Salaeva Inga, Kostyunina Daria

In scientific research work a historical and diagnostic picture of the quality of modern forecasting is presented and forecasting technology using the Excel program is disclosed. The research report is presented in the attached file. Product project activities- on the school portal

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Open International Research Conference for High School and Students “Education. The science. Profession"

Section

Information Technology

Subject

Computer techologies and forecasting

Kostyunina Daria

Salaeva Inga

Educational institution

Municipal educational institution Gymnasium No. 39 “Classical”

Scientific adviser:

Osipova Svetlana Leonidovna, computer science teacher of the highest category

Otradny

Formulation of the problem.Forecast seasonal ice cream sales.

Initial data.Product sales volumes by season.

Solution algorithm.

1. Present ice cream sales data by season in table form.
2. The trend is determined, best approximating the actual data (in this problem this is a polynomial trend)

Conclusions.

The polynomial model describes the dependence more reliably, since its coefficient of determination R 2 closer to 1. The closer R 2 to unity, the more successfully the model is built.

The resulting model predicts seasonal ice cream sales well. But it is difficult to predict sales in the next seasons, since when extrapolating, it is not recommended to go far from the experimental area. However, you may notice that summer ice cream sales (especially in June and July) will be high.

1. Calculation of correlations

Dependencies between quantities, each of which is subject to completely uncontrollable scatter, are called correlation dependencies.

Task:

Formulation of the problem. To determine the dependence of the academic performance of high school students on two factors: the provision of the school library with textbooks and the provision of computers at the school.

Initial data.Results from measuring both factors in 11 different schools.

Solution algorithm.

1. Present the obtained data in the form of a table.
2. Calculate the coefficient using the correlation formula. IN Excel there is a function for this CORREL , which is part of the group statistical functions.

Conclusions.

Linear correlation coefficients were obtained for both dependencies. As can be seen from the table, the correlation between the provision of textbooks and academic performance is stronger than the correlation between computer support and academic performance. We can conclude that the book still remains a more significant source of knowledge than the computer.

1. Optimal planning

The objects of planning can be a variety of systems: the activities of an individual enterprise, industry, or Agriculture, region, and finally, state. It could also be a health condition or weather condition. The formulation of the planning problem is as follows:

1. There are some planned indicators: x, y and others;
2. There are some resources: R1, R2 and others, through which these targets can be achieved. These resources are almost always limited;
3. there is a certain strategic goal depending on the values x, y and other planned indicators on which planning should be oriented.

It is necessary to determine the value of planned indicators, taking into account limited resources, subject to the achievement of the strategic goal. This will be the optimal plan.

conclusions

Forecasting is an integral part of any area of ​​life, such as management or economics, mathematics or meteorology.

While working on the project, we found out that high-quality forecasting of various processes of human activity is not possible without modern computer technologies. For this purpose, we studied the capabilities of the MS Excel spreadsheet processor to create computer models used in forecasting. Many human functions in management, planning, and forecasting can be transferred to a computer.

• Tutorial

I have been doing time series forecasting for over 5 years. Last year I defended my dissertation on the topic “ Time series forecasting model using maximum similarity sampling“However, after the defense there were still quite a few questions left. Here is one of them - general classification of forecasting methods and models.

Typically, in both domestic and English-language works, authors do not ask the question of classifying forecasting methods and models, but simply list them. But it seems to me that today this area has grown and expanded so much that, even if it is the most general, classification is necessary. Below is mine own version general classification.

What is the difference between a forecasting method and a forecasting model?

Forecasting method represents a sequence of actions that need to be performed to obtain a forecasting model. By analogy with cooking, a method is a sequence of actions according to which a dish is prepared - that is, a forecast is made.

Forecasting model there is a functional representation that adequately describes the process under study and is the basis for obtaining its future values. In the same culinary analogy, the model has a list of ingredients and their ratios required for our dish - the forecast.

The combination of method and model forms a complete recipe!

Currently, it is customary to use English abbreviations for the names of both models and methods. For example, there is a famous forecasting model of autoregressive integrated moving average taking into account an external factor (auto regression integrated moving average extended, ARIMAX). This model and its corresponding method are usually called ARIMAX, and sometimes the Box-Jenkins model (method) after the authors.

First we classify the methods

If you look closely, it quickly becomes clear that the concept “ forecasting method"is much broader than the concept" forecasting model" In this regard, at the first stage of classification, methods are usually divided into two groups: intuitive and formalized.

If we remember our culinary analogy, then all recipes can be divided into formalized, that is, written down by the amount of ingredients and method of preparation, and intuitive, that is, not written down anywhere and obtained from the experience of the cook. When do we not use a recipe? When the dish is very simple: fry potatoes or cook dumplings, a recipe is not needed. When else do we not use a recipe? When we want to invent something new!

Intuitive forecasting methods deal with the judgments and assessments of experts. Today they are often used in marketing, economics, and politics, since the system whose behavior needs to be predicted is either very complex and cannot be described mathematically, or is very simple and does not need such a description. Details about this kind of methods can be found in.

Formalized methods— forecasting methods described in the literature, as a result of which forecasting models are built, that is, a mathematical relationship is determined that allows one to calculate the future value of the process, that is, make a forecast.

In my opinion, this general classification of forecasting methods can be completed.

Next we will make a general classification of models

Here it is necessary to move on to the classification of forecasting models. At the first stage, the models should be divided into two groups: domain models and time series models.

Domain Models- such mathematical forecasting models, for the construction of which the laws of the subject area are used. For example, the model used to make weather forecasts contains equations of fluid dynamics and thermodynamics. The population development forecast is made using a model built on a differential equation. The forecast of the blood sugar level of a person with diabetes is made based on a system of differential equations. In short, such models use dependencies specific to a specific subject area. This type of model is characterized by an individual approach to development.

Time series models— mathematical forecasting models that seek to find the dependence of the future value on the past within the process itself and calculate a forecast based on this dependence. These models are universal for various subject areas, that is, they are general form does not change depending on the nature of the time series. We can use neural networks to predict air temperature, and then use a similar model on neural networks to forecast stock indices. These are generalized models, like boiling water, into which if you throw a product, it will cook, regardless of its nature.

Classifying time series models

It seems to me that I should make up general classification domain models are not possible: as many domains, as many models! However, time series models lend themselves easily to simple division. Time series models can be divided into two groups: statistical and structural.

IN statistical models the dependence of the future value on the past is given in the form of some equation. These include:

1. regression models (linear regression, nonlinear regression);
2. autoregressive models (ARIMAX, GARCH, ARDLM);
3. exponential smoothing model;
4. maximum similarity sampling model;
5. etc.

IN structural models the dependence of the future value on the past is specified in the form of a certain structure and rules for transition along it. These include:

1. neural network models;
2. models based on Markov chains;
3. models based on classification and regression trees;
4. etc.

For both groups, I indicated the main, that is, the most common and detailed forecasting models. However, today there are already a huge number of time series forecasting models, and for making forecasts, for example, SVM (support vector machine) models, GA (genetic algorithm) models and many others have begun to be used.

General classification

Thus we got the following classification of models and forecasting methods.

1. Tikhonov E.E. Forecasting in market conditions. Nevinnomyssk, 2006. 221 p.
2. Armstrong J.S. Forecasting for Marketing // Quantitative Methods in Marketing. London: International Thompson Business Press, 1999. pp. 92 – 119.
3. Jingfei Yang M. Sc. Power System Short-term Load Forecasting: Thesis for Ph.d degree. Germany, Darmstadt, Elektrotechnik und Informationstechnik der Technischen Universitat, 2006. 139 p.

UPD. 11/15/2016.
Gentlemen, it has reached the point of insanity! Recently I was sent an article for review for the VAK publication with a link to this entry. Please note that neither in diplomas, nor in articles, much less in dissertations You can't link to the blog! If you want a link, use this one: Chuchueva I.A. TIME SERIES FORECASTING MODEL BY MAXIMUM SIMILARITY SAMPLING, dissertation... Ph.D. those. Sciences / Moscow State Technical University named after. N.E. Bauman. Moscow, 2012.

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The need for forecasting is objective. The future of many phenomena is unknown, but is very important for decisions made at the present moment.

The need for forecasting is objective. The future of many phenomena is unknown, but is very important for decisions made at the present moment. Processes that urgently require the use of forecasting procedures include economic activity. However, all stages of forecasting, including its organization, provision and interpretation of results are far from trivial. And IT can provide very significant assistance in this.

Forecasting: successes and failures

To date, quite a lot of research has been carried out and impressive practical solutions to the problem of forecasting in science, technology, economics, demography and other fields have been obtained. Attention to this problem is due, among other things, to the scale modern economy, the needs of production, the dynamics of social development, the need to improve planning at all levels of management, as well as accumulated experience. Forecasting is one of the decisive elements of the effective organization of management of individual economic entities and economic communities due to the fact that the quality of decisions made is largely determined by the quality of forecasting their consequences. Therefore, decisions made today must be based on reliable assessments of the possible development of the phenomena and events being studied in the future.

Many experts see improvement in forecasting in the development of appropriate information technologies. The need for their use is due to a number of reasons, including:

• growth of information volumes;
• complexity of algorithms for calculating and interpreting results;
• high requirements for the quality of forecasts;
• the need to use forecasting results to solve planning and management problems.

From time to time, information appears about positive results achieved by one or another company. A number of publications note that a successful assessment of trends in the market situation, demand for goods or services, as well as other economic processes and characteristics allows one to obtain a significant increase in profits and improve other economic indicators. The mechanism of success at first glance is simple and clear: by anticipating what will happen in the future, you can take effective measures in a timely manner, using positive trends and compensating for negative processes and phenomena.

However, there are also negative examples. As the Director of Information Service magazine previously noted, Cisco company, once hailed as a symbol of the new economy, not only failed to foresee the economic downturn of 2001, but was even worse off than others because it believed its demand forecasting software was flawless. The company's management did not assume that one of the reasons for its crisis could be the forecasting methods and technologies used. As a result of an analytical error, goods worth $2.2 billion were written off, about 20% of employees were fired, and the company's shares fell in price by almost six times. Thus, the cause of the Cisco crisis does not lie in the delays in obtaining or the insufficient amount of initial information necessary for the work of the company's analysts. Difficulties arose, obviously, due to methodological errors and inadequate assessment of the resulting forecasts. It can be assumed that the model used by Cisco did not provide the necessary level of adaptation of forecast estimates to the current change in the market situation.

Ensuring forecast quality

Accuracy, reliability and efficiency, however, like other components of forecasting quality, are ensured by a number of factors, among which it is necessary to highlight:

• software based on economic and mathematical models adequate to reality; n completeness of coverage and reliability of sources of initial information on which the work of forecasting algorithms is based;
• efficiency of processing internal and external information;
• ability to critically analyze forecast estimates;
• timeliness of making necessary changes in methodological and information support for forecasting.

Special software is based on carefully selected models, methods and techniques. Their implementation is extremely important for obtaining high-quality forecasts when solving problems of current and strategic planning. An analysis of the current situation shows that the difficulties in introducing IT, which provides forecasting of economic processes, are not only technical or methodological, but also organizational and psychological in nature. Consumers of the results sometimes do not understand the principles of the models used, their formalization and objectively existing limitations. This, as a rule, gives rise to distrust in the results obtained. Another group of implementation problems is associated with the fact that predictive models are often closed, autonomous in nature and therefore their generalization for the purpose of development and mutual adaptation is difficult. Hence, compromise solution There may be a step-by-step approach highlighting the main analytical tasks.

However, ready-made replicable or corporate solutions that provide forecasting for small and medium-sized economic entities at the system level with high quality and there are practically none available to them at a price. Currently automated systems Enterprise management is limited mainly to elementary accounting and control tasks. The reason for this situation is that before the advent of modern IT there were no broad opportunities to use effective economic and mathematical models directly in the process economic activity. In addition, the use of existing forecasting models for analytical purposes did not place such high demands on their information support.

Fundamentals of Forecasting Technologies

When building a predictive system from scratch, it is necessary to resolve a number of organizational and methodological issues. The first include:

• training users in methods of analysis and interpretation of forecast results;
• determining the directions of movement of forecast information within the enterprise, at the level of its divisions and individual employees, as well as the structure of communications with business partners and authorities;
• determining the timing and frequency of forecasting procedures;
• development of principles for linking the forecast with long-term planning and the procedure for selecting options for the results obtained when drawing up an enterprise development plan.

The methodological problems of constructing a forecasting subsystem are:

• development of the internal structure and mechanism of its functioning;
• organization of information support;
• development of mathematical software.

The first problem is the most difficult, since to solve it it is necessary to build a set of forecasting models, the scope of which is a system of interrelated indicators. The problem of systematization and evaluation of forecasting methods appears here as one of the central ones, since in order to select a specific method it is necessary to conduct a comparative analysis of them. A variant of the classification of forecasting methods, taking into account the characteristics of the knowledge system that underlies each group, can be presented in aggregate as follows: methods of expert assessments; logical modeling methods; mathematical methods.

Each group is suitable for solving a certain range of problems. Therefore, practice puts forward the following requirements for the methods used: they must be focused on a specific forecasting object, must be based on a quantitative measure of adequacy, and be differentiated by the accuracy of estimates and the forecast horizon.

The main tasks arising in the process of creating a predictive system are divided into:

• building a system of predicted processes and indicators;
• development of an apparatus for economic and mathematical analysis of predicted processes and indicators;
• specifying the method of expert assessments, identifying indicators for examination and obtaining expert assessments of some predicted processes and indicators;
• forecasting indicators and processes indicating confidence intervals and accuracies;
• development of methods for interpreting and analyzing the results obtained.

Work on information and mathematical support for the forecasting system deserves special attention. The process of creating software can be represented in the following stages:

1. development of a methodology for structural identification of a forecast object;
2. development of methods for parametric identification of a forecast object;
3. development of methods for forecasting trends;
4. development of methods for predicting harmonic components of processes;
5. development of methods for assessing the characteristics of random components of processes;
6. creation of complex models for predicting indicators that form an interconnected system.

The creation of a forecasting system requires an integrated approach to solving the problem of its information support, which is usually understood as a set of initial data used to obtain forecasts, as well as methods, methods and means that ensure the collection, accumulation, storage, retrieval and transmission of data during the operation of the forecasting system and its interaction with other enterprise management systems.

Information Support systems usually include:

Information fund (database);

Sources of formation of the information fund, flows and methods of data receipt;

Methods of accumulation, storage, updating and retrieval of data forming an information fund;

Methods, principles and rules for data circulation in the system;

Methods for ensuring the reliability of data at all stages of their collection and processing;

Methods information analysis and synthesis;

Methods for an unambiguous formalized description of economic data.

Thus, the following main components are required to implement the forecasting process:

Sources of internal information, which is based on management and accounting systems;

Sources of external information;

Specialized software that implements forecasting algorithms and analyzes results.

In addition to these components, appropriate technologies for storing, exchanging and presenting information must be used.

Confirmation of forecasting quality

Considering the importance of solving the forecasting problem for market entities, it is advisable to check the quality of the proposed methods and algorithms, as well as technologies in general, using specially selected (test) source data. A similar verification method has been used for quite a long time when assessing the adequacy of mathematical tools designed for nonlinear optimization, for example, using the Rosenbrock and Powell functions.

Confirmation (or verification) of the quality and performance of forecasting technology is usually carried out by comparing a priori known model data with their predicted values ​​and assessing the statistical characteristics of forecast accuracy. Let's consider this technique in a situation where process models are an additive combination of trend Tt, seasonal (harmonic) and random components.

In Fig. 1, as an illustration of the trend of the additive model, a second-order parabolic trend is presented, in Fig. 2 - seasonal component of the process with a period of 12 months, and in Fig. 3 - random component. A comparison of the actual implementation of the process with its forecast, carried out within the framework of the short-term forecasting methodology, is shown in Fig. 4. Absolute errors are illustrated in Fig. 5. The quality of the technology is assessed by the statistical characteristics of the errors in forecast estimates.

Practice and prospects for the development of forecasting in replicated and corporate systems

Currently, a variety of software tools have become widespread, providing, to one degree or another, the collection and analytical processing of information. Some of them, for example MS Excel, are equipped with built-in statistical functions and programming tools. Others, especially inexpensive accounting and management accounting, do not have such capabilities or the analytical capabilities are insufficiently implemented in them, and sometimes incorrectly. However, this is, unfortunately, also inherent in some more powerful and multifunctional enterprise management systems, which was confirmed at the past exhibitions “Pharmacy 2001” (November-December 2001) and “Accounting and Audit 2002” (January 2002). This situation is explained, apparently, by a shallow analysis on the part of the developers of the properties of the forecasting algorithms they have chosen and their uncritical application. For example, judging by the available sources, zero-order exponential smoothing is often used as the basis for predictive algorithms. However, this approach is valid only in the absence of a trend in the process being studied. In fact, economic processes are non-stationary, and forecasting involves the use of more complex models than models with a constant trend.

From the perspective of the topic under consideration, it is interesting to trace the path of development of domestic automated banking systems. Early banking systems were based on rigid technology, constantly requiring changes or additional software. This prompted financial software developers, following the principles of openness, scalability and flexibility, to use industrial DBMSs. However, these DBMSs themselves turned out to be unsuitable for solving high-level analytical problems, which include the problem of forecasting. To do this, it was necessary to use additional technologies for data storage and operational analytical processing, which ensured the operation of decision support systems for financial institutions and the preparation of forecasts. The same approach is used in complex enterprise management systems.

Another direction of modern applied use of IT-based forecasting methods is the solution of a wide range of marketing problems. An illustration is the SAS Churn Management Solution for Telecommunications software. It is intended for telecommunications operators and allows, as its developers claim, to build predictive models and, with their help, assess the likelihood of churn of certain categories of customers. The basis of this software is the distributed database server Scalable Performance Data Server, tools for building and administering warehouses and data marts, data mining tools Enterprise Miner, decision support system SAS/MDDB Server, as well as aids. To ensure the competitiveness of new-fangled CRM systems, the list of their expanded capabilities, as well as for automated banking systems, includes reporting functions that use OLAP technologies and allow, to a certain extent, forecasting the results of marketing, sales and customer service.

There are quite a lot of specialized software products, providing statistical processing of numerical data, including individual elements of forecasting. Such products include SPSS, Statistica, etc. These tools have both advantages and disadvantages that significantly limit their scope. practical application. It should be noted here that assessing the fitness of specialized mathematical and statistical software To solve forecasting problems by ordinary users without special training requires separate serious research and discussion.

However, solving forecasting problems for consumers from small and medium-sized businesses using powerful and expensive information systems and technologies is almost impossible, primarily for financial reasons. Therefore, a very promising direction is the development of the analytical capabilities of existing and widespread low-cost accounting and management accounting systems. Developed additional reports based on specific business processes and containing the necessary analytical information for a specific user have high attitude"efficiency - cost".

Some software developers create entire lines of analytical tools. For example, the Parus Corporation offers the Parus-Analytics and Triumph-Analytics solutions for a wide range of users from small and medium-sized businesses. More complex tasks of analytical processing of forecast information are integrated into the Parus system in the form of a so-called situational center. According to Dmitry Sudarev, manager for the development of circulation solutions, in 1997 it was decided to develop and implement software products that would allow the company to move from simply recording facts in an enterprise’s activities to analyzing information. At the same time, a transition was planned from automating the work of accountants and middle managers to processing information for top management. Taking into account the possible range of consumers, Parus-Analitika and Triumph-Analytika do not have any special requirements for the software and hardware environment, however, the Triumph-Analytika solution is implemented on the basis of MS SQL Server, which provides it with greater capabilities for predicting the processes under study , in particular, the harmonic component of the forecasts is taken into account.

The value of a forecast increases many times over when it is directly used in enterprise management. Therefore, an important direction is the integration of forecasting systems with systems such as Kasatka, MS Project Expert, etc. For example, SBI’s Kasatka software is positioned as automated workplace head and specialists of the marketing department and is intended for the development of management, marketing and strategic planning complexes. This purpose predetermines the need to identify long-term trends and take them into account when planning. The forecasting horizon is determined based on the relevant goals of the organization.

Conclusion

The choice of forecasting technology and means of its implementation should be carried out in accordance with the goals and objectives of a particular consumer, taking into account the level of information support, user qualifications and a number of other factors. These reasons require individual development or adaptation of previously created special software.

Literature

Glossary

Forecasting(in economic planning) - scientific and analytical stage of the process economic planning. The main tasks of forecasting in the development of economic plans are: scientific analysis of social, economic, scientific and technical processes and trends, objective connections of socio-economic phenomena in specific conditions, assessment of the current situation and identification of key problems of economic development; assessment of the development of these trends in the future and anticipation of new economic situations, new problems that require resolution; identification of possible development alternatives for an informed choice of one or another opportunity and making the optimal decision.

Automation of control- use by enterprise management bodies of methods and techniques of automatic information processing, including for the development of optimal economic decisions. Automation of management is associated with the introduction of economic-mathematical methods and IT.

Information support of the system- a set of methods and means for selecting, classifying, storing, searching, updating and processing information in the system. Information support includes: composition of information (list of information units or aggregates); structure of information and patterns of its transformation; characteristics of information flow; information quality characteristics; methods of information processing. Information support can be characterized in functional, structural, transformational and organizational and methodological aspects. The objects of the transformational aspect are the transformation of language economic management by levels and stages of information promotion in the system.

Lag lag- the time interval between the moment of occurrence of the system’s reaction (effect) to the influence applied to it and the moment of its application. In socio-economic systems, lag values ​​play a significant role in planning and management. Lagging returns on investment are especially important.

Trend(deterministic basis of the predicted process) - the general, main trend of change in the time series (process) over a sufficiently long period of observation of it. It is generally accepted that a trend is determined by the action of constantly operating factors.

Harmonic component of the predicted process- a component whose action is determined by factors of a periodic nature. A special case is the seasonal component, which is determined mainly by climatic conditions and social traditions.

Random component of the predicted process- deviations of actual process values ​​from predicted values, the causes of which have not been established and cannot be identified within the framework of the adopted model.

Economic and mathematical methods- a conventional name for a complex of scientific and applied disciplines at the intersection of economics and mathematics. They include the following groups of disciplines: economic and statistical methods; econometrics; operations research in economics; economic cybernetics.

Expert assessments- assessment of processes or phenomena that cannot be directly measured. Expert assessments play a significant role in decision making, including in predicting alternatives and their consequences.

Heuristic forecasting method- use of the opinion of experts in this field; used to forecast processes that cannot be formalized at the time of forecasting. It is synonymous with the method of expert assessments.

Mathematical forecasting methods conventionally divided into methods for modeling development processes and extrapolation methods. They are based on mathematical tools.

Methods of logical forecasting and analysis are associated primarily with the analysis of the consistency of the progress and results of forecasting. Serve as feedback in a forecasting system. Methods of logical analysis, in addition, allow you to solve independent tasks, for example, the construction of morphological models, which are subsequently used as the basis for formalized (mathematical) forecasting models.

Combined forecasting methods- joint use of heuristic and mathematical forecasting methods in order to combine their inherent advantages and compensate for shortcomings.

Interval forecast- the range of values ​​in which the predicted value will fall with a given probability given the known process parameters.

Forecasting quality criteria- the main quality criterion is the accuracy of the forecast. In addition, criteria for efficiency, reliability, etc. can be used.

Forecasting errors- the difference between the current observation of the forecast object and the expected value. Forecasting errors are caused by various reasons: uncertainty of the future situation; changes in the forecast object itself; the influence of newly emerging factors, etc.

Prediction- a judgment about the future state of an object, which is mainly subjective in nature.

Model of the forecasting object- use of the phenomenon of isomorphism (analogy) to describe a real forecast object using mathematical relationships and logical conclusions (in more rare cases, physical models are used). The model is a certain abstraction from reality, taking into account only those characteristics of the original that are of interest or have a significant impact on its development. The difficulty of choosing a model of a forecast object is determined by a number of factors: information about processes or objects similar to the one being predicted; accuracy of information about a given process (object); the amount of this information. Currently, there are many classifications of forecasting models.

Predictive system- a set of methods, methods and means of collecting initial data, processing information and presenting forecasts with the required quality.

Sources

On various enterprises There are specific requirements for creating a budget. These features are taken into account by the creators of software products. Let's look at the most famous and widespread software products.

Hyper Pillar is a large and advanced system that fully automates budgeting. To begin work, you enter planned costs and projected revenues. The result of the calculations is a dynamic model of the company with models responsible for each level and simple technology for making changes to it. The Hyper Pillar program is well integrated with other company products: Enterprise, Essbase OLAP Server, Reporting.

Corporate Planner is a budgeting program that is built on the basis of the company's structural cost tree. Tree nodes - planned, actual values ​​and deviations between them. The nodes are connected by formulas. Files can be imported via ODBC. Corporate Planner is used in small companies and does not support distributed work.

Adaytum Planning - is a three-dimensional spreadsheet with functions for constructing various slices. The tables contain various data (time, finance, etc.) for each division of the company. There is a function for summarizing the consolidated budget for a selected date. Adaytum Planning is a cost-effective product for creating a small budget through the use of a number of analytical tools.

"Jade" is a software product aimed at use in large corporations with a holding structure. Occupies an intermediate position between computer and paper processing of documentation and has a convenient budget approval procedure. The program works even with insufficiently prepared data. The initial data are the budgets of the holding's divisions, which should be combined into one holding budget. "Jade" is created on the basis of spreadsheets.

"Red Director" is a budgeting system designed for small and medium-sized enterprises and has a simple interface. The program is based on a database without the possibility of integration with other software products.

Planning is special kind scientific and practical activities, consisting in the development of strategic decisions (in the form of forecasts, projects, programs, plans), providing for the promotion of such goals and strategies for the behavior of management objects, the implementation of which ensures their effective functioning in the long term, rapid adaptation to changed external conditions.

The Project Expert program from Pro-Invest-Consulting allows users to solve the following problems:

· describe and design the activities of any enterprise in detail, taking into account changes in environmental parameters (inflation, taxes, exchange rates);

· develop a plan for the development of an enterprise or the implementation of an investment project, a marketing strategy and a production strategy that ensures rational use material, human and financial resources;

· determine the financing scheme of the enterprise;

· test different scenarios for the development of an enterprise, varying the values ​​of factors that can affect it financial results;

· prepare financial statements (report on the movement of Money, balance sheet, profit and loss statement, report on the use of profits) and a business plan for an investment project, fully compliant with international requirements, in Russian and English languages;

· conduct a comprehensive analysis of the enterprise (project), including analysis of overall efficiency, sensitivity analysis, cash flow analysis for each project participant, analysis financial condition and profitability of the enterprise using three dozen automatically calculated indicators.

The special Project Expert exchange module allows you to import and export information in *.txt and *.dbf formats. Data from summary tables and text information can be freely copied via the Windows clipboard to Word, Excel and other Windows applications. Project Expert also communicates with the most famous planning and management systems: MS Project, Primavera, Project Planner and Sure Truck. Data is imported and exported in GANTT network diagram format, with a description of the stages, their relationships, and so on.

Being the core of a complex of programs financial analysis and design, Project Expert is able to automatically “download” information characterizing the starting state of the enterprise from the financial analysis program Audit Expert, and data from the marketing operational plan from the Marketing Expert program.

The Project Expert program comes in two modifications: Base and Professional. Project Expert Professional provides its users with two additional features:

1) Updating data and monitoring the implementation of the project (plan). As the project progresses, the user has the opportunity to enter actual data for all project modules and calculate updated indicators real movement funds, as well as control the discrepancy between the real and planned cash flow.

2) Working with a group of projects. The special Project Integrator module allows you to combine several projects (enterprises) into a group and calculate integrated performance indicators for the group as a whole, as well as compare different versions of one project with each other according to any indicators.

The Biz Planner program from Pro-Invest-Consulting is a modification of Project Expert and is designed for planning and analyzing the effectiveness of investments in small and medium-sized businesses.

The Audit Expert program of Pro-Invest-Consulting is effective tool comprehensive analysis of the financial condition and performance of the enterprise. Bringing financial statements to international standards allows you to convert data from financial statements of enterprises for different years into analytical tables that meet the requirements International standards accounting.

The Marketing Expert program from Pro-Invest-Consulting is a decision support system at all stages of developing strategic and tactical marketing plans and monitoring their implementation.

The Forecast Expert program from Pro-Invest-Consulting is a universal applied forecasting system and is designed to build a time series forecast using an autoregressive model and an integrated moving average (ARISS, ARIMA, ARIMA, Box-Jenkins). Forecast Expert allows you to analyze available data and build a forecast indicating boundaries confidence interval for a period of time not exceeding the observation period of the original series. The model determines the degree of influence seasonal factors and takes them into account when creating a forecast.

The MS Project program from Microsoft is a development in the field of investment project management based on graph theory and network planning.

A study of the main directions and problems of implementation in practical activities organizations of modern information and communication technologies. Problems and directions for creating a unified information space are identified. An analysis of the conditions and prerequisites for practical modeling was carried out, and the features of the stage-by-stage construction of forecast models of organizations' activities were analyzed. Dana a brief description of features of the use of various forecasting models, emphasis is placed on the importance of checking the adequacy of forecasting models. A review of modern information and analytical technologies for forecasting the activities of organizations was carried out. Recommendations are given for using forecasting results in practice key indicators organizations.

information and analytical technologies

activity modeling

model adequacy analysis

forecasting the organization's activities

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In the context of the introduction of economic sanctions, a number of Russian enterprises search effective ways ensuring the competitiveness of your products and increasing the efficiency of the organization. In difficult economic conditions To make decisions, it is necessary to use not only practical experience in organizing a business in a certain field of activity, but also modern approaches to planning the activities of the enterprise. Widespread implementation In practice, information and analytical technologies for modeling and forecasting key business indicators allow for operational monitoring of business results and the formation of an organization’s development strategy. The use of information and analytical technologies allows you to create integrated systems for managing business results, optimize material and financial flows, minimize the costs of financial and economic activities, maximize the company’s profits and solve a number of other problems.

The processes of informatization of modern society and the closely related processes of introducing information and communication technologies into all areas of business are characterized by the massive spread of information and analytical technologies for analyzing the activities of organizations various fields and forms of ownership. Modern information technologies make it possible to automate a number of the following areas: researching the properties of a system (object), monitoring the dynamics of development of key indicators of all areas of business, optimizing the parameters of the operating system, creating integrated systems for monitoring and managing the system, planning and forecasting the prospects for the development of the organization.

Strategic goal implementation of information and communication technologies in all spheres of activity of modern society is the creation of a single information space designed to solve a wide range of issues related to access to unified databases, prompt provision statistical reporting, creation of integrated monitoring systems various directions activities. All this contributes to the creation of fundamentally new opportunities for the development of cognitive creative activity person: research, organizational and managerial, expert, entrepreneurial, etc. The creation of a unified information space helps to increase the efficiency and quality of monitoring the activities of organizations, intensify scientific research in various areas, reduce the processing time and provision of information, the efficiency and effectiveness of system management, and the integration of national information system V international systems access to information resources in the field of science, culture, business and other areas of activity.

The introduction of information and communication technologies into the practical activities of organizations is characterized by a number of areas and problems:

● the technical equipment of organizations with information and communication technologies implies access to modern software and is constrained by organizational and economic factors. Thus, access to “small informatization” is in some cases ineffective, and access to “large” is expensive and does not give a quick return.

● Training of specialists in the field of information and communication technologies, especially in the field of network technologies, should become a priority task, the solution of which determines the effectiveness of the organization’s activities in this direction. A highly qualified IT specialist can sometimes complete the work of an entire department of an organization. In this regard, it is necessary to educational organizations increasingly introduce disciplines related to information technology and increase their practical orientation. Modern system education should focus on the fundamentalization of education at all levels, the widespread use of methods and technologies of innovative education, improving the quality and accessibility of education through the development of a distance education system and equipment educational process modern information and communication technologies.

● Creating information databases for all areas of an organization’s activities requires some effort, but is an important link in the integration of an organization’s information technologies into a single information space.

One of current trends The introduction of information and analytical technologies into the practical activities of organizations is the operational monitoring of key business indicators and forecasting alternative options for the development of the company. In general, we can distinguish the following sequence of stages in predicting the development of a research system (object).

● Setting the goals and objectives of the study determines strategic guidelines and tactical directions in the study of the system, which can be clarified and specified during the research process.

● The formulation of a conceptual model of a system involves examining the system in order to identify its properties, features of dynamics and relationships with factors of external and internal environment. The collection of statistical information about the characteristics of the system presupposes the further formulation of a verbal descriptive model of the system, subject to clarification and formalization. The formulation of a conceptual model of a system presupposes a list of basic questions formulated in terms of a given area of ​​research that meet the objectives of the study, and a set of hypotheses regarding the properties and characteristics of the modeling object.

● Formalization of a verbal-descriptive model implies the construction of a mathematical model and the numerical determination of its parameters. An important point in this regard is the correct choice of methods for determining the parameters of a mathematical model. Each system is characterized by its own development features, and such characteristics of the model as adequacy, i.e., largely depend on the choice of method for numerical determination of model parameters. compliance of the formalized model with the features of real processes characterizing the dynamics of the research system. Depending on the specifics of the research system, various classes of forecasting models can be preliminarily selected, for example, growth curves that characterize the dynamics of the system over time, econometric models that establish and evaluate the relationship between various internal characteristics of the system and series external factors, varieties of adaptive models used for highly dynamic systems with seasonal and cyclical fluctuations, from the simplest to autoregressive models with autocorrelated and heteroskedastic residuals.

● Obtaining and interpreting modeling results involves checking a number of properties of the mathematical model, in particular checking the adequacy and accuracy of the model. The adequacy of the model characterizes the degree of closeness of the characteristics of the constructed model to the characteristics and properties of the real object (system). Due to a number of reasons, such as a number of assumptions that take place during modeling, the impossibility of taking into account many factors that determine the dynamics of the development of the object of study, a number of technical errors at the stage of formalizing the model and a number of other points, naturally lead to differences in the characteristics of the model and the real object . It is important that these differences are not fundamental and are within certain limits (deviations). The magnitude of permissible deviations is determined by the characteristics of the dynamics of the research system, the period of analysis of the system characteristics, as well as the purpose of the research. Indicators of model accuracy, such as the standard deviation of a number of residuals, the average error of approximation, and the average relative error, characterize the degree of approximation of the simulated data to the actual observations obtained as a result of collecting statistical information. At this stage, the refinement and final selection of the model used in the future to build a forecast is carried out. In this case, an extended check of the adequacy of the model is carried out, including, in addition to testing hypotheses about the fulfillment of a number of statistical properties of the residual component, such as independence, randomness, equality of the mathematical expectation of the residuals to zero, the fulfillment of the normal distribution law, assessment of a number of such model characteristics as the coefficient of determination, characterizing the proportion of variation the studied characteristic under the influence of external and internal factors, Fisher's coefficient, which evaluates the statistical significance of the resulting model. Based on the results of comparing the characteristics of adequacy and accuracy, the final choice of the forecast model is made.

● Constructing forecasts using a formalized model and using modeling results in system management involves obtaining point forecasts that characterize the prospects for the development of the research system. In addition to them, interval forecasts can be constructed, which carry a higher probability of obtaining intervals in which the characteristics of the system may fluctuate. It should be noted that forecasting is probabilistic in nature and will be reliable only if during the lead-up period the same patterns of development operate as those that took place at the stage of system research.

The use of forecasting results in making management decisions is creative process and requires not only theoretical knowledge in a certain area, but also practical experience on working with the research system. Nowadays Scientific research have made great progress in developing information and analytical technologies for forecasting the activities of organizations. For example, the technologies of neural network forecasting, fuzzy logic, a number of specialized multifunctional analysis and forecasting programs, such as Statistica, SPSS, Stadia, VSTAT, Project Exspert and a number of other software products are known. For operational monitoring and forecasting of system functioning results, as well as for educational purposes, the MS Excel package can also be used, which implements trend and regression analysis, and also allows, based on a spreadsheet processor, to calculate a number of additional system characteristics.

Based on the results of a study of a management system (object) using information and analytical forecasting technologies, recommendations can be formulated for improving the activities of the organization (system), for example, focusing on achieving certain values ​​of key performance indicators that implement the organization’s development strategy, optimizing cash flows, developing new promising directions activities. The use of modern information and analytical technologies for modeling and forecasting will help improve operational efficiency in the light of the implementation of the organization's development strategy and tactics.

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