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24системе ЖКХ поможет самоорганизовать, стимулировать работу системы и решить вопросы рационального использования ресурсов данной сферы деятельности и разумно вкладывать инвестиции в производственный процесс.
Ещё одно из проявлений самоорганизации ЖКХ и строительства это организация так называемой Оперативной службы ЖКХ (или ОС ЖКХ) наподобие «Скорой помощи» в сфере здравоохранения. В случае возникновения неполадок в работе санитарно-технических приборов, запорно-регули-рующей арматуры или прорыва системы отопления или водоснабжения, на месте прибывают работники оперативной службы ЖКХ после экстренного звонка с места происшествия. Работники Оперативной службы ЖКХ гипотетически могли бы иметь свою спецодежду, специализированный транспорт с соответствующими опознавательными знаками и иметь при себе кейсы с рабочими инструментами и принадлежностями. Оперативная служба ЖКХ могла бы входить в состав МКЦИР, который в свою очередь мог бы находиться в подчинении Министерства строительства и ЖКХ. ОС ЖКХ могла бы проводить дежурство по городу, контролировать выбросы бытового отхода, на неподобающем месте. При нарушении кем-либо из жителей санитарного состояния улицы составляется акт на его имя, передаваемый в правоохранительные органы, и
устанавливается размер штрафа. Ведь несанкционированный выброс бытового мусора способствует загрязнению окружающей среды и распространение болезнетворных бактерий в воздухе и в почве.
Элемент стимулирования в логистическом управлении системой ЖКХ может выражаться, например, следующим образом. Например, премировать рабочих сферы ЖКХ не за то, что ими была устранена авария на линии (водопроводной, газопроводной), а за то, что они не допустили её возникновения вообще в течение квартала.
Из вышеизложенного следуют следующие выводы:
- логистическое управление приведет к сокращению общих затрат производства;
- позволит сохранить бюджет предприятий до максимального уровня;
- сформирует модель синергетического управления в системе строительстве и ЖКХ;
- будет способствовать развитию инвестиционной деятельности сферы строительства и ЖКХ;
- появится возможность для внедрения инновационных технологий;
- приведет к общему улучшению работы системы строительства и ЖКХ.
СПИСОК ЛИТЕРАТУРЫ:
1. www. studFiles. net.
RISK ASSESSMENT SYSTEM OF INVESTMENT AND INNOVATION ACTIVITIES OF
ORGANIZATIONS
Arsenyev Yu.
Dr. Technical Science, Professor,
The Russian Presidential Academy of National Economy and Public Administration (RANEPA), a branch in
the city of Tula Davydova T.
Candidate of Science (pedagogics), Associate Professor, Tula State Pedagogical University Shamanaeva J. Tula State University
Abstract
Various risk factors affecting the activity of economic entities in modern conditions and ways of their adequate assessment are analyzed.
Keywords: management, investment and innovation activity, evaluation criteria, risks, uncertainty, models.
Even under favorable economic conditions, there is always a probability of occurrence of crisis phenomena associated with risk for any economic entity (EE). World experience in the development of countries proves that ignoring or underestimating economic risk when developing economic policies, tactics and development strategies, making specific decisions inevitably hinders their development, condemning them to stagnation [1-5].
The economic, political and business environment introduces conditions of uncertainty into the activities of Russian organizations, expands the zones of risk situations. This creates ambiguity and uncertainty in obtaining the expected final result, and the degree of risk
increases. Awareness of the possibility of a partial prediction of uncertainty in the future, the impact of risks on the final results of activities led to the development and implementation of risk management systems in Russia. The use of a risk management system is particularly relevant in investment and innovation activities, use of internal resources, when the income is again invested in the processes of creating innovations. One of the criteria for making such investment decisions is that an investment project is effective if its profitability and risk are balanced in a proportion acceptable to the project participants. In a situation of uncertainty, it is impossible to confidently talk about the results and the magnitude of costs, since they are not yet received, but
only expected in the future. A new risk factor appears, which must be taken into account when analyzing the effectiveness of investments. Investments in any project involve a certain risk, as the income from the project and the amount of losses are random, rather than deterministic values unknown at the time of making a decision to invest. When analyzing an investment project, you should take into account and evaluate as many risks as possible and try to minimize the overall risk of the project.
The most important indicator characterizing the measure of EE risk is its level, which significantly affects the level of profitability of financial transactions, financial security and reflects the degree of protection of financial activity from external and internal threats. The level of risk characterizes the probability of its occurrence under the influence of groups of factors and possible losses in the event of a risk event.
Economic and statistical methods are the basis for quantifying the level of financial risk by such calculated indicators as the level of financial risk, variance, standard deviation, coefficient of variation, etc. Expert methods for assessing the level of risk are applied if there is no necessary information for calculations using economic and statistical methods, and are based on a survey of qualified specialists with subsequent mathematical processing of its results. In conditions of uncertainty, the likelihood of different alternatives for the development of events to a person taking a risky decision (PRD) is unknown. In this case, when choosing an alternative, the decision maker is guided by both his preference for risk and the criterion for choosing a solution from all alternatives in the decision matrix.
The decision matrix can be characterized by a "winning matrix" if the efficiency index and its values aij are considered - a specific level of net discounted income corresponding to a particular i-th alternative in the j-th situation. But often situations are possible in which the factors act more favorably in comparison with the worst condition on which the PRD was oriented, when the useful result differs significantly from the maximum value. In this case, the decision matrix is transformed into a "loss matrix" or a "risk matrix", in which instead of the efficiency values, the deviations of the obtained results Hj from their maximum value are presented for different variants of the development of events. On the basis of these matrices, the best alternative solutions can be calculated based on the main criteria used in decision-making under uncertainty and risk. These include the Bayes-Laplace criteria (insufficient justification), Wald ("maximin"), Savage (losses from the "minimax"), Hurwitz (optimism-pessimism), and others.
Risk is a probabilistic category. In assessing uncertainty and risk, probabilistic calculations are used, characterized by the fact that the effectiveness of decisions taken depends both on deterministic factors and the likelihood of their occurrence. The probabilistic formulation of the problem of choosing optimal solutions in the economy more adequately reflects real situations, and probabilistic models often allow to reduce the risk when choosing the most effective solutions. However, the use of these models is associated with the need to
determine the probabilistic characteristics of the situations analyzed, which significantly complicates the solution of problems. Under conditions of complete uncertainty, the Bayes-Laplace rule is used for the probabilities of the situations under consideration: all the unknown probabilities of the occurrence of alternative situations pj are considered equal. Then the effective solution is chosen according to the minimum average
risk rule: min ^ P H^, where Hj is the loss matrix
j i =1
values.
The Wald criterion recommends that from all possible variants of the decision matrix, choose an alternative that maximizes the minimum win, i.e. the value of efficiency is the best of all the worst. The Wald's income criterion is written as max min (maximal criterion).
The Savage criterion assumes that among all possible variants of the decision matrix an alternative is selected, which allows to minimize the maximum losses for each of the possible solutions from the loss matrix (this criterion reduces the loss of strategy implementation).
The Hurwitz criterion makes it possible to take into account the combination of the worst states, and when choosing a solution, they are guided by some average result characterizing the state between extreme pessimism and unrestrained optimism. According to this compromise criterion for each decision determine the combination of the minimum and maximum wins. Preference is given to the solution variant for which this indicator proves to be maximum:
max
a
(1 - k )
max a
j a'J
where k is
k min,
j
the coefficient considered as an indicator of optimism (0 < k < 1); aij are the values of the solution matrix. or k = 0, the Hurwitz criterion coincides with the maximum criterion (a reference to the marginal risk, since a larger gain is usually associated with a greater risk), with k = 1, a guide to cautious behavior (according to the Wald criterion). The values of k between 0 and 1 are estimated by the values intermediate between risk and caution, taking into account the specific situation and the propensity of risk-taking to risk.
A qualitative risk analysis provides a description of the uncertainties inherent in the project, the causes that cause them, and the risks of the project, ranked by the importance and magnitude of possible losses. The main risks should be analyzed by quantitative methods and accurately assessed. Quantitative risk analysis is needed to assess how the most significant risk factors affect the performance of the investment project. A number of methods of such analysis are known: the consideration of the influence of individual factors (sensitivity analysis), the influence of a complex of factors (scenario analysis), and imitation modeling (Monte Carlo method).
Sensitivity analysis as a method of quantitative analysis consists in changing the values of key factors, substituting them into the financial model of the project
and calculating the indicators of its effectiveness with such a change. Despite the fact that the selling price strongly affects the net present value (NPV), the probability of its fluctuation may be very low, and, consequently, a change in this factor represents a minor risk. To determine this probability, use the "probability tree". First, based on expert opinions, the probability of the first level is determined - the real price may change, i.e. will be greater, less than or equal to the planned one, and then the probability of the second level - there may be a deviation by a certain amount.
The final probability of deviation of the realization price from the planned value is calculated by multiplying the probabilities of the first and second levels. This method allows to determine the influence of individual initial factors on the final result of the project. Its main drawback is the assumption that the change of one factor is considered in isolation, whereas in practice all economic factors are more or less interconnected. To assess such a situation and adjust the NPV of the investment project, the scenario analysis helps to determine the amount of risk.
Using the results of sensitivity analysis, the factors that determine the greatest impact on the project are the cost of production, the selling price, and the physical volume of sales. In accordance with the methodology of scenario analysis, it is necessary to develop options for the implementation of the investment project and expertly determine the likelihood of each of them. Thus, consider three scenarios for the development of the project: pessimistic, average, optimistic. In each scenario, the relevant values of the selected factors are fixed, after which the project efficiency indicators are calculated, the expected values of NPV and the magnitude of the risks are determined.
If the behavior of the indicators can not be controlled by experts (they can determine only the intervals of their possible fluctuation), then the methods of simulation (often the Monte Carlo method) are used. Risk analysis using this method is a combination of sensitivity analysis methods and scenarios based on computer implementation. The result of such an analysis is the probability distribution of possible project results (probability of obtaining NPV <0). To simulate it is recommended to use a normal distribution (in practice it is inherent in a lot of cases). The number of simulations can be as large as desired, taking into account the required accuracy of the analysis.
Effective management of domestic investment EE assumes the following goal: finding the optimal balance between the risk and profitability of the investment project. The risk of an investment project is a system of factors that manifests itself as a set of risks, individual for each participant in the investment project. The existence of risk is due to the inability to accurately predict the future. Hence its main property is distinguished: the risk takes place only in relation to the future and is inextricably linked with forecasting and planning, and hence with the decision making in general. In the methods of assessing the commercial efficiency of an investment project, the risks associated with the project are indirectly considered (without showing the likelihood of occurrence of a risk event and
the possible magnitude of the damage). When resources are insufficient to conduct investment analysis, only one method is used (with the correct formulation of the initial assumptions about the probability of risk). For more accurate results, it is advisable to apply the methods of scenarios and simulation modeling with full consideration of the interrelations of the initial project indicators. The evaluation of the impact of risk weakens the degree of its impact on the outcome of investment activity through mechanisms such as insurance or redundancy, hedging, diversification, exclusion or minimization, which is recommended by the EE, leading risk management, without the condition of admission of a crisis in a market environment.
Successful functioning and productive activities of the Union of Crafts are impossible without their own competitiveness, products, services provided and the work performed, the activities carried out. For the integral calculation of the level of competitiveness for m compared objects from the system of N particular criteria, it is recommended to form an evaluation matrix of the form
kij ^ max (min), j = 1, ..., m; i e Ki (K2),
where i = 1, ..., K are the numbers of the criteria; j - numbers of competing objects; kij - the value of the ith criterion for the j-th object; Ki (K2) - a subset of the competitiveness assessment criteria to be maximized or minimized; Ki& K2 = 0, Ki + K2 = K.
In order to obtain an objective assessment of the competitiveness of products or the EE itself, it is necessary to choose one scheme from the following list:
- the normalization of particular criteria due to the different size and scale of the indicators used;
- taking into account the significance (importance) of these criteria; searching for a point or area of compromise.
Normalization of all the criteria kij is achieved as follows:
- bringing them to a uniform dimension, which, with a large list of heterogeneous criteria, is difficult to apply;
- to dimensionless quantities pij, while for each of the criteria an extremal k°j value (j = 1, ..., K) is determined by choosing normalized values of the form k°j = max {kij} if i e Ki, or k°j = min{kij} , if i e K2.
The transition to the dimensionless quantities kij ^ Pij can be performed on the basis of the following normalization methods:
i) Pij = kij / k°j, j e Ki; pj = k°,,/ kij, j e K2;
2) py = (k°j - kij) / k°j , j e Ki; py = (k°j- kij) / kj, j
e K2;
3) pij = kij / (k°j - min kij), j e Ki; pij = kij / (max
ky- k°j), j e K2;
4) pij = (kij- min kij) / (k°j- min kij), j e Ki; pij =
(max kij- kij) / (max kij- k°j), j e K2;
5) pij = (koj - kij) / (k°j - min kij), j e Ki; pij = (kij
- koj) / (max kij- k°j), j e K2.
Accounting for the significance of particular quality criteria is usually conducted on the basis of expert evaluation methods, and in determining the importance
factors, one should take into account the normalization When assessing the competitiveness of EE by the
k method of scalarization, the vector formulation of the
condition: V ci = 1, ci > 0. problem kj ^ extr, j = 1, ..., m; i e Ki, K2 reduce to
i=1 a problem of the form:
The consideration of a compromise in the formal- Fbj = f1(kij, pij, i = 1,..., K) ^ extr, j = 1, ..., m;
ization of the transition from a multicriteria vector op- f^ = f (k, p, i = 1,..., K) ^ extr, j = 1, ..., m.
timality problem to a scalar one is usually based on ad- Such packages usually are many, and the choice of
ditive or multiplicative convolution of the evaluation of any of them is subjective. The following convolutions
a set of criteria. The main methods for determining are usually chosen:
trade-offs include: the method of the main criterion, the
method of concessions, the lexicographic, the game-
theoretic, the method of scalarization, etc.
K K
7b. = ^ _iw mov „ a T^J- T7b. =
1) Fbj = X c1p(v)1J, ^ max, v e Ri; Fbj = X c1p(v)1J, ^ min, v e R2;
k=1 k=1 K K
2) Fbj = ^ cip(v)ij, ^ max, v e Ri; Fbj = ^ Cip(v)ij, ^ min, v e R2;
k=1 k=1
K K
3) Fbj = ^ (p(v)ij)yi ^ max, v e Ri; Fbj = ^ (p(v)ij)yi ^ max, v e R2;
k=1 k=1
4) Fbj = min Cip(v)ij, ^ max, v e Ri; Fbj = max Cip(v)ij, ^ min, v e R2;
K
5) Fbj = V ci(qp(v)iJ -1 - 1) ^ min, v e R1; Fbj = max ci (qp(v)iJ -1 -1) ^ min, v e R1.
k=1
With the same dimensionality of all criteria, no compromise is required, and the convolution expression becomes simpler and acquires the following form:
K K
^ = V cikij, ^ max, V k e R1; Fb, = V cj
1) Fbj = X Cikij, ^ max, Vk e Ri; Fbj = X cikij, ^ min, v e R2;
k=1 k=1 KK
2) Fbj = ^ cikij, ^ max, Vk e Ri; Fbj = ^ cikij, ^ min, Vk e R2;
k=1 k=1 K K K
3) Fbj = V ci(kij - k°j)2 ^ extr; Fbj = ^ ci(kij - kj ^ max; Fbj = ^ (kij - k°,)ci ^ min; k=i k=i k=i (the signs extr, max, min vary within 1 < j < m);
7b- = ^ cikij - V cikij ^ max; Fbj = V cikij - V cikij
4) Fbj = X cikij - X cikij ^ max; Fbj = X cikij - X cikij ^ min;
keK1 keK 2 keK 2 keK1
5) Fbj = X cikij / X cikij ^ max; Fbj = X cikij / X cikij ^ min.
keK1 keK 2 keK 2 keK1
Doubts about the objectivity of the integrated assessment of the competitiveness of the facility remain in connection with the unreliability of statistical data in the information base; subjectivism of experts and assessments of their rational basis in selecting particular criteria for assessing the competitiveness of the facility; the dependence of the assessment on the size of the group, the experience and qualifications of the experts, the list of criteria applied, their impact on the result of calculations,
in conclusion, we note that this approach allows:
1) to classify, rank and evaluate EE, positioning them as leaders, middle peasants or outsiders of the market; quantitatively or qualitatively identify the competitiveness of EE by their availability, the number and correlation of components and the nature of their interrelationships;
2) to develop the directions, programs and activities to increase the competitiveness of facilities, reduce dysfunctions in the development of the EE.
REFERENCES:
1. Arsenyev Yu.N., Davydova T.Yu., Sheloba-yev S.I. Management of business entities (management of innovation and investment, quality and knowledge, safety and risk): Monograph / under scientific. Ed. Doctor of technical sciences, prof. Yu.N. Arsenyev. Tula: Publishing House of Tula State University, 20i7. 298 p.
2. Arsenyev Yu.N., Davydova T.Yu., Sheloba-yev S.I. Knowledge and risk management of activity: theory and practice: Monograph / under scientific. Ed. Doctor of technical sciences, prof. Yu.N. Arsenyev. M.- Tula: ICI "Innovations and investments", 20i7. 3i3 p.
3. Arsenyev Yu.N., Davydova T.Yu., Sychugov A.A. Project management and decision making: efficiency, reliability, quality, safety, risks: Monograph / under scientific. Ed. Doctor of technical sciences, prof.
18 Norwegian Journal of development of the International Science No 17/2018
Yu.N. Arsenyev. M.-Tula: Publishing House of Tula 4. Arsenyev Yu.N., Davydova T.Yu. Systems of
State University, 2018. 360 p. hybrid intelligence. Economy. Control. Education. M.:
High School, 2008. 564 p.
ANALYSIS OF METHODOLOGIES IN IT MANAGEMENT AND PROJECT MANAGEMENT IN THE
PUBLIC SECTOR
Arystanov N.
Master's degree in the International Information Technology University
АНАЛИЗ МЕТОДОЛОГИЙ УПРАВЛЕНИЯ IT-ПРОЕКТАМИ И ПРОЕКТНОГО УПРАВЛЕНИЯ В ГОСУДАРСТВЕННОМ СЕКТОРЕ
Арыстанов Н.К.
Магистр
Международный Университет Информационных Технологий
Abstract
In modern conditions of globalization and the necessity of sustainable development, companies are moving from the classical (functional) to the project model of management. In Kazakhstan, project management a modern tool for the rapid and effective use of innovations in production. Project management has taken on a new look, combining the best management tools into improved methods in IT market. This article provide comparative analysis of modern methodologies of project management in IT, and implementing projects at the state level in Kazakhstan and different countries.
Аннотация
В современных условиях глобализации и необходимости устойчивого развития, компании переходят от классической (функциональной) к проектной модели управления гибкостью адаптацией. В многих развитых странах, а теперь и в Казахстане управление проектами рассматривается как современный инструмент для быстрого и эффективного применения инноваций в производстве. На быстро развивающемся рынке IT индустрии, проектное управление приняло новый облик, объединив лучшие инструменты менеджмента в усовершенствованные методы. В данной статье проанализированы современные методологии ведения IT проектов и их различия, а также мировой опыт внедрения проектов на государственном уровне.
Keywords: project management, it-management, flexible methodologies, state programs, Kazakhstan
Ключевые слова: управление проектами, it-менеджмент, гибкие методологии, государственные программы, Казахстан.
Основной причиной перехода от общего менеджмента к управлению проектами чаще других, служит надежность - вместо относительной абстрактной перспективы к предсказуемым результатам. Согласно исследованиям Международной Ассоциации Управления Проектами (1РМА), по результатам которого, компании в среднем теряют около 97 млн. долл. США на каждый инвестируемый миллиард. Несмотря на такое количество убытков, проектный подход сэкономит около 2030% времени и 15-20% ресурсов. [1, с.2]
В 1Т-отрасли, управление проектами - это дисциплина, что объединяет процедуры, принципы и политику ведения бизнеса. 1Т-проект подразумевает собой деятельность, направленную на создание или использование информационных технологий. Управление ИТ-проектами включает в себя курирующие задачи по установке оборудования и модернизации сети, разработке программного обеспечения, созданию виртуальной среды и облачным вычислениям, системам управления данными и бизнес-аналитике, внедрение других ИТ-услуг. [2, с.304]
Основными отличиями 1Т-проекта от обычного проекта, можно выделить диссонанс между заказчиком и исполнителем. Чаще всего 1Т-проекты заказываются бизнес-отделом организации, а внедряются - собственным 1Т отделом или самостоятельной 1Т-компанией, то есть аутсорсинг. Другой особенностью 1Т-проекта является равномерное распределение ответственности на участников проекта, ответственность за результат ложится не только на исполнителя, но и на заказчика. Третьей особенностей 1Т-проекта можно назвать постоянное обновление и изменение по ходу выполнения. Если при построении здания возможны лишь минимальные отклонения от конечных требований и ожиданий, то в 1Т-проектах, это, весьма вероятно. Соответственно, есть риск внесения существенных изменений. Большое число изменений подразумевает под собой и большую стоимость, именно поэтому 1Т-проекты считаются одними из самых дорогих видов проектов.
В 1Т, проектный менеджмент может идти по трем жизненным циклам проекта:
