summands, the closer its distribution to normal. Based on the law of large numbers we can hypothesize that the number of clients attracted to the start-up is normal.
To adapt the random number of clients attracted to the normal distribution low with average value of x and standard deviation a, we will use the Monte-Carlo imitation method. At that, we take into account pessimistic and optimistic scenarios:
+ pessimistic scenario - the forecast number of clients attracted on the following day may be less than the number of clients on the previous day. In its turn, the standard error will vary for the average number of clients in the previous period to the average number of clients according to the statistical data. Also, the forecast number of clients may be less than the minimum number of the start-up's users in the representative sample; + optimistic scenario - the forecast number of clients that will be attracted on the following day cannot be less than the number of clients on the previous day. As well as in the pessimistic scenario, the standard deviation, in turn, will vary by the share of the average number of clients in the previous period
with respect to the average number of clients in historical dataset. Also the forecast number of clients can't be less than the minimum number of users of the start-up in the representative sample.
Thus, let's make a forecast for the number of the service users for the following month. With this purpose we will use the simulation modeling tool based on the normal law of probability distribution
(x—x)
f (x) = - 1
2
(10)
with the average value x and standard deviation a for each stratum.
Let the probability of the number of clients per 1 day may be distributed randomly in the range from 0 to 1. For the modeling of the client attraction on weekdays we will make 150 experiments of occurrence of the probability as presented in Tbl. 5.
Table 5
Forecast flow of attraction of new clients on weekdays based on simulation modeling
Experiment # Probability of attraction of new clients Actual number of new attracted clients
1 0.962045 167
2 0.660521 134
3 0.551235 142.4
4 0.157268 75.33333
149 0.861734 158.7
150 0.301862 108.7
Source: calculated by the authors.
According to Tbl. 5, the number of new attracted clients meets the probability distribution function calculated by Formula (10) with the mean value and standard error of clients attracted on weekdays. Thus, for example, having substituted 0.962 probability in Formula (10) with x = 138.934, a = 25.773, we get that the start-up can attract x = 167 users on a weekday with the probability 0.962. Other experiments were conducted similarly.
Base on the results of the simulation modeling of the number of new clients attracted on weekdays, the number of new clients on the first forecast day will be 167 or 134, or 142, etc. Thus, the average number of clients per day will be: 150
2 x
x = 750 = 124. (11)
In this case the standard deviation is a = 37 clients.
The results of the simulation modeling of the number of new clients attracted under both scenarios and historical data for weekdays are shown in Fig. 3.
Similarly to the abovementioned approach, the simulation model of forecasting the number of Buffer's clients at weekends was build. The results of forecasting new users under both scenarios and historical data for the weekends are shown in Fig. 4.
The forecast of new clients for each day of the week based on the combined results of previous stages is presented in Fig. 5.
CONCLUSIONS
Nowadays the problems and tasks in the field of innovative entrepreneurship are particularly relevant. This development enables innovative businesses to achieve the strategic vector associated with overcoming the raw material dependence of the domestic economy and developing new technological order.
An important factor, which influences the business activity and development of small and medium innovative enterprises, including start-ups, is a possibility to determine the number of its potential clients. The main cause of "failure" of such enterprises is the lack of tools for forecasting the potential number of its clients and, thus, the financial results. For this purpose, it is suggested to use a series of mathematical models based on toolkits of descriptive statistics and simulation modeling. The proposed model is based on the hypothesis of normal distribution of a random number of clients and allows making a high accuracy forecast depending on the day of the week and, thus, estimating the investment risks for potential investors. As the prospects for further research, in our opinion, it is reasonable to conduct a study of time series of new clients and systemize factors, which can influence it, particularly tools of marketing strategy, quality of the website, number of visitors, etc. ■
123456789 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 -----Historical data -Optimistic scenario - -Pessimistic scenario
Fig. 3. The forecast number of the start-up's clients for 30 periods under the simulation model for the first stratum Source: developed by the authors based on Formulae (10) - (11).
Optimistic scenario--Pessimistic scenario -----Historical data
Fig. 4. The forecast number of the start-up's clients for 30 periods under the simulation model for the second stratum Source: developed by the authors based on Formulae (10) - (11).
Optimistic scenario--Pessimistic scenario -----Historical data
Fig. 5. The forecast number of the start-up's clients for 28 periods under the simulation model for the whole sample Source: developed by the authors based on Formulae (10) - (11).
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УДК 338.432:620.952.(477)
СТРАТЕПНН1 ПРИНЦИПИ СТАНОВЛЕНИЯ ТА РОЗВИТКУ Б1ОПАЛИВНО1 1НДУСТРН В УКРА1Н1
© 2017
КЛИМЧУК О. В.
УДК 338.432:620.952.(477)
Климчук О. В. Стратепчш принципи становлення та розвитку бюпаливноТ шдустрм в УкраТш
Мета cmammi полягае у висвтленн'! стратег1чних принцитв становлення та розвитку б'юпаливного виробництва в Укранi на конкурентоспро-можному рiвнi. Проведений всеб'мний анал'в наукових праць вказуе на актуальтсть нарощування темтв розвитку бюпаливноi iндустри як у свтi, так i в УкраЫ. Проте низький р'юень споживання та виробництва бюпалива в нашш державi потребуе проведення подальших дотджень стра-mегiчного характеру. Встановлено, що формування конкурентоспроможного виробництва бюпалива в УкраЫ буде забезпечувати позитивт зрушення в економ'жо-енергетичному та агропромислово-екологiчному напрямках. На основi проведеного SWOT-аналiзу було оцмено внутршнi сили та систему внутр'>шн'1х недолтв, а також ресурсний потен^ал агропромислового комплексу для реал'ваци наявнихзовшшн'!хможливостей та протистояння рзного роду загрозам, що виникають в ринкових умовах у процес розвитку та становлення бюпаливноi шдустри. Ключов'! слова: економка, регулювання, енергоспоживання, енергетична залежшсть, бюпаливо, стратег'т, SWOT-аналiз. Рис.: 1. Табл.: 1. Ббл.: 8.
Климчук Олександр Васильович - кандидат сльськогосподарських наук, доцент, доцент кафедри адмiнiсmраmивного менеджменту та альтер-нативних джерел енергИ, Внницький нацональний аграрний утверситет (вул. Сонячна, 3, Внниця, 21008, Украна) E-mail: klimchuk-77@mail.ru
УДК 338.432:620.952.(477) Климчук А. В. Стратегические принципы становления и развития биотопливной индустрии в Украине
Цель статьи заключается в освещении стратегических принципов становления и развития биотопливного производства в Украине на конкурентоспособном уровне. Проведенный всесторонний анализ научных работ указывает на актуальность наращивания темпов развития биотопливной индустрии как в мире, так и в Украине. Однако низкий уровень потребления и производства биотоплива в нашей стране требует проведения дальнейших исследований стратегического характера. Установлено, что формирование конкурентоспособного производства биотоплива в Украине будет обеспечивать положительные сдвиги в экономическом, энергетическом, агропромышленном и экологическом направлениях. На основе проведенного SWOT-анализа были оценены внутренние силы и система внутренних недостатков, а также ресурсный потенциал агропромышленного комплекса для реализации имеющихся внешних возможностей и противостояния различного рода угрозам, возникающим в рыночных условиях в процессе развития и становления биотопливной индустрии. Ключевые слова: экономика, регулирование, энергопотребление, энергетическая зависимость, биотопливо, стратегия, SWOT-анализ. Рис.: 1. Табл.: 1. Библ.: 8.
Климчук Александр Васильевич - кандидат сельскохозяйственных наук, доцент, доцент кафедры административного менеджмента и альтернативных источников энергии, Винницкий национальный аграрный университет (ул. Солнечная, 3, Винница, 21008, Украина) E-mail: klimchuk-77@mail.ru
UDC 338.432:620.952.(477) Klymchuk O. V. The Strategic Principles of Formation and Development of the Biofuel Industry in Ukraine
The article is aimed at highlighting the strategic principles of formation and development of the biofuel production in Ukraine at a competitive level. The carried out comprehensive analysis of scientific publications indicates the relevance of the pace of development in the biofuel industry as in the world, so in Ukraine. However, the low level of consumption and production of biofuels in our country requires further research of strategic nature. It has been found that formation of the competitive production of biofuels in Ukraine would ensure the positive developments in the economic, energy, agro-industrial, and environmental directions. On the basis of the carried out SWOT-analysis, the author has evaluated internal forces and the system of internal shortages, as well as the resource potential of the agro-industrial complex towards the implementation of existing external opportunities and confronting various threats, emerging in the market conditions during the process of development and rise of the biofuel industry.
Keywords: economy, regulation, energy consumption, energy dependence, biofuel, strategy, SWOT-analysis. Fig.: 1. Tbl.: 1. Bibl.: 8.
Klymchuk Oleksandr V. - PhD (Agriculture), Associate Professor, Associate Professor of the Department of the Administrative Management and Alternative Energy Sources, Vinnitsa National Agrarian University (3 Soniachna Str., Vinnytsia, 21008, Ukraine) E-mail: klimchuk-77@mail.ru
Ефективне виршення проблеми енергозабезпе-чення е ключовим, першочерговим завданням сталого, тобто гармоншного з природою та су-спкьством, розвитку кожно! держави, здшснення нею незалежно! зовншньо! политики, внутршньо! пол^ич-но! та сощально! стабкьносп, шднесення економiчно-го та культурного рiвня життя населення. Як наслцок -
роль енергетики в розвязанш завдань сталого розвитку постшно зростае. Неухильне шдвищення останшм часом свиових щн на традицшш енергонош та енергш призводить до посилення впливу проблеми енергоза-безпечення на перелiченi фактори, особливо в державах iз нестабкьним економiчним становищем, до яких належить i Укра!на.
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