In 2017, from water supply - 291,2 thousand UAH., from sewerage - 317,5 thousand UAH, and in 2018 from water supply - 371,7 thousand UAH, from sewerage 464.0 thousand UAH. Financial and other incomes of ordinary activities in 2016 amounted to 8.2 thousand UAH, and in 2018 - 6,5 thousand UAH
It is interesting to study the correlation between the dynamics of current and capital assets, as well as the investigation of the property condition and the efficiency of its use (Table 2). From the Table 2 it can be seen that the rate of growth of correlation coefficient of current and capital assets amounted to 115,4% in 2017 in comparison with 2016. The rate of growth of Mobility ratio of property value was in 2017 – 108,3%, in 2018 – 92,3%.
Table 2. Correlation between current and capital assets
Indicators 2016 2017 2018 Rate of growth,%
2017/2016 2018/ 2017 1. Correlation coefficient
of current and capital assets
0,13 0,15 0,14 115,4 93,3
2. Mobility ratio of property value 0,12 0,13 0,12 108,3 92,3 3. Mobility ratio of current
assets
0,01 0,26 0,16 2600 61,5
4. Correlation coefficient of current assets and the total value of
property
0,12 0,13 0,12 108,3 92,3
Thus, the analysis showed that during the last three years the company was working unstable - in 2017 there was a loss of 146.6 thousand UAH, and already in 2018 the enterprise started to work better and received a small profit amounting to 60,8 thousand UAH.
2. Forecasting of financial results of activity of utility enterprise
Methods of expert assessments are based on the use of expert opinions or a group of such experts in the development of forecasts.
Methods of logical modeling are used mainly for qualitative description of predicted processes. There are the general principle of the development of the predicted object of management, studied in a certain logical sequence іn their basis.
Methods of economic-mathematical modeling are based on simulating the real behavior of the object of management, by constructing the corresponding economic and mathematical models. These are the most advanced methods in forecasting socio-economic processes. the method of mathematical extrapolation is widely used. It allows to continue the tendency of changing the economic parameter from the field of its observation in the past to the future. Therefore, forecasting of financial results of the enterprise is proposed precisely by using adaptive forecasting models [9].
Adaptive methods are called forecasting, which allow to build self-adjusting economic and mathematical models, which are able to react promptly for changing conditions by taking into account the result of the forecast made in the previous step, and taking into account the various information values of the levels of the series. Due to these properties of adaptive methods are particularly well used for short-term forecasting (with one or several steps forward) [5]. To adaptive methods include a lot of different techniques, but to obtain a short-term forecast according to available series, it is expedient to use methods of exponential smoothing [8, 9, 12].
Calculation of exponentially smoothing values is carried out according to the following formula [5, 9, 10, 12]:
= ∙ + (1 − ) ∙ , (1)
where – smoothed levels values;
- output levels values;
- smoothing parameter.
Modifications and generalizations of this model led to the appearance of a whole group of adaptive models with different properties [5, 9, 10, 12].
A lot of occurrence in the economy are characterized by periodic seasonal effects.
Accordingly, time series contain periodic seasonal fluctuations. These series and their fluctuations can be represented as generated by models of two main types: models with multiplicative and with additive coefficients of seasonality [5, 9, 10, 12].
The first type models have the form [5, 9, 10, 12]:
= + ; (2)
= , , (3)
where , - the dynamics of the value is characterized by the tendency of the process;
, , …, – seasonality coefficients;
l – the number of phases in the full seasonal cycle;
е – non-auto-correlation noise with zero mathematical expectation.
The second type models have the form [5, 9, 10, 12].
= + ; (4)
= , + , (5)
where , - the dynamics of the value is characterized by the tendency of the process;
, , …, – additive seasonality coefficients;
l – the number of phases in the full seasonal cycle;
е – non-auto-correlation noise with zero mathematical expectation.
For the construction of models were used the application packages of Statistica 8.0. Adaptive models are built in the Time Series Forecasting module. Statistica 8.0 makes it possible to construct adaptive models with different types of trends (linear, exponential, fading trend, and also without trend) and multiplicative or additive seasonality [5]. To find the adaptation parameters for constructing models, we used the built-in Automatic estimation function. This function automatically calculates the model with all possible combinations of parameters and gives the most adequate model. The adequacy of the model is determined by the value of the average absolute percentage error. The program gives the model with the smallest mistake, because the smaller the error, the more accurately the model describes the actual process. Approbation by adaptive models of forecasting was carried out and implemented on an example of UE "NWSSS". The most adequate model with mean absolute percentage error (m.a.p.e.) was selected for each individual diagnostic indicator of the financial state of the enterprise [5].
Exponentially smoothing adaptive models, which were used to predict the indicators, include the model: no trend, linear trend, exponential trend, fading trend. The choice of models was conducted separately for each diagnostic indicator of the financial condition of the enterprise. Thus, for determination the forecast of financial result of UE "NWSSS" is proposed to conduct forecasting of incomes and expenses of the enterprise using modern adaptive models. Quarterly figures of UE "NWSSS" incomes and expenses for the period 2016-2018 are the baseline for forecasting. They are presented in Table 3.
Table 3. Вaseline for forecasting of financial result of UE "NWSSS", thousand UAH
Period Іncome Expenses
2016 -1 Quarter 186,2 180,4
2016 -2 Quarter 185,4 173,5
2016 -3 Quarter 179,9 179,4
2016 - 4 Quarter 196,1 184,5
2017 -1 Quarter 195,4 240,1
2017 -2 Quarter 199,2 222,5
2017 -3 Quarter 190,0 235,7
2017 - 4 Quarter 197,2 233,9
2018 -1 Quarter 300,7 297,2
2018 -2 Quarter 298,2 289,9
2018 -3 Quarter 300,7 292,1
2018 - 4 Quarter 302,4 297,6
The comparison of income models forecasting of UE "NWSSS" is presented in Table 4.
Table 4. Selecting a model for forecasting the enterprise’s income Model name Type of
forecasting model
Diagram M.a.p.e.
Without trend
α=0,9, S0=1,196 6,656%
Linear trend α=0,9,
γ=0,4, Т0=0,017, S0=0,6225
22,431%
Expo-nential trend
α=0,9, γ=0,9, Т0=0,8833, S0=1,926
4,405%
Fading trend α=0,9, γ=0,9,
φ=0,8, Т0=0,035, S0=1,827
4,0578%
Exponential s moothing: S0=1,196 No trend,no s eason; Alpha= ,900
X1
0 5 10 15 20 25 30 35 40 45 50
X1 (L) Smoothed Series (L) Resids (R) 0,6
0,8 1,0 1,2 1,4 1,6 1,8 2,0
X1:
-0,3 -0,2 -0,1 0,0 0,1 0,2 0,3 0,4 0,5 0,6 0,7
Residuals
Ex p. s moothing: S0=,6225 T0=-,017 Lin.trend,no seas on; Alpha= ,900 Gamma=,400
X1 : Exp.smooth.r es ids .;
0 5 10 15 20 25 30 35 40 45 50
X1trns frmd ( L) Smoothed Series (L) Resids ( R) -0,6
-0,4 -0,2 0,0 0,2 0,4 0,6 0,8
X1:Exp.smooth.resids.;
-1,0 -0,8 -0,6 -0,4 -0,2 0,0 0,2 0,4
Residuals
Exp. s moothing: S0=1,926 T0=,8833 Expon.tr end,no s eas on; Alpha= ,900 Gamma=,900
X1
0 5 10 15 20 25 30 35 40 45 50
X1 (L) Smoothed Series (L) R esids ( R) 0,6
0,8 1,0 1,2 1,4 1,6 1,8 2,0
X1:
-0,3 -0,2 -0,1 0,0 0,1 0,2
Residuals
Exp. smoothing: S0=1,827 T0=-,035 Damped trend,no season; Alpha= ,900 Gamma=,900 Phi=,800
X1
0 5 10 15 20 25 30 35 40 45 50
X1 (L) Smoothed Series (L) Resids (R) 0,6
0,8 1,0 1,2 1,4 1,6 1,8 2,0
X1:
-0,3 -0,2 -0,1 0,0 0,1 0,2
Residuals
The forecasting model of the expenses of the UE "NWSSS" is presented in Table 5.
Table 5. Selecting a model for forecasting the enterprise’s expenses Model
name
Type of forecasting model
Diagram M.a.p.e.
Without trend
α=0,9, S0=0,283 Exponential smoothing: S0=,0283 No trend,no season; Alpha= ,900
X2
0 5 10 15 20 25 30 35 40 45 50
X2 (L) Smoothed Series (L) Resids (R) -0,01
0,00 0,01 0,02 0,03 0,04 0,05
X2:
-0,04 -0,03 -0,02 -0,01 0,00 0,01 0,02
Residuals
64,088%
Linear trend α=0,9, γ=0,9, Т0=0,0009, S0=0,0016
Exp. smoothing: S0=,0016 T0=,0009 Lin.trend,no season; Alpha= ,900 Gamma=,900
X2
0 5 10 15 20 25 30 35 40 45 50
X2 (L) Smoothed Series (L) Resids (R) -0,01
0,00 0,01 0,02 0,03 0,04 0,05 0,06 0,07 0,08
X2:
-0,015 -0,010 -0,005 0,000 0,005 0,010 0,015 0,020
Residuals
19,193%
Expo- nential
trend
α=0,9,
γ=0,9, Т0=3,251, S0=0,0012
Exp. smoothing: S0=,0012 T0=3,251 Expon.trend,no season; Alpha= ,900 Gamma=,900
X2
0 5 10 15 20 25 30 35 40 45 50
X2 (L) Smoothed Series (L) Resids (R) -0,02
0,00 0,02 0,04 0,06 0,08 0,10 0,12 0,14
X2:
-0,015 -0,010 -0,005 0,000 0,005 0,010 0,015
Residuals
15,118%
Fading trend
α=0,9, γ=0,9,
φ=0,6, Т0=0,001, S0=0,0016
Exp. smoothing: S0=,0013 T0=,0016 Damped trend,no season; Alpha= ,900 Gamma=,900 Phi=,600
X2
0 5 10 15 20 25 30 35 40 45
X2 (L) Smoothed Series (L) Resids (R) -0,01
0,00 0,01 0,02 0,03 0,04 0,05 0,06
X2:
-0,015 -0,010 -0,005 0,000 0,005 0,010 0,015
Residuals
8,958%
Comparison results have shown that an adequate result of forecasting is using a fading trend, because the model error is 8,958%. The selection of models with the specified percent of errors for UE "NWSSS" is presented in the Table 6.
Table 6. Selecting a model for forecasting the enterprise’s financial results
Financial result Model M.a.p.e.
Іncome Fading trend 4,0578%
Expenses Fading trend 8,958%
The results of the forecast are presented in Table 7 [1.5].
Table 7. The forecasting financial results of UE "NWSSS", thousand UAH
Period Income Expenses
2019 - 1 quarter 197,8 232,7
2019 - 2 quarter 199,9 245,8
2019 - 3 quarter 199,4 259,7
2019 - 4 quarter 198,8 264,9
2020 - 1 quarter 298,7 278,5
2020 - 2 quarter 300,9 299,7
2020 - 3 quarter 306,5 296,7
2020 - 4 quarter 307,8 298,5
Thus, using adaptive models were estimates of income and expenses of UE "NWSSS" (figure 1).
Fig. 1. The forecasting financial results of UE "NWSSS": income, expenses, thousand UAH Thus, in the forecast period - in the 4th quarter of 2020, the income of UE "NWSSS" will be 307,8 thousand UAH, which is on 5,4 thousand UAH more than in the 4th quarter of 2019 and expenses - 298,5 thousand UAH, which is on 0,9 thousand UAH more in comparison with the 4th quarter of 2019. It should be noted that this forecast, for 2020, does not give 100% certainty, as the activities of utility companies are influenced by a number of internal and external factors. Management of the enterprise and executive body of municipal authorities will be necessary to develop and implement a number of management measures in order to maintain the financial stability of the enterprise in the future.
0 50 100 150 200 250 300 350
2019 - 1 quarter
2019 - 2 quarter
2019 - 3 quarter
2019 - 4 quarter
2020 - 1 quarter
2020 - 2 quarter
2020 - 3 quarter
2020 - 4 quarter 197,8 199,9 199,4 198,8
298,7 300,9 306,5 307,8 232,7 245,8 259,7 264,9 278,5 299,7 296,7 298,5
Іncome Expenses
Management of utility companies depends not only on the quality of decision-making by the management of the enterprise, but also on the quality of the reform program being developed at various levels. The quantitative and qualitative results of the reform of the utility sector depend on how well the projects, strategies and programs will be developed, and how it will be organized through monitoring and control of the implementation of these decisions [16]. Program management takes place on four levels [3]: state level - the development of a nationwide sector reform program with the identifying of forecast values for macroeconomic indicators and taking into account the economic course of state development; regional level - the development of a regional program management model, analysis and monitoring of the reform in sub-sectors, justification of the projects for financing from the state and local budgets, control of the activities of housing and utility enterprises at the regional level; city level - the development of a city program and model of governance, optimal ownership and organization of housing and utilities enterprises in the city, analysis and monitoring of the enterprises development reform and its results, justification of projects for financing from the city budget, substantiation and organization of granting of a guarantee to investors; enterprise level - development of a strategic plan, control over strategy implementation, project development, organization of attracting external funds, tendering and procurement, project monitoring, preparation of documents for senior executives.
Conclusions.
Based on the analysis of the status of the water supply and sewerage enterprise, we can conclude that the industry entered the stage of economic crisis, being in a protracted depression and not having a margin of safety. Crisis, especially the price increase due to inflation, high borrowing costs and exchange rate differences, leads to an increase in operating costs and the activity will make it difficult to implement investment projects involving debt financing. These circumstances and the complexity of predicting the crisis allows to suggest the possible directions of the industry development. These trends of development are followings: technical direction - improving the quality of investment projects and justifying tariff decisions through technical standardization; staffing direction - special attention should be given to the development of required competencies in training workers in the sector; ecological direction - to improve existing and introduce innovative economically accessible environmental protection technologies in the preparation of drinking water and wastewater treatment in order to reduce the negative impact on the environment [17];
economical direction - implementation of benchmarking based management system, target indicators, monitoring of the situation in the industry; financial direction – search for investment. An important source of financial support for the transformations are interactions with budgets of various levels. State support water utilities can be provided as a direct way, by providing grant, tax concessions or loans on special terms, and indirectly - through the guarantee provision on loans, and through targeted support for the indigent.
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"Ukraine-2020" No.5/2015 (2015). Retrieved from: https://zakon.rada.gov.ua
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Sergio Smirnov
Honored coach of Ukraine,
Postgraduate student of the Department of Pedagogy East European National University
Lutsk, Ukraine
Dr.Servalio@meta.ua orcid.org/0001-9895-4117
ANALYSIS OF PROFESSIONAL PREPARATION FOR FUTURE OFFICERS OF THE STATE OF ARMED FORCES OF UKRAINE IN THE SYSTEM OF MODERN
MILITARY EDUCATION
Abstract. The main purpose of the concept of transformation of domestic military education is to achieve an effective system of training in accordance with the need for professional training and development of future military specialists in the conditions of full compatibility of national military education with the military educational environment of the Euro-Atlantic security space states. Ukrainian military education at the present stage is experiencing large and complex processes of reform. The length of these phenomena has been rather long in time due to their globalization. Reforms aimed at Euro-Atlantic integration should lead to a change in the format from the training of future reserve officers focused on career growth to the direction of the academic type of higher military education, in which the readiness of future military specialists will enable the need for professional conversion into military activities, and, vice versa.
JEL Classіfіcatіon: A30, I21.
Introduction.
The problem of improving the quality of professional training of future reserve officers of the Armed Forces of Ukraine in the system of modern military education is actualized due to the increasing number of threats that determine the need to develop safe behavior skills and professional preparedness for action in extreme situations related primarily to Russia's planned armed aggression against Of Ukraine. In the context of the Operation of the United forces, this issue is a priority and not sufficiently investigated, because it defines:
psychological and mental condition (positive attitude to the profession, motivational setting), the availability of abilities, the quality of the individual, the procedural orientation of the individual to a particular activity, volitional qualities of personality, character traits, ability to learn, ways of behavior, practical skills and abilities, intrapersonal education, development of professionally important cognitive processes, manifestations of temperaments NTU internal configuration and adaptation of the individual to successful operations in extreme situations.
In the conditions of the modern militarization of the system of international relations, the intensification of military preparations, the increase of military budgets, the reformation and rearmament of the Armed Forces and other security structures of most countries, the issue of personnel readiness for combat missions becomes of particular importance.
The relevance of the study of innovation issues in military education is due to the need to seek ways to improve the quality of training of future reserve officers, due to the exhaustion of opportunities traditional approaches and technologies that take place in the military-educational process and taking into account the experience of combat operations in the east of Ukraine, experience Leading NATO countries for conducting modern armed struggle [18, p.202].
An integral part of higher education in Ukraine is the training of officers who are involved in the training of citizens in the reserve officers training program. In a situation where the system of higher education is fundamentally reformed in Ukraine, a number of military training units of higher education institutions have been reduced, there is a need for developing innovative approaches to designing the content of reserve officers training in military departments, which becomes of paramount importance and directly concerns the national security of the country, since they activity can significantly affect the combat readiness of military units and units. Consequently, there is a need for a qualitative development, implementation, continuous improvement (in accordance with modern requirements) of certain pedagogical foundations of the above-mentioned vocational training in higher educational institutions [14, p.128].
What is relevant for our study is the scientific position that the professional activity of military specialists sets new requirements that graduates of the training department of reserve officers have never encountered in their everyday lives. The main requirement is the constant readiness to carry out their professional appointment at any time and under all conditions, including in the immediate risk to life, which in itself causes a constant psychological tension.
Among the many military experts interviewed in the eastern part of Ukraine, there is an objective opinion that the overall educational and professional level of reserve officers is much lower than the educational level of graduates of higher military educational institutions, which enables the latter to adapt more quickly to extreme conditions . The specifics of the training of contemporary student youth at the departments of military training of institutions of higher education in Ukraine is that the vast majority does not link their future professional activities with the military profession acquired as a result of additional training. Military training is not essential for students. For this reason, reserve officers who have completed military training in military departments are definitely inferior to officers who have been trained in higher military educational institutions.
1. Psychological-pedagogical problem of professional training of future reserve officers