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Tactical decision support systems. Decision support systems (DSS) general concept of DSS

Introduction

Modern decision support systems (DSS) are systems that are maximally adapted to solving the problems of daily management activities, they are a tool designed to help decision makers (DM). With the help of DSS, the choice of solutions to some unstructured and semi-structured problems, including multi-criteria, can be made.

DSS, as a rule, are the result of multidisciplinary research, including the theory of databases, artificial intelligence, interactive computer systems, and simulation methods.

As rightly noted in, "... since the appearance of the first developments on the creation of a DSS, there was no clear definition of a DSS ...".

Early definitions of DSS (in the early 70s of the last century) reflected the following three points: (1) the ability to operate with unstructured or semi-structured problems, as opposed to the problems with which operations research deals; (2) interactive automated (ie, computer-based) systems; (3) separation of data and models. Here are the definitions of DSS: DSS is a set of data processing procedures and judgments that help a manager in making decisions based on the use of models.

History of the creation of DSS

Until the mid-60s of the last century, the creation of large information systems (IS) was extremely expensive, therefore the first management IS (the so-called Management Information Systems - MIS) were created in these years only in fairly large companies. MIS was designed to prepare periodic structured reports for managers.

In the late 60s, a new type of IS appears - Model-oriented Decision Support Systems (DSS) or Management Decision Systems (MDS).

According to the pioneers of DSS Keen P. G. W., Scott Morton M. S. (1978), the decision support concept was developed on the basis of "theoretical research in the field of decision making ... and technical work on the creation of interactive computer systems."

In 1971, Scott Morton's book was published, in which the results of the implementation of a DSS based on the use of mathematical models were first described.

1974 - the definition of management IS - MIS (Management Information System) is given in the work: “MIS is an integrated man-machine information supply system that supports the functions of operations, management and decision-making in an organization. The systems use computer hardware and software, management and decision-making models, and a database. "

1975 - J.D.C. Little in work proposed design criteria for DSS in management.

1978 - a textbook on DSS is published, which comprehensively describes the aspects of creating a DSS: analysis, design, implementation, evaluation and development.

1980 - S. Alter's dissertation was published, in which he gave the basics of DSS classification.

1981 - Bonczek, Holsapple and Whinston in the book created the theoretical foundations of DSS design. They identified 4 necessary components inherent in all DSS: 1) Language System (LS) - DSS can receive all messages; 2) Presentation System (PS) (DSS can issue its messages); 3) Knowledge System (KS) - all knowledge of the DSS keeps; 4) System of processing tasks (Problem-Processing System (PPS)) - a software "mechanism" that tries to recognize and solve a problem while the DSS is running.

1981 - In the book, R. Sprague and E. Carlson described how in practice it is possible to build a DSS. At the same time, the executive information system (EIS) was developed - a computer system designed to provide current adequate information to support managerial decision-making by a manager.

Since the 1990s, the so-called Data Warehouses have been developed.

At the beginning of the new millennium, a Web-based DSS was created.

On October 27, 2005 in Moscow at the International Conference "Information and Telemedicine Technologies in Health Protection" (ITTHC 2005), A. Pastukhov (Russia) presented a new class of DSS - PSTM (Personal Information Systems of Top Managers). The main difference between PSTM and existing DSS is the construction of a system for a specific person making a decision, with preliminary logical-analytical processing of information in automatic mode and displaying information on one screen.

DSS classifications

For DSS, there is not only a single generally accepted definition, but also an exhaustive classification. Different authors offer different classifications.

At the user level, Haettenschwiler (1999) divides DSS into passive, active, and cooperative DSS. A passive DSS is a system that helps the decision-making process, but cannot make a proposal as to what decision to make. An active DSS can propose which solution to choose. Cooperative allows decision makers to change, supplement or improve the solutions offered by the system, then sending these changes to the system for verification. The system changes, supplements or improves these solutions and sends them back to the user. The process continues until an agreed solution is obtained.

Conceptually, Power (2003) distinguishes Communication-Driven DSS, Data-Driven DSS, Document-Driven DSS, Knowledge-Driven DSS ) and Model-Driven DSS. Model-driven DSSs are characterized mainly by access and manipulation of mathematical models (statistical, financial, optimization, simulation). Note that some OLAP systems that allow complex data analysis can be classified as hybrid DSSs that provide modeling, search and data processing.

Communication-Driven DSS (formerly GDSS) DSS supports a group of users working on a common task.

DSS, data-driven (Data-Driven DSS) or DSS, oriented to work with data (Data-oriented DSS) are mainly focused on accessing and manipulating data. Document-Driven DSS (DSS) manage, search and manipulate unstructured information specified in various formats. Finally, Knowledge-Driven DSS provides solutions to problems in the form of facts, rules, procedures.

On the technical level Power (1997) distinguishes between enterprise-wide DSS and desktop DSS. The enterprise-wide DSS is connected to large repositories of information and serves many enterprise managers. Desktop DSS is a small system that serves only one user's computer. There are other classifications (Alter, Holsapple and Whinston, Golden, Hevner and Power). Let's just note that the Alter'a classification, excellent for its time, which divided all DSSs into 7 classes, is now somewhat outdated.

Depending on the data with which these systems work, DSS can be conditionally divided into operational and strategic. Operational DSSs are designed for immediate response to changes in the current situation in the management of financial and business processes of the company. Strategic DSSs are focused on analyzing significant amounts of heterogeneous information collected from various sources. The most important goal of these DSSs is to find the most rational options for the company's business development, taking into account the influence of various factors, such as the conjuncture of the target markets for the company, changes financial markets and capital markets, changes in legislation, etc. DSS of the first type were called Information Systems Guidelines (Executive Information Systems, IRS). In fact, they are finite sets of reports built on the basis of data from the transactional information system of the enterprise, ideally adequately reflecting the main aspects of production and financial activities in real time. The ISR has the following main features:

reports, as a rule, are based on standard queries for the organization; the number of the latter is relatively small;
WIS presents reports in the most convenient form, including, along with tables, business graphics, multimedia capabilities, etc.;
as a rule, IDBs are focused on a specific vertical market, for example, finance, marketing, resource management.

DSS of the second type involve a sufficiently deep study of data, specially transformed so that it is convenient to use them during the decision-making process. An integral component of the DSS of this level are decision-making rules, which, based on aggregated data, enable company managers to justify their decisions, use factors sustainable growth the company's business and reduce risks. DSS of the second type has been actively developing lately. Technologies of this type are based on the principles of multidimensional data presentation and analysis (

DSS architecture is presented in different ways by different authors. Let's give an example. Marakas (1999) proposed a generic architecture with 5 different parts: (a) the data management system (DBMS), (b) the model management system (MBMS), (c) the knowledge machine ( the knowledge engine (KE)), (d) the user interface, and (e) the user (s).

Notes (edit)

Literature

Larichev O. I., Petrovsky A. V. Decision support systems. Current state and prospects for their development. // Results of Science and Technology. Ser. Technical cybernetics. - T.21. M .: VINITI, 1987, p. 131-164, http://www.raai.org/library/papers/Larichev/Larichev_Petrovsky_1987.pdf
Saraev A.D., Shcherbina O.A. System analysis and modern Information Technology// Proceedings of the Crimean Academy of Sciences. - Simferopol: SONAT, 2006 .-- S. 47-59, http://matmodelling.pbnet.ru/Statya_Saraev_Shcherbina.pdf
Alter S. L. Decision support systems: current practice and continuing challenges. Reading, Mass .: Addison-Wesley Pub., 1980.
Bonczek R.H., Holsapple C., Whinston A.B. Foundations of Decision Support Systems. - New York: Academic Press, 1981.
Davis G. Management Information Systems: Conceptual Foundations, Structure, and Development. - New York: McGraw-Hill, 1974.
Druzdzel M. J., Flynn R. R. Decision Support Systems. Encyclopedia of Library and Information Science. - A. Kent, Marcel Dekker, Inc., 1999.
Edwards J.S. Expert Systems in Management and Administration - Are they really different from Decision Support Systems? // European Journal of Operational Research, 1992. - Vol. 61. - pp. 114-121.
Eom H., Lee S. Decision Support Systems Applications Research: A Bibliography (1971-1988) // European Journal of Operational Research, 1990. - N 46. - pp. 333-342.
Finlay P. N. Introducing decision support systems. - Oxford, UK Cambridge, Mass., NCC Blackwell: Blackwell Publishers, 1994.
Ginzberg M.I., Stohr E.A. Decision Support Systems: Issues and Perspectives // Processes and Tools for Decision Support / ed. by H.G. Sol .. - Amsterdam: North-Holland Pub.Co, 1983.
Golden B., Hevner A., Power D.J. Decision Insight Systems: A Critical Evaluation // Computers and Operations Research, 1986. - v. 13. - N2 / 3. - p. 287-300.
Haettenschwiler P. Neues anwenderfreundliches Konzept der Entscheidungs-unterstutzung. Gutes Entscheiden in Wirtschaft, Politik und Gesellschaft. Zurich: Hochschulverlag AG, 1999. - S. 189-208.
Holsapple C.W., Whinston A.B. Decision Support Systems: A Knowledge-based Approach. - Minneapolis: West Publishing Co., 1996.
Keen P.G.W. Decision support systems: a research perspective. Decision support systems: issues and challenges. G. Fick and R. H. Sprague. Oxford; New York: Pergamon Press, 1980.
Keen P.G.W. Decision Support Systems: The next decades // Decision Support Systems, 1987. - v. 3. - pp. 253-265.
Keen P.G.W., Scott Morton M. S. Decision support systems: an organizational perspective. Reading, Mass .: Addison-Wesley Pub. Co., 1978.
Little I.D.C. Models and Managers: The Concept of a Decision Calculus // Management Science, 1970. - v. 16. - N 8.
Marakas G. M. Decision support systems in the twenty-first century. Upper Saddle River, N.J .: Prentice Hall, 1999.
Power D. J. "What is a DSS?" // The On-Line Executive Journal for Data-Intensive Decision Support, 1997. - v. 1. - N3.
Power D. J. Web-based and model-driven decision support systems: concepts and issues. Americas Conference on Information Systems, Long Beach, California, 2000.
Power D.J. A Brief History of Decision Support Systems. DSSResources.COM, World Wide Web, version 2.8, May 31, 2003.
Scott Morton M. S. Management Decision Systems: Computer-based Support for Decision Making. - Boston: Harvard University, 1971.
Sprague R. H., Carlson E. D. Building Effective Decision Support Systems. - Englewood Cliffs, NJ: Prentice-Hall, 1982.
Sprague R.H. A Framework for the Development of Decision Support Systems // MIS Quarterly, 1980. - v. 4. - pp. 1-26.
Thierauf R.J. Decision Support Systems for Effective Planing and Control. -Englewood Cliffs, N.J: Prentice Hall, Inc, 1982 .-- 536 p.

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See what "DSS" is in other dictionaries:

DSS- Specialized enterprise for fire-prevention works CJSC http://www.sppr.ru/ Moscow, organization of DSS decision support system DSS control Prismatic suspended lamp mercury ... Dictionary of abbreviations and acronyms

DSS RK- Union of Industrialists, Entrepreneurs and Employers of the Komi Republic organization, Komi Republic

Decision support systems(DSS) are computer systems, almost always interactive, designed to help a manager (or supervisor) make decisions. DSS includes both data and models to help the decision maker solve problems, especially those that are poorly formalized. Data is often retrieved from an online query processing system or database. The model can be a simple gain-and-loss model to calculate the profit under certain assumptions, or a complex optimization model type to calculate the load for each machine in the shop. DSS and many of the systems discussed in the following sections are not always justified by the traditional cost-benefit approach; for these systems, many of the benefits are intangible, such as deeper decision making and better understanding of the data.

Rice. 1.4 shows that a decision support system requires three primary components: a management model, data management to collect and manually process data, and dialogue management to facilitate user access to the DSS. The user interacts with DSS through the user interface, selecting the private model and dataset to use, and then DSS presents the results to the user through the same user interface. The governance model and data governance go largely unnoticed, ranging from a relatively simple generic spreadsheet model to a complex, complex planning model based on mathematical programming.

Rice. 1.4. Decision support system components

An extremely popular type of DSS is in the form of a financial statement generator. Using a spreadsheet such as Lotus 1-2-3 or Microsoft Excel, models are created to predict various elements of an organization or financial condition. Previous financial statements of the organization are used as data. The initial model includes various assumptions about future trends in expense and income categories. After reviewing the results of the baseline model, the manager conducts a series of what-if studies by modifying one or more of the assumptions to determine their effect on the baseline. For example, a manager could probe the impact on profitability if new product sales grew 10% annually. Or the manager could investigate the impact of a larger-than-expected increase in the price of raw materials, for example, 7% instead of 4% annually. This type of financial statement generator is a simple yet powerful DSS for guiding financial decision making.

An example of DSS for Data Transaction Coordination is a police exit scoring system used by cities in California. This system allows the police officer to see the map and displays geographic area data, shows the police call calls, call types and call times. The system's interactive graphics capability allows an officer to manipulate the map, zone, and data to quickly and easily guess variations of police exit alternatives.

Another example of DSS is an interactive volume and production planning system for a large paper company. This system uses detailed historical data, predictive and planning models to play back on a computer the overall performance of the company under various planning assumptions. Most oil companies are developing DSS to support capital investment decisions. This system includes various financial conditions and models for creating future plans, which can be presented in tabular or graphical form.

All of the DSS examples given are referred to as specific DSSs. They are actual applications that aid in the decision making process. In contrast, a decision support system generator is a system that provides a set of capabilities to quickly and easily build specific DSSs. A DSS generator is a software package designed to run on a partially computer-based basis. In our example financial statement Microsoft Excel or Lotus 1-2-3 can be considered DSS generators, while the models for designing financial statements for a private branch based on Excel or Lotus 1-2-3 are specific DSS.

DSS is discussed in more detail in Sec. 2.2.

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Introduction

There are several definitions ISPR which, in general, revolve around the same functionality. V general view, IDMS is a system that assists decision makers (Decision Makers) in making these very decisions, using data mining, modeling and visualization tools, has a friendly (G) UI, is stable in quality, interactive and flexible in settings.

Why do we need DSS:

Difficulty making decisions
The need for an accurate assessment of the various alternatives
The need for predictive functionality
The need for multi-threaded input (to make a decision, you need conclusions based on data, expert opinions, known limitations, etc.)

The first DSS (then still without I) grew out of SPT (Transaction Processing Systems), in the mid-60s - early 70s. Then these systems did not have any interactivity, being, in fact, superstructures over the RDBMS, with some (not at all large) functionality of numerical modeling. One of the first systems can be called DYNAMO, developed in the depths of MIT and representing a system for simulating any processes based on historical transactions. After the IBM 360 mainframe market entered the market, shareware systems began to appear, which were used in the defense industry, special services and research institutes.

Since the beginning of the 80s, we can already talk about the formation DSS subclasses, such as MIS (Management Information System), EIS (Executive Information System), GDSS (Group Decision Support Systems), ODSS (Organization Decision Support Systems), etc. In fact, these systems were frameworks capable of working with data on various levels of the hierarchy (from individual to corporate), and inside it was possible to implement any logic. An example is the GADS (Gate Assignment Display System) developed by Texas Instruments for United Airlines, which supported decision-making in Field Operations - assigning gates, determining the optimal parking time, etc.

In the late 80s appeared PSPPR(Advanced) which allowed for what-if analysis and used more advanced modeling tools.

Finally, since mid-90s began to appear and ISPR, which began to be based on statistics and machine learning tools, game theory and other complex modeling.

Variety of DSS

At the moment, there are several ways classification DSS, we will describe 3 popular ones:

By area of application

Business and management (pricing, labor, products, strategy, etc.)
Engineering (product design, quality control ...)
Finance (lending and loans)
Medicine (medicines, treatments, diagnostics)
Environment

By data / model ratio(Stephen Alter's method)

FDS (File Drawer Systems - systems for providing access to the necessary data)
DAS (Data Analysis Systems - systems for fast data manipulation)
AIS (Analysis Information Systems - data access systems by type of required solution)
AFM (s) (Accounting & Financial models (systems) - systems for calculating financial consequences)
RM (s) (Representation models (systems) - simulation systems, AnyLogic as an example)
OM (s) (Optimization models (systems) - systems that solve optimization problems)
SM (s) (Suggestion models (systems) - rule-based inference systems)

By type of toolkit used

Model Driven - based on classic models (linear models, inventory management models, transport, financial, etc.)
Data Driven - based on historical data
Communication Driven - systems based on group decision-making by experts (systems for facilitating the exchange of opinions and calculating average expert values)
Document Driven is essentially an indexed (often multidimensional) document store
Knowledge Driven - Suddenly, Knowledge Driven. Moreover, the knowledge of both expert and machine-derived

I demand a complaint book! normal DSS

Despite such a variety of classification options, the requirements and attributes of the DSS fit well into 4 segments:

Quality
Organization
Restrictions
Model

In the diagram below, we will show exactly which requirements and in which segments to fit:

Separately, we note such important attributes as scalability (nowadays one agile approach is nowhere without it), the ability to process bad data, usability and user-friendly interface, and lack of demand for resources.

IDSS architecture and design

There are several approaches to how to architecturally represent a DSS. Perhaps the best description of the difference in approaches is “who knows what”. Despite the variety of approaches, attempts are being made to create some kind of unified architecture, at least at the top level.

Indeed, DSS can be divided into 4 large layers:

Interface
Modeling
Data mining
Data collection

And in these layers you can stuff any tools you want.

In the diagram below, I present my vision of the architecture, with a description of the functionality and examples of tools:

The architecture is more or less clear, let's move on to the design and construction of the DSS.

Basically, there is no rocket science here. When building IDSS, you must adhere to the following steps:

Domain analysis (actually, where we will use our IDMS)
Data collection
Data analysis
Model selection
Expert analysis \ interpretation of models
Model injection
IDSS assessment
Implementation of IDMS
Feedback collection ( at any stage, in fact)

On the diagram, it looks like this:

There are two ways to evaluate IDSS. First, the attribute matrix presented above. Secondly, according to the criterion checklist, which can be anything and depend on your specific task. As an example of such a checklist, I would give the following:

Let me emphasize that this is only IMHO and you can make a checklist convenient for yourself.

Where is machine learning and game theory?

Almost everywhere! At least on the modeling layer.

On the one hand, there are classic domains, let's call them "heavy", like supply chain management, production, inventory of goods and materials, and so on. In heavy domains, our favorite algorithms can bring additional insights for proven classic models. Example: predictive analytics on equipment failures (machine learning) will work great with some kind of FMEA analysis (classic).

On the other hand, in "light" domains like customer analytics, churn prediction, loan repayments, machine learning algorithms will be at the forefront. And in scoring, for example, you can combine the classics with NLP, when we decide whether to issue a loan based on a package of documents (just the same document driven DSS).

Classic machine learning algorithms

Let's say we have a problem: the sales manager for steel products needs to understand, at the stage of receiving an application from the client, what quality of the finished product will go to the warehouse and apply some kind of control action if the quality is lower than required.

We proceed very simply:

Step 0. Determine the target variable (well, for example, the titanium oxide content in the finished product)
Step 1. We decide on the data (we download it from SAP, Access and in general from everywhere we reach)
Step 2. Collecting features \ generating new ones
Step 3. Draw the data flow process and launch it into production
Step 4. Select and train a model, start it spinning on the server
Step 5. Determine feature importances
Step 6. We decide on the input of new data. Let our manager enter them, for example, by hand.
Step 7. We write on the knee a simple web-based interface, where the manager enters the values of important features with pens, it spins on a server with a model, and spits out the predicted product quality into the same interface

Voila, kindergarten-level IDSS is ready, you can use it.

Similar "simple" algorithms are also used by IBM in its Tivoli DSS, which allows you to determine the state of your super-computers (Watson in the first place): based on the logs, information on the Watson performance is displayed, resource availability, cost vs profit balance, maintenance requirements, etc. are predicted.

Company ABB offers its customers DSS800 for analyzing the operation of electric motors of the same ABB on a paper-making line.

Finnish Vaisala, a sensor manufacturer for the Finnish Ministry of Transport uses IDMS to predict when to use de-icer on roads to avoid accidents.

Finnish again Foredata offers a IDPR for HR, which helps to make decisions on the suitability of a candidate for a position even at the stage of resume selection.

At the Dubai airport, a DSS is operating in the cargo terminal, which determines the suspicion of the cargo. Under the hood, algorithms, based on accompanying documents and data entered by customs officials, highlight suspicious goods: the features are the country of origin, information on the package, specific information in the fields of the declaration, etc.

Thousands of them!

Conventional neural networks

In addition to simple ML, Deep Learning is excellent for DSS.

Some examples can be found in the military-industrial complex, for example in the American TACDSS (Tactical Air Combat Decision Support System). There, neurons and evolutionary algorithms are spinning inside that help in determining friend or foe, in assessing the probability of hitting with a volley at a given moment and other tasks.

In a slightly more real world, consider this example: in the B2B segment, you need to determine whether to issue a loan to an organization based on a package of documents. In B2C, the operator will torture you with questions on the phone, put down the values of the features in his system and voice the solution to the algorithm, in B2B it is somewhat more complicated.

IDSS there can be built like this: a potential borrower brings a pre-agreed package of documents to the office (well, or sends scans by email, with signatures and seals, as expected), documents are fed to OCR, then transferred to the NLP algorithm, which further divides words into features and feeds them to NN. The client is asked to drink coffee (at best), or that's where the card was drawn up there and go to come in after lunch, during this time everything will be calculated and will display a green or red smiley on the screen of the operator. Well, or yellow, if it seems to be ok, but more information is needed for the god of inquiries.

Similar algorithms are also used at the Ministry of Foreign Affairs: the visa application form + other certificates are analyzed directly at the embassy / consulate, after which one of 3 emoticons is displayed to the employee on the screen: green (to issue a visa), yellow (have questions), red (applicant in the stop list ). If you have ever received a visa to the United States, then the decision that a consular officer voices to you is precisely the result of the algorithm in conjunction with the rules, and in no way his personal subjective opinion about you :)

In heavy domains, neuron-based DSSs are also known, which determine the locations of buffer accumulation on production lines (see, for example, Tsadiras AK, Papadopoulos CT, O'Kelly MEJ (2013) An artificial neural network based decision support system for solving the buffer allocation problem in reliable production lines. Comput Ind Eng 66 (4): 1150-1162), General Fuzzy Neural Networks based on min-max (GFMMNN) for clustering water consumers ( Arsene CTC, Gabrys B, Al-Dabass D (2012) Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection. Expert Syst Appl 39 (18): 13214-13224) other.

In general, it should be noted that NNs are the best suited for making decisions under conditions of uncertainty, i.e. conditions in which real business lives. The clustering algorithms fit well too.

Bayesian networks

It sometimes happens that our data is heterogeneous in terms of the types of appearance. Let's take an example from medicine. A patient was admitted to us. We know something about him from the questionnaire (gender, age, weight, height, etc.) and anamnesis (heart attacks, for example). Let's call this data static. And we learn something about him in the process of periodic examination and treatment (several times a day we measure the temperature, blood composition, etc.). We will call this data dynamic. It is clear that a good DSS should be able to take into account all this data and issue recommendations based on the entirety of the information.

Dynamic data is updated over time, respectively, the pattern of the model will be as follows: learning-solution-learning, which is generally similar to the work of a doctor: to roughly determine the diagnosis, drip the medicine, look for the reaction. Thus, we are constantly in a state of uncertainty whether the treatment will work or not. And the patient's condition changes dynamically. Those. we need to build a dynamic DSS, and also knowledge driven.

In such cases, Dynamic Bayesian Networks (DBN) - a generalization of models based on Kalman filters and Hidden Markov Models - will help us perfectly.

Let's divide patient data into static and dynamic.

If we were building a static Bayesian grid, then our task would be to calculate the following probability:

Where is the node of our grid (the top of the graph, in fact), i.e. the value of each variable (gender, age ....), and C is the predicted class (disease).

The static grid looks like this:

But this is not ice. The patient's condition changes, time goes by, it is necessary to decide how to treat him.

This is what the DBS is for.

First, on the day of the patient's appointment, we build a static grid (as in the picture above). Then, every day i building a grid based on dynamically changing data:

Accordingly, the aggregate model will take the following form:

Thus, we will calculate the result using the following formula:

Where T- cumulative hospitalization time, N- the number of variables at each of the steps of the DBS.

It is necessary to implement this model in the DSS in a slightly different way - rather, here it is necessary to go from the opposite, first fix this model, and then build interface around... That is, in fact, we made a hard model, inside which there are dynamic elements.

Game theory

Game theory, in turn, is much better suited to IDMSs designed to make strategic decisions. Let's give an example.

Suppose there is an oligopoly on the market (a small number of rivals), there is a certain leader and this (alas) is not our company. We need to help the management make a decision about the volume of our products: if we are going to produce products in volume, and our rival is, will we go into negative territory or not? For simplicity, let's take a special case of oligopoly - duopoly (2 players). While you are thinking, RandomForest is here or CatBoost, I suggest you use the classic - Stackelberg equilibrium. In this model, the behavior of firms is described by a dynamic game with complete perfect information, while the feature of the game is the presence of a leading firm, which first sets the volume of goods output, and the rest of the firms are guided in their calculations by it.
To solve our problem, we just need to calculate something that solves the following optimization problem:

To solve it (surprise-surprise!), You just need to equate the first derivative with respect to zero.

Moreover, for such a model, we only need to know the supply on the market and the cost for the product from our competitor, then build a model and compare the resulting q with the one that our management wants to throw onto the market. Agree, somewhat easier and faster than sawing NN.

Excel is also suitable for such models and DSS based on them. Of course, if the input data needs to be calculated, then something more complicated is needed, but not much. The same Power BI will do it.

Looking for a winner in the ML vs ToG battle is pointless. Too different approaches to solving the problem, with their own pros and cons.

What's next?

With the current state of IDSS it seems to have figured out where to go next?

In a recent interview, Judah Pearl, the creator of those Bayesian networks, expressed an interesting opinion. To rephrase slightly, then

“All machine learning experts are currently doing is fitting a curve to the data. Fitting is non-trivial, complicated and dreary, but still fitting. "

(read)

Most likely, wangyu, in 10 years we will stop hard-coding the models, and will instead start teaching computers everywhere in the simulated environments that are being created. Probably, the implementation of IDSS will follow this path - along the path of AI and other skynets and WAPRs.

If you look at a closer perspective, the future of IDSS is in the flexibility of solutions. None of the proposed methods (classical models, machine learning, DL, game theory) is universal in terms of efficiency for all tasks. A good DSS should combine all these tools + RPA, while different modules should be used for different tasks and have different output interfaces for different users. A kind of cocktail, mixed, but by no means shaken.

Literature

Merkert, Mueller, Hubl, A Survey of the Application of Machine Learning in Decision Support Systems, University of Hoffenhaim 2015
Tariq, Rafi, Intelligent Decision Support Systems- A Framework, India, 2011
Sanzhez i Marre, Gibert, Evolution of Decision Support Systems, University of Catalunya, 2012
Ltifi, Trabelsi, Ayed, Alimi, Dynamic Decision Support System Based on Bayesian Networks, University of Sfax, National School of Engineers (ENIS), 2012

Federal State Budgetary Educational Institution of Higher Professional Education

"RUSSIAN ACADEMY OF FOLK ECONOMY

AND PUBLIC SERVICE

under the PRESIDENT OF THE RUSSIAN FEDERATION "

Northwest Institute of Management

Faculty: State and Municipal Administration

Department: General Management and Logistics

Course work

"Decision support systems"

3rd year student

Full-time education

Fetiskin Ivan Yurievich

Work manager

Associate Professor, Candidate of Philology

Mysin Nikolay Vasilievich

Saint Petersburg 2015

Introduction

Chapter 1. Theoretical aspects and concepts of decision support systems

1 Definition of a decision support system, its functions

2 Structure of decision support systems

3 Data warehouses

4 OLAP technologies

5 Data mining

6 Classifications of decision support systems

7 Applications

8 DSS Market

9 Decision Support System Assessment (DSS)

Chapter 2 Practice of implementation of the DSS by the example of regional offices of the Bank of Russia

1 Formulation of the goals and objectives of the study, characteristics of the investigated object

2 Overview and job description

2.1 Development of a DSS in the management of the activities of regional offices of the Bank of Russia

2.2 Description of functional subsystems

2.3 Development of a DSS at the TU level that implements methodological and instrumental solutions

3 Conclusions and results of the application of this DSS

Conclusion

Bibliography

Introduction

Developing market relations, decentralization of management, rapid obsolescence of information determine high requirements for a modern leader. Knowledge and skillful use of the provisions of management greatly facilitate the work of the manager, help him to determine priorities and systematize the work. Organizational structures serve as the basis on which all management activities are built.

Organizations create structures in order to ensure coordination and control of the activities of their departments and employees. The structures of organizations differ from each other in complexity (that is, the degree of division of activities into different functions), formalization (that is, the degree of use of pre-established rules and procedures), the ratio of centralization and decentralization (that is, the levels at which management solutions).

Structural relationships in organizations are the focus of many researchers and leaders. In order to effectively achieve the goals, it is necessary to understand the structure of work, departments and functional units. The organization of work and people greatly influences the behavior of workers. Structural and behavioral relationships, in turn, help to establish the goals of the organization, influence the attitudes and behavior of employees. A structured approach is used in organizations to ensure the basic elements of activities and the relationships between them. It involves the use of division of labor, coverage of control, decentralization and departmentalization.

In the conditions of the dynamism of modern production and social structure, management should be in a state continuous development, which today cannot be ensured without researching the ways and possibilities of this development, without choosing alternative directions. Management research is carried out in the daily activities of managers and personnel and in the work of specialized analytical groups, laboratories, departments. The need for research on management systems is dictated by a fairly wide range of problems that many organizations have to face. From correct decision the success of these organizations depends on these problems.

The organizational structure of management is one of the key concepts of management, closely related to the goals, functions, management process, the work of managers and the distribution of powers between them. Within this structure, the entire management process takes place (movement of information flows and management decision-making), in which managers of all levels, categories and professional specializations participate. The structure can be compared to the frame of a building of a management system, built so that all processes occurring in it are carried out in a timely manner and efficiently.

Differences in the structure of the organization, in the peculiarities of their functioning leave a very significant imprint on managerial activity, and in some cases have a decisive influence on it. In addition, the activities of the leader, its psychological characteristics depend not only on the type of organizational structure, but also on his hierarchical place in this structure, which, in fact, makes the topic of this course work the most relevant.

Scientifically grounded formation of organizational management structures is an urgent task of the modern stage of adaptation of business entities to market economy... In modern conditions, it is necessary to widely use the principles and methods of designing a management organization based on a systematic approach.

THE PURPOSE OF THIS COURSE WORK is to explore the principle of hierarchy in the management structure of an organization.

To achieve this goal, the following tasks have been identified in the work:

study of the essence and principles of building organizational structures, their classification and stages of historical development;

study of the essence and principles of building organizational structures;

building a strategy for organizational change.

RESEARCH METHODS: analytical, graphic.

To write this work, scientific works and developments of domestic and foreign authors were used, devoted to the issues of process management, the creation of systems to support the adoption of managerial decisions. The work used materials published in the Russian and foreign press, as well as presented on specialized professional Internet sites.

Chapter 1. Theoretical aspects and concepts of decision support systems

1 Definition of a decision support system, its functions

It is obvious that the decisions made on the strategy and tactics of the city's development must be carefully thought out and justified. This is especially important in socio-economic systems, since the decisions made relate to living people, their material and spiritual condition. Nevertheless, today the decision-making by the mayor, city administration, and committees is based on the experience and intuition of the leaders. But socio-economic systems are complex and their behavior is difficult to predict due to the presence of a huge number of direct and feedback links, often not obvious at first glance. The human brain is unable to cope with a task of this dimension; therefore, it is necessary to provide information and analytical support for decision making. In recent years, a new direction has been formed and is actively used in the field of automation of managerial work - decision support systems. They are successfully used in a wide variety of industries: telecommunications, finance, trade, industry, medicine and many others.

The concept of decision support systems (DSS) includes a number of tools, united by a common goal - to facilitate the adoption of rational and effective management decisions.

Decision support system (DSS) is a computer automated system, the purpose of which is to help people who make decisions in difficult conditions for a complete and objective analysis of subject activity. It is an interactive system using decision rules and corresponding database models, as well as an interactive computer simulation process.

DSS arose as a result of the merger of management information systems and database management systems. DSS are human-machine systems that allow decision-makers to use data, knowledge, objective and subjective models to analyze and solve unstructured and poorly formalized problems.

The decision-making process is the receipt and selection of the most optimal alternative, taking into account the miscalculation of all the consequences. When choosing alternatives, it is necessary to choose the one that most fully meets the set goal, but at the same time it is necessary to take into account a large number of conflicting requirements and, therefore, to evaluate the chosen solution option according to many criteria.

The decision support system is designed to support multi-criteria decisions in a complex information environment. At the same time, multicriteria is understood as the fact that the results of decisions made are evaluated not one by one, but by a set of many indicators (criteria) considered simultaneously. Information complexity is determined by the need to take into account a large amount of data, the processing of which is practically impossible without the help of modern computer technology. Under these conditions, the number possible solutions, as a rule, very large, and the choice of the best of them "by eye", without a comprehensive analysis can lead to gross errors.

Also DSS allows to facilitate the work of enterprise managers and increase its efficiency. They greatly speed up the resolution of business problems. DSS help to establish interpersonal contact. On their basis, you can conduct education and training. These information systems allow you to increase control over the activities of the organization. Having a well-functioning DSS provides great advantages over competing structures. Thanks to the proposals put forward by the DSS, new approaches are opening up to solving everyday and non-standard tasks.

DSS is characterized by the following distinctive features:

· focus on solving poorly structured (formalized) tasks, typical mainly for high levels of management;

· the possibility of combining traditional methods access and processing of computer data with the capabilities of mathematical models and methods for solving problems based on them;

· focusing on the non-professional end user of a computer through the use of an interactive mode of operation;

· high adaptability, providing the ability to adapt to the peculiarities of the existing hardware and software, as well as to the requirements of the user.

The decision support system solves two main tasks:

.choosing the best solution out of many possible ones (optimization);

2.ordering of possible solutions by preference (ranking).

Various methods are used to analyze and develop proposals in the DSS. It can be:

· information search,

· data mining,

· search for knowledge in databases,

· reasoning based on precedents,

· simulation modeling,

· evolutionary computing and genetic algorithms,

· neural networks,

· situational analysis,

· cognitive modeling, etc.

Some of these techniques have been developed within the framework of artificial intelligence. If the work of the DSS is based on the methods of artificial intelligence, then they speak of an intelligent DSS or IDSS.

Classes of systems close to DSS are expert systems and automated control systems.

The system allows you to solve the tasks of operational and strategic management based on the credentials of the company.

The decision support system is a set of software tools for data analysis, modeling, forecasting and management decisions, consisting of the corporation's own developments and purchased software products (Oracle, IBM, Cognos).

Theoretical research in the development of the first decision support systems was carried out at the Carnegie Institute of Technology in the late 50s and early 60s of the XX century. Experts from the Massachusetts Institute of Technology managed to combine theory with practice in the 60s. In the mid and late 80s of the XX century, systems such as EIS, GDSS, ODSS began to appear. In 1987, Texas Instruments developed the Gate Assignment Display System for United Airlines. This made it possible to significantly reduce losses from flights and regulate the management of various airports, ranging from International Airport O Hare in Chicago and ending at Stapleton in Denver, Colorado. In the 90s, the scope of DSS capabilities expanded with the introduction of data warehouses and OLAP tools. The emergence of new reporting technologies has made the DSS indispensable in management.

1.2 Structure of the DSS

If we talk about the structure of the DSS, there are four main components:

· Information data warehouses. A data warehouse is a data bank of a certain structure, containing information about the production process of a company in a historical context. The main purpose of the store is to provide fast execution of arbitrary analytical queries. (For more details on data warehouses, see Section 1.3 of Chapter 1.)

· Multidimensional database and analysis tools OLAP (On-Line Analitycal Processing) - the service is a tool for analyzing large amounts of data in real time. (in detail in paragraph 1.4 of Ch. 1)

· Data Mining Tools. With the help of data mining tools, you can conduct deep data exploration. (For more details, see section 1.5, Ch. 1.)

The DSS is based on a set of interrelated models with appropriate information support for research, expert and intelligent systems that include experience in solving management problems and ensure the participation of a team of experts in the process of developing rational decisions.

Below in Fig. 1 is an architectural and technological diagram of information and analytical decision support:

Fig. 1 Architectural and technological scheme of the DSS

DSS analytical systems allow you to solve three main tasks:

.accounting,

.analysis of information in real time (OLAP),

.data mining.

3 Data warehouses

It is clear that decision making should be based on real data about the control object. This information is usually stored in online databases of OLTP systems. However, this intelligence is not suitable for analysis purposes, since aggregated information is mainly needed for analysis and strategic decision-making. In addition, for the purposes of analysis, it is necessary to be able to quickly manipulate information, present it in various aspects, make various ad hoc queries to it, which is difficult to implement on operational data for reasons of performance and technological complexity.

The solution to this problem is to create a separate data warehouse (DW) containing aggregated information in a convenient form. The purpose of building a data warehouse is to integrate, update and reconcile operational data from heterogeneous sources to form a single consistent view of the control object as a whole. At the same time, the concept of data warehouses is based on the recognition of the need to separate the datasets used for transactional processing and the datasets used in decision support systems. Such division is possible by integrating detailed data disaggregated in various data processing systems (DDS) and external sources into a single repository, their coordination and, possibly, aggregation.

The main advantages of DSS data warehouses should be noted:

· Single source of information: the company receives a verified single information environment on which all reference and analytical applications will be built in the subject area for which the repository is built. This environment will have a single interface, unified storage structures, common reference and other corporate standards, which will facilitate the creation and maintenance of analytical systems.

· Also, when designing an information data warehouse, special attention is paid to the reliability of information that goes into the data warehouse.

· Performance: The physical structures of the data warehouse are specially optimized to perform completely random samples, which allows you to build really fast query systems.

· Development speed: the specific logical organization of the repository and the existing specialized software allow you to create analytical systems with minimal programming costs.

· Integration: the integration of data from different sources has already been done, so there is no need to make a data connection every time for queries that require information from several sources. Integration means not only joint physical storage of data, but also their subject, coordinated union; cleaning and reconciliation during their formation; compliance with technological features, etc.

· Historicity and stability: OLTP systems operate with up-to-date data, the term of use and storage of which usually does not exceed the value of the current business period (six months to a year), while the data warehouse is aimed at long-term storage of information for 10-15 years. Stability means that the actual information in the data warehouse is not updated or deleted, but only specially adapted to changes in business attributes. Thus, it becomes possible to carry out a historical analysis of information.

· Independence: the dedicated information storage significantly reduces the load on OLTP systems from analytical applications, thereby the performance of existing systems does not deteriorate, but in practice there is a decrease in response time and an improvement in system availability.

Thus, the data warehouse operates according to the following scenario. According to the specified regulations, it collects data from various sources - databases of operational processing systems. The storage maintains chronology: along with the current, historical data is stored with an indication of the time to which they relate. As a result, the necessary available data about the control object is collected in one place, brought to a single format, agreed upon and, in some cases, aggregated to the minimum required level of generalization.

And based on the data warehouse, it is already possible to prepare reports for management, analyze data using OLAP technologies and data mining.

The DSS reporting service helps an organization to cope with the creation of all kinds of information reports, references, documents, summary sheets, etc., especially when the number of reports issued is large and the report forms change frequently. DSS tools, automating the issuance of reports, allow them to transfer their storage into electronic form and distribute them over the corporate network between the employees of the company.

Along with large corporate data warehouses, Data Mart is also widely used. A data mart is understood as a small specialized storage for a certain narrow subject area, focused on storing data related to one business topic. The project to create a data mart requires less investment and is completed in a very short time. There can be several such data marts, for example, a revenue data mart for the company's accounting department and a customer data mart for a company's marketing department.

1.4 OLAP technologies

By interacting with the OLAP system, the user will be able to perform flexible viewing of information, obtain arbitrary data slices, and perform analytical operations of detailing, convolution, end-to-end distribution, comparison over time. All work with an OLAP system occurs in terms of the subject area. The concept of online analytical processing (OLAP) is based on a multidimensional view of data.

The term OLAP was coined by E. F. Codd in 1993. In his article, he considered the shortcomings of the relational model, primarily the inability to "combine, view and analyze data from the point of view of multiple dimensions, that is, in the most understandable way for corporate analysts", and defined the general requirements for OLAP systems that extend the functionality of relational DBMSs and include multivariate analysis as one of its characteristics.

According to Codd, the multi-dimensional conceptual view is the most natural view of management personnel at the object of management. It is a multiple perspective made up of multiple independent dimensions along which specific sets of data can be analyzed. Simultaneous analysis across multiple dimensions of data is defined as multivariate analysis. Each dimension includes directions of data consolidation, consisting of a series of successive levels of aggregation, where each higher level corresponds to a greater degree of data aggregation for the corresponding dimension. Thus, the Contractor dimension can be determined by the direction of consolidation, which consists of the levels of generalization "enterprise - department - department - employee". The Time dimension may even include two consolidation directions - Year-Quarter-Month-Day and Week-Day, because time counting by month and by week is incompatible. In this case, it becomes possible to arbitrarily select the desired level of information detail for each of the measurements. The drilling down operation corresponds to the movement from the higher stages of consolidation to the lower ones; on the contrary, a rolling up operation means moving from lower levels to higher ones.

1.5 Data Mining

The greatest interest in DSS is data mining, since it allows you to carry out the most complete and deep analysis of the problem, makes it possible to discover hidden relationships, and make the most informed decision. For some time now, the modern level of development of hardware and software has made possible the widespread maintenance of databases of operational information at different levels of management. In the course of their activities, industrial enterprises, corporations, departmental structures, state authorities and local governments have accumulated large amounts of data. They store in themselves great potential for extracting useful analytical information, on the basis of which it is possible to identify hidden trends, build a development strategy, and find new solutions.

Data mining, IAD (Data Mining) is a decision support process based on the search for hidden patterns (information patterns) in data. In this case, the accumulated information is automatically generalized to information that can be characterized as knowledge.

In general, the IAD process consists of three stages:

.identifying patterns;

.using the revealed patterns to predict unknown values (predictive modeling);

.analysis of exceptions, designed to identify and interpret anomalies in the found patterns.

New computer technologies that form IAD are expert and intelligent systems, artificial intelligence methods, knowledge bases, databases, computer modeling, neural networks, fuzzy systems. Modern IAD technologies allow creating new knowledge, revealing hidden patterns, predicting the future state of systems. The main method for modeling the socio-economic development of a city is the method of simulation, which allows you to explore the city system using an experimental approach. This makes it possible to play various development strategies on the model, compare alternatives, take into account the influence of many factors, including those with elements of uncertainty.

The model constructed in this work belongs to just such a class of systems. On its basis, local self-government bodies of strategic and tactical levels are able to analyze the dynamics of the development of a complex socio-economic urban system, identify interrelationships that are not obvious at first glance, compare various alternatives, analyze anomalies and make the most informed decision.

The use of combined decision-making methods in combination with artificial intelligence and computer modeling, various simulation and optimization procedures, decision-making in combination with expert procedures is promising.

1.6 DSS classifications

According to user interaction, there are three types of DSS:

· passive ones help in the decision-making process, but cannot put forward a specific proposal;

· active participants are directly involved in the development of the right solution;

· cooperative ones involve the interaction of the DSS with the user. A proposal put forward by the system, the user can modify, improve, and then send back to the system for verification. After that, the proposal is presented to the user again, and so on until he approves the decision.

By the method of support, they are distinguished:

· model-oriented DSS, use access to statistical, financial or other models in their work;

· DSS, based on communications, support the work of two or more users engaged in a common task;

· The data-driven DSS has access to the organization's time series. They use in their work not only internal, but also external data;

· Document-oriented DSSs manipulate unstructured information contained in various electronic formats;

· Knowledge-based DSS provides specialized, fact-based problem solutions.

According to the scope of use, there are:

· System-wide - they work with large storage systems and are used by many users.

By architecture and principle of operation, they are distinguished:

· Functional DSS.

They are the most simple in terms of architecture. They are common in organizations that do not set themselves global goals and have a low level of information technology development. A distinctive feature of functional DSS is that the data contained in the files of operating systems are analyzed. The advantages of such DSSs are compactness due to the use of one platform and efficiency due to the absence of the need to reload data into a specialized system. Among the shortcomings, the following can be noted: narrowing the range of issues that can be solved using the system, reducing the quality of data due to the lack of a stage for cleaning them, increasing the load on the operating system with the potential to terminate its work.

· DSS using independent data marts.

They are used in large organizations with several departments, including information technology departments. Each specific data mart is created to solve specific problems and is focused on a separate circle of users. This greatly improves the performance of the system. Implementing such structures is fairly straightforward. On the negative side, it can be noted that the data is repeatedly entered into different storefronts, so it can be duplicated. This increases the cost of storing information and complicates the unification procedure. Filling data marts is quite difficult due to the fact that you have to use multiple sources. There is no unified picture of the organization's business due to the fact that there is no final consolidation of data.

· DSS based on two-tier data storage.

Used in large companies, the data of which is consolidated into a single system. The definitions and methods of information processing in this case are unified. To ensure the normal operation of such a DSS, it is required to allocate a specialized team that will serve it. This DSS architecture is devoid of the disadvantages of the previous one, but it does not have the ability to structure data for individual groups of users, as well as to restrict access to information. Difficulties may arise with the performance of the system.

· DSS based on a three-tier data warehouse.

Such DSSs use a data warehouse, from which data marts are formed, used by groups of users solving similar problems. Thus, access is provided both to specific structured data and to a single consolidated information. Filling data marts is simplified by using verified and cleaned data from a single source.

There is an enterprise data model. Such DSSs are distinguished by guaranteed performance. But there is data redundancy, which leads to an increase in storage requirements. In addition, such an architecture needs to be reconciled with many areas with potentially different demands.

Depending on the functional content of the system interface, there are two main types of DSS: EIS and DSS. (Execution Information System) - enterprise management information systems. These systems are aimed at unprepared users, have a simplified interface, a basic set of offered capabilities, and fixed forms of information presentation. EIS systems paint a general visual picture of the current state of the company's business indicators and their development trends, with the possibility of deepening the information under consideration to the level of large company facilities. EIS-systems are the real return that the company's management sees from the implementation of DSS technologies. (Desicion Support System) 7 - full-featured data analysis and research systems designed for trained users with knowledge both in the subject area of research and in the computer literacy. Usually, for the implementation of DSS systems (if data is available), it is enough to install and configure specialized software from providers of solutions for OLAP systems and Data Mining.

This division of systems into two types does not mean that the construction of a DSS always implies the implementation of only one of these types. EIS and DSS can operate in parallel, sharing common data and / or services, providing their functionality to both senior management and analysts of companies.

1.7 Applications

Telecommunications

Telecommunication companies use DSS to prepare and make a set of decisions aimed at retaining their customers and minimizing their churn to other companies. DSS allows companies to more effectively carry out their marketing programs, conduct more attractive pricing of their services.

Analysis of records with characteristics of calls allows you to identify categories of customers with similar stereotypes in order to differentiate the approach to attracting customers of one category or another.

There are categories of clients who are constantly changing providers in response to certain advertising campaigns. DSS allows you to identify the most characteristic features of "stable" clients, ie. customers who remain loyal to one company for a long time, making it possible to orient their marketing policy towards retaining this particular category of customers.

Banking

DSSs are used to better monitor various aspects of banking, such as servicing credit cards, loans, investments, and so on, which can significantly improve operational efficiency.

Detecting fraud, assessing lending risk, predicting changes in clientele - areas of application of DSS and data mining methods. Classification of clients, identifying groups of clients with similar needs allows for a targeted marketing policy, providing more attractive sets of services for a particular category of clients.

Insurance

A set of DSS applications in insurance business can be called classic - it is the identification of potential cases of fraud, risk analysis, customer classification.

The detection of certain stereotypes in claims for the payment of insurance compensation, in the case of large amounts, can reduce the number of cases of fraud in the future.

Analyzing the characteristic features of cases of payments on insurance obligations, Insurance companies can reduce their losses. The data obtained will lead, for example, to a revision of the system of discounts for customers falling under the identified characteristics.

Classification of customers makes it possible to identify the most profitable categories of customers in order to more accurately target the existing set of services and introduce new services.

Retail

Trading companies use DSS technologies to solve problems such as planning purchases and storage, analyzing joint purchases, and finding patterns of behavior over time.

Analysis of data on the number of purchases and the availability of goods in the warehouse over a certain period of time allows you to plan the purchase of goods, for example, in response to seasonal fluctuations in the demand for the goods.

Often, when buying a product, the buyer purchases along with him another product. Identifying groups of such goods allows, for example, placing them on adjacent shelves in order to increase the likelihood of their joint purchase.

The search for patterns of behavior in time gives an answer to the question "If a customer has purchased one product today, then after what time will he buy another product?" For example, when purchasing a camera, a customer is likely to buy film, develop and print services in the near future.

The medicine

Many expert systems are known for making medical diagnoses. They are built mainly on the basis of rules describing the combination of various symptoms of various diseases. With the help of such rules, they will find out not only what the patient is sick with, but also how to treat him. The rules help to choose means of medication, determine indications and contraindications, navigate medical procedures, create conditions for the most effective treatment, predict the outcomes of the prescribed course of treatment, etc. Data Mining technologies make it possible to detect patterns in medical data that form the basis of these rules.

Molecular genetics and genetic engineering

Perhaps the most acute and at the same time clear task of discovering patterns in experimental data is in molecular genetics and genetic engineering. Here it is formulated as a definition of the so-called markers, which are understood as genetic codes that control certain phenotypic characteristics of a living organism. Such codes can contain hundreds, thousands or more related elements.

Large funds are allocated for the development of genetic research. Recently in this area there has been a special interest in the application of Data Mining methods. There are several large firms specializing in the application of these methods for decoding the human and plant genomes.

Applied chemistry

Data Mining methods are widely used in applied chemistry (organic and inorganic). Here the question often arises of elucidating the features of the chemical structure of certain compounds that determine their properties. This problem is especially relevant in the analysis of complex chemical compounds, the description of which includes hundreds and thousands of structural elements and their bonds.

1.8 DSS Market

In the DSS market, companies offer the following types of services for creating decision support systems:

· Implementation of pilot projects on DSS systems, in order to demonstrate to the Customer's management the high-quality potential of analytical applications.

· Creation, together with the Customer, of full-featured DSS systems, including a data warehouse and Business Intelligence tools.

· Designing a data warehouse architecture, including storage structures and management processes.

· Creation of "data marts" for the selected subject area.

· Installing and configuring OLAP and Business Intelligence tools; their adaptation to the requirements of the Customer.

· Tool analysis statistical analysis and "data mining" for the selection of software products for the architecture and needs of the Customer.

· Integration of DSS systems into the corporate intranet of the Customer, automation of electronic exchange of analytical documents between users of the repository.

· Development of Executive Information Systems (EIS) for the required functionality.

· Services for the integration of databases into a unified information storage environment

· Training of the Customer's specialists in technologies of data warehouses and analytical systems, as well as working with the necessary software products.

· Provision of consulting services to the Customer at all stages of design and operation of data warehouses and analytical systems.

· Comprehensive projects for the creation / modernization of a computing infrastructure that ensures the functioning of a DSS: solutions of any scale, from local systems to systems of an enterprise / concern / industry scale.

1.9 Evaluation of a Decision Support System (DSS)

Criteria for assessing the DSS. The system must effectively manage revenues and risks under any market conditions, generating effective signals for entering and exiting the market. At the same time, the frequency of transactions should be moderate, taking into account operating costs, commissions, losses on the spread, etc. The complexity of the construction should not scare away. Most of those who reject numerical methods in favor of their "intuition" end up with below average results.

A naturally important characteristic in evaluating a system is the total (final) profit. With high operating costs, such a characteristic as profit per operation becomes important. Decision accuracy (percentage), calculated as the ratio of the number of profitable transactions to the total number of transactions, is a popular characteristic for many traders, although its importance is overestimated. The point is that many effective systems are more likely to make wrong decisions than right ones, while many nonprofit (or almost nonprofit) systems are more likely to make right decisions.

Maximum losses own funds are an important characteristic for measuring the risk of strategies used by the system. Systems subject to periodic large losses cannot be considered usable, even if, in the end, they provide sufficient bottom line. At the same time, maximum losses mean not just the largest amount of losses from a sequence of unprofitable operations, but the maximum decrease in capital during the period under consideration. During such a decline, the sequence of unprofitable operations may be interrupted by separate profitable operations that are not able to change the overall unprofitable nature of the period of inefficiency of the system. The main characteristic of the system's efficiency is calculated as the ratio of the total profit to the amount of capital decrease during the period of maximum system inefficiency and is usually called the income / risk ratio. There are also many other assessments of the effectiveness of the system, sometimes quite complex, requiring a large amount of statistical calculations, but in most cases the simple characteristics given are sufficient. It should be noted that when evaluating the system, you can use the criteria that are recommended by the classical theory of portfolio management.

System optimization consists in finding the best formula for the indicator - the best in the sense of obtaining with its help the maximum and / or the most stable profit based on data collected over a long period of time. This optimization is internally inconsistent. Its critics will immediately point out that the behavior of future prices may differ from their behavior in the past. Proponents of such optimization must be convinced of the existence of certain patterns, stability in price behavior, which does not change or does not change significantly over time.

To check the effectiveness of the fact that the rules used in technical analysis give sustainable profit in the future, being themselves calculated on the basis of past data, the following simple testing method is used (the so-called blind modeling). First, the decision rule is optimized based on past data, and then it is tested against later (recent) data. In this way, you can determine how well you can generally predict the future from past data using a given rule. If an indicator with optimal parameters performs well on later data, it is hoped that it will perform well in the future.

When reevaluating the parameters of the system, one should switch to the new system only if the obtained "improvement" is statistically significant.

Robert Pelletier recommends limiting the number of parameters when constructing decision rules, since their increase increases the number of degrees of freedom of the system. In addition, there may be relationships between them, that is, they may turn out to be statistically dependent, which is usually seen from the coefficient of their mutual correlation. Pelletier believes that a good system should contain no more than 2-5 parameters.

The sample for checking the indicator should be large enough so that there are at least 30 signals for the selected period. In this case, the period should include an integer number of full long (low-frequency) cycles in order to limit the influence of offsets in the direction of selling or buying. So, for example, for a known 4-year stock market cycle, the analysis should be performed on data for at least 8 years.

organizational smart data bank

Chapter 2. Practice of implementation of the DSS on the example of regional offices of the Bank of Russia

1 Formulation of the goals and objectives of the study, characteristics of the investigated object

Currently, the Central Bank of the Russian Federation (hereinafter referred to as the Bank of Russia) is a key regulator banking system Russia and in many respects is the guarantor of its stability and economic stability. The Bank of Russia system has a complex organizational structure - the central office (hereinafter referred to as CA), regional offices (hereinafter referred to as TU), and has more than 80 thousand employees. In turn, regional offices have a network of cash settlement centers and other subdivisions that support the operation of the technical department. At present, the Bank of Russia is concerned with the solution of the following main tasks: overall cost reduction, standardization of the activities of territorial offices, improvement of the management system of territorial offices.

The process approach to management is considered as the main tool for performing these tasks, the experiment on the implementation of which at the Bank of Russia began back in 2002. The process approach is the prevailing approach to building a flexible and effective management system, which has become widespread in world practice over the past 10-15 years. The process approach presupposes a clear formulation of goals and activity strategies, a description of activities in the form of a set of interrelated processes that have concrete results at the output, a clear distribution of responsibility between all participants in the processes.

As shows world practice, the effective application of the process approach is largely determined by the presence of an information and computing system that generates and provides the information necessary for decision-making. With the help of such a system, at the level of the TC of the Bank of Russia, it would be possible to describe and control the execution of processes, estimate their cost, calculate the real load, conduct a reasonable assessment of the effectiveness of processes, employees, departments, etc. At the Central Asian level of the Bank of Russia, the system would allow comparing technical specifications for various indicators accumulated in the course of work, typing technical specifications, describing process standards, replicating them in technical specifications and solving a number of other problems.

All of the above determines the relevance of the topic of this chapter, which is devoted to the development of methodological, mathematical, and software-instrumental approaches to creating a decision support system in the field of managing the activities of regional offices of the Bank of Russia based on the process approach (hereinafter referred to as the System, DSS "Process Management").

The aim of this work is to develop a comprehensive methodological, mathematical, informational and software-instrumental support of a decision support system in the tasks of managing the activities of regional offices of the Bank of Russia, including the level of technical administration and the central office.

2 Overview and job description

2.1 Development of a new concept of a DSS in the management of the activities of regional offices of the Bank of Russia

The specificity of the Bank of Russia was analyzed, which consists in the presence of a complex organizational structure, a vertical two-tier management system for territorial institutions, clear regulation of activities based on a large-scale regulatory framework, the complexity of document flow, features of financial management, informatization and security requirements. information security... As a result, it was found that the existing products are not fully suitable for solving the problems of managing the territorial offices of the Bank of Russia.

The study of the specifics of the Bank of Russia and the analysis of the main tasks in managing the activities of the technical department made it possible to formulate the following conceptual principles for constructing the DSS:

) Two-tier structure. The developed DSS should operate at two levels - TU (regional) and CA (federal). At the regional level, the DSS supports the management of technical specifications based on a process approach; at the federal level, it provides collection of information on activities from all technical specifications, centralized storage and analysis of this information, classification of technical specifications, and the formation of standards;

) Full cycle of management based on a process approach. For effective and continuous improvement activities, an important characteristic of the DSS is the provision of a full management cycle based on a process approach, which involves the iterative execution of procedures for describing processes, monitoring and controlling performance, analyzing processes, and reengineering.

Taking into account the two-level structure of the system, the control cycle is presented in the following form (Fig. 2):

Rice. 2. Cycle of management support in DSS

)Integration of approaches and technologies. In order to most effectively solve the tasks of improving the performance of technical specifications in the created DSS, it is necessary to integrate approaches and technologies for business process management (BPMS), performance management (CPM) and business intelligence (BI). These approaches should be implemented on uniform architectural principles and function within the framework of a single information and software-technological infrastructure;

)The support of standards is necessary for solving the tasks of standardization of the TU activities. At the federal level - development, debugging, analysis of process standards, etc .; at the regional level - “overlapping” of standards on existing processes;

)Integration of processes into the data warehouse. Systems of the BPMS class are transactional and do not require a data warehouse. The Bank of Russia needs not only to organize the management of processes, but also to ensure their comprehensive analysis - dynamic, comparative, structural, etc. Therefore, information on activities should be accumulated in the data storages of each technical specification, part of the data will be transferred to the federal level (to a centralized storage);

)Development of a methodological basis for analysis. For a more complete and effective solution of the tasks of analyzing information on the activities of technical specifications, it is necessary to develop a methodological and instrumental base in the following areas: calculating the cost of processes, assessing the duration of processes, analyzing the organizational structure, managing efficiency;

)Interaction with TPK. DSS should interact with standard software systems (TPK) operating in territorial institutions. Interaction is organized with the aim of: obtaining initial data (for example, data on the costs of technical specifications); obtaining up-to-date regulatory and reference information; obtaining data on the execution of processes. Taking into account these principles, a conceptual model of the system has been developed, covering the federal and regional levels of government (Fig. 3):

Rice. 3. Conceptual model of the DSS in the management of the activities of regional offices of the Bank of Russia

The presented conceptual model most fully meets the solution of management tasks of the Bank of Russia and includes the following components:

· Regional level systems (in each territorial office). DSS of the regional level is replicable and provides uniform technical specifications for all functionality... Information about the activities of the technical department is accumulated in the data warehouse, over which the analytical BI-tools operate.

· Federal level system (in the central office). The DSS of the federal level is an integrating component that presupposes centralized storage and processing of information about the activities of all technical specifications and functional capabilities different from the system of the regional level. In the system of the federal level, data (process standards, norms, etc.) are formed, which are replicated in the DSS of the regional level.

· External sources of information are mainly provided with regional-level DSS data, these include various software systems operating in territorial institutions. External sources can be considered as external components of the DSS.

Since the system of the federal level is largely based on data transmitted from the systems of the regional level, it is first of all necessary to develop information, mathematical and instrumental support for the system of the regional level as the basis for an integral DSS of the Bank of Russia. At the same time, it should be noted that the developed methods and tools will be used in the construction of a federal-level system. In the course of the study, the structure of the DSS at the regional level was developed (Fig. 4), taking into account the scale of technical specifications, the variety of functions and processes performed, factors of the existing management practice, and the features of current automation.

Rice. 4. Structure of the DSS at the regional level of the Bank of Russia

2.2.2 Description of functional subsystems

The system includes functional subsystems that provide user interfaces and implement business functions, and technological subsystems that support the operation of functional subsystems based on unified data management mechanisms and centralized metadata. The operation of all subsystems is carried out under the control of the administration and information security subsystem, which ensures the proper level of data protection from unauthorized access in accordance with the requirements of the Bank of Russia. In the course of the study, taking into account the specifics of the Bank of Russia, the requirements for information and instrumental support of functional subsystems were developed and substantiated.

The process description subsystem is intended for a formalized description of activities in the form of a set of interrelated processes, taking into account the specifics of the Bank of Russia. To model the processes in the system, the IDEF0 and IDEF3 standards were used, supplemented with a number of additional structures: control operations, return transitions, links to other processes, auxiliary processes, points of the beginning and end of the process. When forming the information model for describing the TU processes, the specifics of the Bank of Russia and the requirements of standards were taken into account, as well as the following principles:

· Support for versioning implies maintaining a chronology of all changes in the description of processes (changes to objects are recorded as versions sorted by date). Due to this, it is possible to obtain a model of the TU activity as of any date;

· Support for modeling changes is provided by maintaining temporary versions of objects, which can be approved or canceled as needed;

· The customizability of process models involves expanding the set of attributes of process models, introducing new objects and linking with existing ones.

Taking into account the stated principles and peculiarities in the course of the study, an information model of processes and objects of their environment was developed (Fig. 5).

Rice. 5. The interrelation of the main objects of the environment of the processes.

On the basis of the formed information model, the process description subsystem allows solving the following main tasks:

· the formation of a holistic formalized model of the TU activity;

· keeping information about activities up to date;

· generation of reports and certificates on documenting the activities of technical specifications.

The subsystem for monitoring the execution of processes ensures the execution of formalized processes, routing tasks between performers in accordance with the description, monitoring compliance with deadlines and efficiency of execution, transforming data on the execution of processes from external sources into a single unified format.

As a result of the study, the life cycle of processes and operations was developed (Fig. 6), which, together with the notation for describing processes, provide the solution to the following tasks:

· organization of process execution;

· monitoring and managing the progress of the execution of processes;

· organization of control over the execution of processes at critical points;

· formation of analytical reports for managers of various levels of technical management (heads of sectors, departments, departments, top management).

Rice. 6. Life cycle of process execution

The subsystem of the cost of processes is intended for calculating the cost characteristics of processes and their analysis in various sections, provides tools for a detailed analysis of the cost characteristics of processes, balancing, comparative analysis, and various calculation options.

The activity analysis subsystem implements support for the analysis of TU activities in various aspects - efficiency, costs, personnel, processes, etc., while collecting and structuring data from external sources and other subsystems. The analytical subsystem is based on the CPM methodology, taking into account the tasks of the Bank of Russia and provides a set of analytical applications and tools for solving the following tasks:

.Management of the system of strategic goals, objectives and indicators (taking into account the targets determined at the federal level by the Bank of Russia);

.Decision-making support in the field of personnel management and the organizational structure of TU;

.Monitoring and analysis of performance indicators.

The system of strategic goals, objectives and indicators is a system of balanced indicators (BSC) and key performance indicators that can be set for processes, departments, employees, etc. All goals, objectives and indicators are chronological. The data warehouse is the data source for the BSC. Target values of indicators can be set in several scenarios; indicators can be weighted to assess the degree of achievement of goals and objectives. Based on the comparison of target and actual values, monitoring and analysis of the achievement of goals is carried out.

Decision support in personnel management includes analytical applications for analyzing the organizational structure, analyzing personnel in terms of performance discipline, effectiveness and key performance indicators, balancing and distributing functional responsibilities.

Monitoring and analysis of performance indicators is carried out using BI-tools based on a repository, while providing the ability to compare different indicators and various types of analysis (dynamic, structural, comparative, cluster, ranking, etc.).

2.2.3 Development of a DSS at the TU level that implements methodological and instrumental solutions

In the process of developing the DSS, the requirements for the construction of the system were analyzed, the logical and physical structure of the data was developed, the basic principles of building the system were substantiated, and the problems of choosing information technologies for the implementation of the system were solved.

In the structure of the system, functional subsystems are identified that implement business logic and the user interface, and technological subsystems that ensure the operation of functional subsystems based on unified data management mechanisms and centralized metadata.

The following information technologies have been selected to implement the system:

· as a basis for storing information - Oracle relational database management system version 9i;

· as a software and instrumental development environment - the analytical complex "Forecast-5", focused on the development of information and analytical systems and decision support systems in various areas of the economy;

· for developing web components - Microsoft Visual Studio 2005 integrated environment and ASP.NET platform.

In the course of creating a DSS, a set of software and technological solutions is being developed based on common architectural principles for the most optimal and reliable operation. When developing procedures for managing a complex database, including transactional and analytical segments, the following solutions were developed and applied:

· Ensuring the consistency of the data of the transactional and analytical segments of the database, for this, a system of interconnected classes has been developed, focused on the use of a unified transaction processing kernel, which is based on the use of Oracle DBMS metadata. At the level of tables, data integrity control is provided by means of the DBMS to improve the reliability of operation (Fig. 7):

Rice. 7. DSS data consistency management scheme.

· Support for versioning objects while maintaining integrity control at the DBMS level. To do this, each object is stored in two tables: the object table and the object version table;

· Database scalability at the level of attributes and objects with integrity control. For additional attributes, integrity is controlled at the trigger level; when new objects are created in tables, unified integrity control triggers are automatically created;

· Optimization of retrieving and writing to the database for large amounts of data. After the creation of the physical structure, its indexing was carried out, for the tables of the data warehouse, the means of forming Partitions of the Oracle DBMS were applied.

The sources of data for the initial filling of the DSS and subsequent updating can be data from the standard software systems operated in the TU: On-farm activity systems (VCD), Document management systems, Automation systems, etc. DSS allows you to download process descriptions from MS Word and Excel files, which important for territorial institutions that have drafts of models of processes "on paper".

The developed DSS is used in industrial mode in the National Bank of the Republic of Bashkortostan at more than 300 workplaces of managers and specialists to describe processes, organize and monitor the execution of processes, substantiate changes in the organizational structure, and analyze activities. The system describes about 980 processes, about 730 of them are approved, about 200 processes are regularly launched in industrial mode.

2.3Conclusions and results of the application of this DSS

The following main results and conclusions were obtained:

On the basis of the obtained conclusions, the concept of an integrated decision support system in the management of technical management activities is presented, focused on the integration of BPMS, BI and CPM approaches, in which the methods and algorithms developed by the author are built on the basis of a single information and instrumental environment. The concept combines both new and previously known methods for monitoring and analyzing the activities of technical institutions based on a process approach, adapted to the specifics of the Bank of Russia.

A decision support system for managing the activities of a territorial institution at the regional level has been created and tested in specific technical specifications of the Bank of Russia. The use of the DSS in technical specifications allows to increase the controllability of activities based on the process approach, improve the internal control system, optimize the existing organizational structure, and form a repository for performance indicators.

As a result of the implementation of the system, the following results were achieved (as follows from the reports to the management of the Bank of Russia):

· the system of internal control of activities has been improved;

· improved technologies for performing emission and cash transactions and reduced labor costs (for some operations up to 10%);

· centralization of the functions carried out by the Settlement and Cash Centers (13 functions in 9 processes);

· cash circulation management was transformed into two independent departments;

· positions were redistributed between departments within the security and information protection department;

· staff reduction in the economic and operational management was carried out; proposals are being prepared to optimize the workflow.

Conclusion

To date, there is no recognized leader in the production of software for building DSS systems. None of the companies produces a ready-made solution, which is called "out of the box", suitable for direct use in the customer's production process. The creation of a DSS always includes the stages of analyzing the customer's data and business processes, designing storage structures, taking into account his needs and technological processes.

Considering the size of the involved financial and other resources, the complexity and multi-stage design of DSS systems, the high cost of design errors is obvious. Mistakes in the choice of software can entail financial costs, not to mention the increase in project time. Data structure design errors can lead to both unacceptable performance characteristics and can be worth the time spent reloading the data, which sometimes reaches several days. Therefore, with a deep understanding of the architecture of data warehouses, it is necessary to avoid any mistakes, which entails a significant reduction in project execution time and the ability to get the most out of the implementation of the DSS.

It should be noted separately that the decision-making problems, namely the DSS, are underdeveloped in our country and are little applied in practice. The use of programs like the one described here is not only very simple, but also quite effective and does not require special knowledge and investment.

Several dozen different firms produce products capable of solving certain problems arising in the process of designing and operating DSS systems. This includes a DBMS, data upload / transform / load tools, OLAP analysis tools, and much more.

An independent analysis of the market, the study of at least a few of these tools is not an easy and time-consuming task.

So, in this work we got acquainted with decision support systems.

In the introduction, the relevance of this topic is substantiated, the purpose and objectives of the study are given, a general description of the work is given, and the subject of the study is identified.

The first chapter provides theoretical aspects and concepts of decision support systems, provides a detailed classification of the types of DSS, and initially disclosed their functions. Also in this chapter we got acquainted with the history of the creation of support systems, examined in more detail the structure of the DSS and its main elements. The distinctive features of decision support systems, as well as the spheres and areas in which they can be applied are given.

The methodology of decision-making support has been identified, and this allows us to summarize that its application makes it possible to:

· formalize the process of finding a solution based on the available data (the process of generating solution options);

· rank the criteria and give criterion assessments of the physical parameters affecting the problem being solved (the ability to evaluate the options for solutions);

· use formalized approval procedures when making collective decisions;

· use formal procedures for predicting the consequences of decisions made;

· choose the option that leads to the optimal solution to the problem.

It follows from this that we got acquainted with the basic things and the theoretical part about decision support systems.

The second chapter describes the practical implementation of the DSS in the field of managing the organization's activities based on the process approach (using the example of regional offices of the Bank of Russia). The concept of building a DSS "Management of the activities of regional offices of the Bank of Russia" is proposed. The conceptual model of the DSS, functional structure and requirements for the main components have been developed and substantiated. A set of methods and tools is proposed to support decision-making in the management of technical specifications, taking into account the specifics of the Bank of Russia. Requirements for information and analytical support of the system have been developed and substantiated, taking into account the urgent tasks of managing regional offices of the Bank of Russia. The results of the implementation of this system are presented based on reports to the Bank of Russia management.

Thus, we have found out how these decision support systems are applied in practice - in our case, in the banking sector.

The use of DSS is promising if only because any management decision is subjective, based on the company's policy, reflects the main goals of the organization and, most importantly, is not necessarily true. All this leads to the need to formalize the decision-making process and attract auxiliary means to reduce the risk of making the wrong decision. The latter increases with the accumulation of information to be processed. This happens because a person is either not able to process all the necessary information to make a decision on his own, or is not able to do it in time when the task is still relevant.

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Tactical decision support systems. Decision support systems (DSS) general concept of DSS

Introduction

History of the creation of DSS

DSS classifications

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Tactical decision support systems. Decision support systems (DSS) general concept of DSS

Introduction

History of the creation of DSS

DSS classifications

Notes (edit)

see also

Links

Literature

See what "DSS" is in other dictionaries:

Send your good work in the knowledge base is simple. Use the form below

Similar documents

Introduction

Variety of DSS

By area of ​​application

By data / model ratio(Stephen Alter's method)

By type of toolkit used

I demand a complaint book! normal DSS

IDSS architecture and design

Where is machine learning and game theory?

Classic machine learning algorithms

Conventional neural networks

Bayesian networks

Game theory

What's next?

Literature

Popular

By area of application