What DSS systems exist. Decision support systems for business Decision support systems highload

A decision support system (DSS) is a computer interactive system designed to help a manager (or executive) make decisions. DSS include both data and models to help the decision maker solve problems, especially those that are poorly formalized.

The systems are aimed at chief executives and middle managers, at change, flexibility and quick response. The emphasis is on models, assumptions and display of graphics. The basis is professional analysis and design techniques. These systems are iterative, not rigid, and never finished. This is required by the essence of unstructured problems, which are original and unusual, for which there are no algorithms for solving and each has its own answer.

DSS are therefore designed to support semistructured and unstructured application analysis to help design, evaluate alternatives, and monitor implementation. The most common type of DSS is in the form of a financial report generator.

The advantage of a computer is its enormous speed and memory, which makes it necessary in almost all areas of human activity.

In decision making, the most important areas in which the computer becomes a human's closest assistant are:

· quick access to information accumulated in the decision maker’s computer or in a computer network;

· performing optimization or interactive simulation based on mathematical or heuristic models;

· finding in databases previously made decisions in situations similar to those under study, for use by decision makers at the appropriate moment;

· using the knowledge of the best specialists in their field included in the knowledge bases of expert systems;

· presentation of results in the form most suitable for the decision maker.

· But the traditional use of computers is not the most effective. The manager, in addition to information from the database, in addition to some economic or technological calculations, in his activities encounters a large number of system management tasks that cannot be solved within the framework of traditional information technologies.

· Due to the need to solve problems of this kind, a new type of computer systems was developed - decision support systems (DSS).

· DSS are information processing systems for the purpose of interactively supporting the activities of a manager in the decision-making process.

· There are two main areas of such support:

· facilitating the interaction between data, procedures for analyzing and processing data and decision-making models, on the one hand, and the decision-maker, as a user of these systems, on the other;

· providing supporting information, especially for solving unstructured or semi-structured problems for which it is difficult to determine in advance the data and procedures for the corresponding decisions.

· In other words, DSS are computerized assistants that support the manager in converting information into actions that are effective for the managed system. These systems must have qualities that make them not only useful, but also indispensable for decision makers. Like any information systems, they must provide the specific information needs of the decision-making process. In addition, and this, apparently, is the main thing - the DSS should adapt to his working style, reflect his thinking style, and assist all (ideally) or most of the important aspects of the decision-maker’s activity. DSS should be able to adapt to changes in computational models, communicate with the user in a language specific to the domain being controlled (ideally in natural language), and present results in a form that would facilitate a deeper understanding of the results.

· At the same time, naturally, the role of the DSS is not to replace the manager, but to increase his efficiency. The purpose of the DSS is not to automate the decision-making process, but to implement cooperation and interaction between the system and a person in the decision-making process. The DSS must support intuition, be able to recognize ambiguity and incomplete information, and have the means to overcome them. They should be friendly to decision makers, helping them in conceptually defining tasks, offering familiar presentations of results.

· Each manager has unique knowledge, talent, experience and work style. One of the goals of the DSS is to help a person improve these qualities. In addition to the known requirements for information systems (a powerful DBMS that provides effective access to data, their integrity and protection; developed analytical and computational procedures that ensure data processing and analysis; transportability, reliability, flexibility, the ability to include new technological procedures), DSS must have specific features:

The ability to develop variants of solutions in special situations that are unexpected for the decision maker;

The ability of models used in systems to adapt to a specific, specific reality as a result of dialogue with the user;

Possibility of interactive model generation system.

Due to the fact that the decision maker does not always have a well-defined goal in each situation, the decision is a research process, and the DSS is a means of more in-depth knowledge of the system and improving one’s style of work as a manager. As a rule, DSS have a modular structure, which allows you to include new procedures and upgrade those already included in the system in accordance with new requirements.

Decision making involves the sequential implementation of the following steps: understanding the problem, diagnostics, conceptual or mathematical modeling, developing alternatives and selecting those that best satisfy the goals, as well as monitoring the implementation of the decision.

DSS are designed to help decision makers at each of the listed steps and, therefore, progress in the development and expansion of the scope of their application depends both on the concept of their construction and on the perfect reflection of each of the functions that they support.

Progress in recent years is reflected in the integration of knowledge-based systems into DSS, which allows receiving advice and explanations of the proposed solution.

The evolution of the DSS is also characterized by the level of assistance provided by the decision maker - from passive support to extended, active support. Passive support provides a convenient tool without pretending to change the existing methods of action of decision makers. The quality of these DSS depends on the convenience and accessibility of the software product, or more precisely, on its interface. In fact, these are interactive information systems that provide the manager with only those services that he requires, and only in response to his request. The passive approach includes traditional DSS that answers the question “what if?” (what if?). The decision maker selects alternatives and evaluates them, having the ability to analyze simple alternatives, generalizing, increases the efficiency of the decision-making process.

Currently, the prerequisites have been created for the transition to advanced decision support, which uses new, non-traditional areas, uses analytical methods and, in particular, multi-criteria analysis. This approach makes greater use of the normative aspect of obtaining an effective solution than conventional DSS. At the same time, there are procedures for analyzing and explaining the resulting solution and assessing both the benefits and possible losses.

Thus, the decision maker can evaluate the option proposed by the DSS and make a decision, having a broader view of both the decision itself and its consequences, thanks to the consultations provided by the system.

As a rule, DSS use information from databases and knowledge and (or) provided by the decision maker. It is known that managers also use information from textual documents, reports, special reviews, articles, etc. A wider use of unstructured information in DSS is also possible.

Currently, there are three classes of DSS depending on the complexity of the problems being solved and areas of application.

First-class DSS, which has the greatest functionality, is intended for use in higher-level government bodies (for example, ministries) and management bodies of large companies when planning large complex target programs to justify decisions regarding the inclusion in the program of various political, social or economic activities and distribution resources between them based on an assessment of their impact on achieving the main goal of the program. DSS of this class are systems for collective use, the knowledge bases of which are formed by many experts - specialists in various fields of knowledge.

DSS of the second class are systems for individual use, the knowledge bases of which are formed by the user himself. They are intended for use by mid-ranking civil servants, as well as managers of small and medium-sized firms to solve operational management problems.

Third-class DSS are systems for individual use that adapt to the user's experience. They are designed to solve frequently encountered applied problems of system analysis and management (for example, choosing a lending entity, choosing a performer of work, appointment to a position, etc.). Such systems provide a solution to a current problem based on information about the results of practical use of solutions to the same problem adopted in the past.

Competitive production must be based on the latest achievements and, therefore, it is quite easy to reorient to more advanced technologies. Therefore, a manager of any rank should provide the necessary assistance in developing and justifying decisions that are adequate to the changing conditions in which the system he manages operates and to environmental influences. DSS are a powerful tool for developing alternative courses of action, analyzing the consequences of their use and improving the skills of a manager in such an important area of ​​his activity as decision making.

- 2 Diagram of the decision-making process

The general outline of the decision-making process includes the following main stages:

Stage 1. Preliminary analysis of the problem.

At this stage the following are determined:

Main goals;

Levels of consideration, elements and structure of the system (process), types of connections;

Subsystems, the main resources they use and quality criteria for the functioning of subsystems;

Main contradictions, bottlenecks and limitations.

- Stage 2. Statement of the problem.

Setting a specific decision-making problem (DPR) includes:

Formulation of the problem;

Determining the type of task;

Identification of many alternative options and basic criteria for selecting the best ones;

Choosing a method for solving the problem.

- Stage 3. Obtaining initial data.

At this stage, ways to measure alternatives are established. This is either the collection of quantitative (statistical) data, or methods of mathematical or simulation modeling, or methods of expert assessment. In the latter case, it is necessary to solve the problems of forming a group of experts, conducting expert surveys, and preliminary analysis of expert assessments.

- Stage 4. Solving the ZPR with the involvement of mathematical methods and computer technology, experts and the decision maker.

At this stage, mathematical processing of the initial information is carried out, its clarification and modification if necessary. Processing information can be quite labor-intensive, and there may be a need for several iterations and a desire to use different methods to solve the problem. Therefore, it is at this stage that the need arises for computer support for the decision-making process, which is performed using automated decision-making systems.

- Stage 5. Analysis and interpretation of the results obtained.

The results obtained may be unsatisfactory and require changes in the formulation of the ZPR. In this case, you will need to return to stage 2 or stage 1 and go through the entire path again. Solving the problem may take a fairly long period of time, during which the environment of the problem may change and require adjustments in the formulation of the problem, as well as in the initial data (for example, new alternatives may appear that require the introduction of new criteria).

Decision-making problems can be divided into static and dynamic. The first are problems that do not require repeated solutions at short intervals. The dynamic ones include ZPR, which occur quite often. Consequently, the iterative nature of the decision-making process can be considered natural, which confirms the need to create and use effective computer support systems. ZPR requiring one cycle can be considered the exception rather than the rule.

- 3 Components of a decision support system

A decision support system requires three primary components:

Management model;

Data management model for data collection and manual processing;

Dialogue management model to facilitate user access to DSS.

The user interacts with the DSS through a user interface, selecting a particular model and data set to use, and then the DSS presents the results to the user through the same user interface. Management and data management models operate largely independently and range from a relatively simple generic spreadsheet model to a complex, complex planning model based on mathematical programming.

Using a spreadsheet such as Microsoft Excel, models are created to forecast various elements of an organization or financial condition. The data used is the organization's previous financial statements. The initial model includes various proposals for future trends in spending and income categories. After reviewing the results of the baseline model, the manager conducts a series of “what-if” studies, changing one or more assumptions to determine their impact on the baseline. For example, a manager might probe the impact on profitability if sales of a new product grew by 10% annually. Or the manager could explore the impact of a larger than expected increase in the price of raw materials, such as 7% instead of 4% annually. This type of financial report generator is a simple but powerful DSS for management when making decisions, including financial ones.

A decision support system generator is a system that provides a set of capabilities to quickly and easily build specific DSS. A DSS generator is a software package designed to solve semi-structured or unformalized problems only partially using a computer.

- 4 Use of decision support systems

Decision support systems help to find answers not only to the direct question “what if?”, but also to similar ones. Typical questions about decision support systems (DSS):

1. Case analyzes – assessment of the values ​​of output quantities for a given set of output variables.

2. Parametric (case analyses) analysis – assessment of the behavior of output quantities when the values ​​of the original variables change.

3. Sensitivity analysis - study of the behavior of the resulting variables depending on changes in the value of one or more input variables.

4. Possibility analysis – finding the values ​​of the input variable that provide the desired final result (also known as “search for target solutions”, “analysis of goal values”, “management by goals”).

5. Impact analysis - identifying for a selected resulting variable all input variables that affect its value, and estimating the magnitude of the change in the resulting variable for a given change in the input variable, say by 1%.

6. Data analysis – direct input of previously known data into the model and manipulation during forecasting.

7. Comparison and aggregation - comparing the results of two or more forecasts made under different input assumptions, or comparing predicted results with actual results, or combining results obtained from different forecasts or for different models.

8. Command sequences – the ability to use and save for later use regularly executed series of commands and messages.

9. Risk analysis – assessment of the performance of output variables with random changes in input values.

10. Optimization is the search for values ​​of controlled input variables that provide the best value for one or more result variables.

11. Examples of problems solved using DSS: choosing methods to conquer the household appliances market; assessment of the prospects of alternative fuels for cars.

12. Recently, DSS have begun to be used in the interests of small and medium-sized businesses (for example, choosing an option for locating retail outlets, choosing a candidate to fill a vacant position, choosing an informatization option, etc.). In general, they are able to support the individual style and meet the personal needs of the manager.

13. There are systems designed to solve complex problems in large commercial and government organizations:

14. Airline system. The air transportation industry uses a decision support system - Analytical Information Management System. She was created American Airlines, but is also used by other companies, aircraft manufacturers, airline analysts, consultants and associations. This system supports many solutions in this industry by analyzing data collected during vehicle recycling, assessing cargo flow, and statistically analyzing the schedule. For example, it allows you to make forecasts for the aviation market in terms of company shares, revenue and profitability. Thus, this system allows the airline management to make decisions regarding ticket prices, transport requests, etc.

15. Geographical system. A geographic information system is a special category of support systems that allows the integration of computer graphics with geographic databases and other functions of decision support systems. For example, IBMs GeoManager – is a system that allows the construction and display of maps and other visual objects to assist in making decisions regarding the geographic distribution of people and resources. For example, it allows you to create a geographic map of crime and helps to correctly redistribute police forces. It is also used to study the degree of urbanization, in the forestry industry, railway business, etc.

Introduction

Why do we need DSS?:

1. Difficulty making decisions
2. Need for accurate assessment of various alternatives
3. The need for predictive functionality
4. The need for multi-stream input (to make a decision you need conclusions based on data, expert assessments, known limitations, etc.)

Since the early 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 essence, these systems were frameworks capable of working with data on various levels of the hierarchy (from individual to organization-wide), and any logic could be implemented inside. An example is the GADS (Gate Assignment Display System) 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 they appeared PSPPR(Advanced), which allowed for “what-if” analysis and used more advanced modeling tools.

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

Variety of DSS

By area of ​​application

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

According to the data/model relationship(Stephen Alter 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 solution required)
• 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) - systems for constructing logical conclusions based on rules)

By type of tools used

• Model Driven - based on classical 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 - essentially an indexed (often multidimensional) document store
• Knowledge Driven - suddenly, based on knowledge. What does knowledge have to do with both expert and machine-derived knowledge?

I demand a complaint book! normal DSS

1. Quality
2. Organization
3. Restrictions
4. Model

Let us separately note such important attributes as scalability (in the current agile approach there is nowhere without this), the ability to process bad data, usability and user-friendly interface, and low requirements for resources.

Architecture and design of ISPR

Indeed, the DSS can be divided into 4 large layers:

1. Interface
2. Modeling
3. Data Mining
4. Data collection

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 actual construction of the DSS.

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

1. Domain analysis (in fact, where we will use our IDSS)
2. Data collection
3. Data analysis
4. Model selection
5. Expert analysis\interpretation of models
6. Implementation of models
7. ISPR assessment
8. Implementation of ISDS
9. Collecting feedback ( at any stage, In fact)

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

I would like to emphasize that this is only IMHO and you can make a convenient checklist for yourself.

Where are machine learning and game theory?

On the one hand, there are classic domains, let’s call them “heavy” ones, such as supply chain management, production, inventories and so on. In difficult domains, our favorite algorithms can bring additional insights to proven classic models. Example: predictive analytics for equipment failures (machine learning) will work perfectly with some FMEA analysis (classic).

On the other hand, in “soft” domains, such as customer analytics, churn prediction, loan repayments, machine learning algorithms will take the lead. And in scoring, for example, you can combine classics with NLP, when deciding whether to issue a loan based on a package of documents (just like a document driven DSS).

Classic machine learning algorithms

Let's do it very simply:

Step 0. Determine the target variable (well, for example, the content of titanium oxide in the finished product)
Step 1. Decide on the data (download from SAP, Access and generally from anywhere we can 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 the model, start it spinning on the server
Step 5. Define feature importances
Step 6. Decide on entering new data. Let our manager enter them, for example, by hand.
Step 7. We write a simple web-based interface on our knees, where the manager enters the values ​​of important features by hand, this runs on a server with a model, and the predicted quality of the product is spit out into the same interface

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

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

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

Finnish Vaisala, a manufacturer of sensors for the Finnish Ministry of Transport, uses IDS to predict when deicer should be applied on roads to avoid accidents.

Again Finnish Foredata offers an IDS for HR, which helps make decisions on the suitability of a candidate for a position at the stage of resume selection.

At Dubai Airport in the cargo terminal there is a DSS that determines the suspiciousness of the cargo. Under the hood, algorithms based on supporting documents and data entered by customs officers identify suspicious cargo: the features include the country of origin, information on the packaging, specific information in the fields of the declaration, etc.

Thousands of them!

Conventional neural networks

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

In a slightly more real world, we can consider this example: in the B2B segment, it is necessary to determine whether to issue a loan to an organization based on a package of documents. In B2C, the operator will torment you with questions over the phone, enter the values ​​of features in his system and announce the solution to the algorithm; in B2B it is somewhat more complicated.

The ISPP can be structured like this: a potential borrower brings a pre-agreed package of documents to the office (or sends scans by email, with signatures and seals, as expected), the documents are fed into OCR, then transferred to the NLP algorithm, which then divides the words into features and feeds them to NN. The client is asked to drink coffee (at best), or where the card was issued there and come back after lunch, during which time everything will be calculated and a green or red smiley will be displayed on the screen of the girl-operator. Well, or yellow, if it seems ok, but the god of information needs more information.

Similar algorithms are also used in 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 on the employee’s screen: green (issue a visa), yellow (have questions), red (applicant on the stop list) ). If you have ever received a visa to the USA, then the decision that the consulate officer voices to you is precisely the result of the algorithm’s work in conjunction with the rules, and not his personal subjective opinion about you :)

In heavy domains, DSS based on neurons is also known, which determine where the buffer accumulates 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 Min-Max Neural Networks (GFMMNN) for water consumer clustering ( 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) and others.

In general, it is worth noting that NNs are ideally suited for making decisions under conditions of uncertainty, i.e. conditions in which real business lives. Clustering algorithms also fit well.

Bayesian networks

Dynamic data is updated over time, so the model’s operating pattern will be as follows: training-solution-training, which is generally similar to the work of a doctor: roughly determine the diagnosis, administer a medicine, watch 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 (DBNs) will be of great help to us - a generalization of models based on Kalman filters and the Hidden Markov Model.

Let's divide the patient data into static and dynamic.

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

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

A static grid looks like this:

But this is not ice. The patient’s condition changes, time passes, and we must decide how to treat him.

This is why we use DBS.

First, on the day the patient is admitted, we build a static mesh (as in the picture above). Then, every day i We build a grid based on dynamically changing data:

Accordingly, the aggregate model will take the following form:

Thus, we 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 a DSS somewhat differently - rather, here we need to go from the opposite, first fix this model, and then build an interface around. That is, in essence, we made a hard model, inside of which there are dynamic elements.

Game theory

Let’s say there is an oligopoly in the market (a small number of competitors), there is a certain leader and this (alas) is not our company. We need to help management make a decision about the volume of products we produce: if we produce products in volume, and our rival produces in volume, will we go into the red or not? To simplify, let's take a special case of oligopoly - duopoly (2 players). While you are thinking about RandomForest or CatBoost, I will suggest you to use the classic - Stackelberg equilibrium. In this model, the behavior of firms is described by a dynamic game with complete perfect information, and a feature of the game is the presence of a leading firm, which is the first to set the volume of output of goods, and the remaining firms are guided in their calculations by it.
To solve our problem, we just need to calculate such that the optimization problem of the following form will be solved:

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 offer on the market and the cost of goods 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, it’s 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 handle it.

There is no point in looking for a winner in the ML vs ToG battle. Too different approaches to solving the problem, with their pros and cons.

What's next?

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

“All machine learning experts do now is fit a curve to the data. The fit is non-trivial, complex and tedious, but it’s still a fit.”

Most likely, in about 10 years, we will stop hard-coding models, and will instead begin to train computers everywhere in created simulated environments. Probably, the implementation of ISPR will follow this path - along the path of AI and other Skynets and WAPRs.

If we look at a closer perspective, then the future of DSSS lies 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 sort of cocktail, mixed, but by no means shaken.

Literature

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

Section “Information and economic systems”

UDC 658.5.011

DECISION SUPPORT SYSTEM

A. A. Starodubtsev Scientific supervisor - D. V. Tikhonenko

Siberian State Aerospace University named after Academician M. F. Reshetnev

Russian Federation, 660037, Krasnoyarsk, ave. them. gas. "Krasnoyarsk worker", 31

Email: [email protected]

It is described why decision support systems are needed, how they can be useful and their classification.

Key words: DSS, decision making, support system.

DECISION SUPPORT SYSTEM

A. A. Starodubcev Scientific Supervisor - D. V. Tkhonenko

Reshetnev Siberian State Aerospace University 31, Krasnoyarsky Rabochy Av., Krasnoyarsk, 660037, Russian Federation E-mail: [email protected]

The article explains why a decision support system is needed, than they can be useful and their classification.

Keywords: DSS, making decisions, support system.

Decision support system (DSS) is a computer automated system, the purpose of which is to help people making decisions in difficult conditions for a complete and objective analysis of subject activity.

DSS emerged from the merger of management information systems and database management systems.

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

The DSS decision support system solves two main problems. First, selecting the best solution from many possible ones (optimization). Secondly, ordering possible solutions by preference (ranking).

In both problems, the first and most important point is the selection of a set of criteria on the basis of which possible solutions will be subsequently assessed and compared (we will also call them alternatives). The DSS system helps the user make such a choice.

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

Information search;

Data mining;

Searching for knowledge in databases;

Reasoning based on precedents;

Simulation modeling;

Current problems of aviation and astronautics - 2016. Volume 2

Evolutionary computation and genetic algorithms;

Neural networks;

Situational analysis;

Cognitive modeling, etc.

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

The system allows you to solve problems of operational and strategic management based on accounting data about the company's activities.

A decision support system is a set of software tools for data analysis, modeling, forecasting and management decision-making, 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 20th century. Specialists from the Massachusetts Institute of Technology managed to combine theory with practice in the 60s. In the mid and late 80s of the 20th 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, from O"Hare International Airport in Chicago to 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 have made DSS indispensable in management.

There are several large groups of DSS.

Based on user interaction, there are three types of DSS:

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

Active ones are directly involved in developing the right solution;

Cooperative ones involve the interaction of the DSS with the user. The user can refine, improve the proposal put forward by the system, and then send it back to the system for verification. After this, the proposal is again presented to the user, and so on until he approves the solution.

According to the method of support they distinguish:

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

Communication-based DSS supports the work of two or more users engaged in a common task;

Data-centric DSSs have access to an organization's time series. They use not only internal, but also external data in their work;

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

Knowledge-oriented DSSs provide specialized, evidence-based solutions to problems.

By area of ​​use there are:

System-wide

Desktop DSS.

System-wide ones work with large data storage systems (DSS) and are used by many users. Desktop systems are small and are suitable for control from a single user’s personal computer.

The structure of the DSS includes four main components:

Information data warehouses;

Tools and methods for extracting, processing and loading data (ETL);

Multidimensional database and OLAP analysis tools;

Data Mining Tools.

Section “Information and economic systems”

DSS makes it possible to make the work of enterprise managers easier and increase its efficiency. They significantly speed up the resolution of business problems. DSS contribute to establishing interpersonal contact. On their basis, it is possible to conduct training and training. These information systems allow you to increase control over the activities of the organization. The presence of a clearly functioning DSS provides great advantages over competing structures. Thanks to the proposals put forward by DSS, new approaches to solving everyday and non-standard problems are opening up.

Using the system allows you to find answers to many questions that arise both for the general director and the head of any department.

The process of creating a management reporting, data analysis and decision support system consists of the following stages:

Analysis of existing information flows and enterprise management procedures at the enterprise;

Identification of indicators that influence the financial and economic condition of the enterprise and reflect the efficiency of doing business (based on data from systems already in use);

Development of procedures to ensure that management personnel receive the necessary information at the right time, in the right place and in the right form;

Setting up multidimensional analysis software;

Training of the Customer's personnel to work with multidimensional analysis software.

The result is thoughtful decisions based on an information foundation, adequate actions, qualified execution and, as a result, the success of the entire enterprise.

1. Decision support systems, purpose and tasks to be solved [Electronic resource]. URL: http://referatz.ru/works/296331/ (access date: 03/10/2016).

There are three types of such tools:

1. Multidimensional analysis tools - also known as OLAP (On-Line Analytical Processing) - software that allows the user to observe data in different dimensions, directions or sections.

2. Query Tools - software that allows you to create queries on data based on content or pattern.

3. Data Mining Tools - software that automatically searches for important patterns (models) or dependencies in data.

The presence in the training system, built on the basis of the classical DSS (Decision support system), of developed modeling tools and advising tools, qualitatively changes the workload of decision makers in the direction of intellectualizing their activities. This is achieved by increasing information flows passing through the training system, which is an integral part of the MRIS. This increase is associated with the development of information technologies, which currently provide more and more opportunities for processing poorly formalized information. The development in mathematics and computer science of such areas as fuzzy sets, multi-valued logic, etc., the improvement of programming tools and technical means makes it possible to carry out such processing.

The implementation of systems built on the basis of the DSS approach into practice is characterized by many problems, including poor integration of software tools that provide the characteristic capabilities of DSS. This can be explained by relatively little experience in creating and using truly developed DSS systems and the high cost of their development. The last factor is related to the need to ensure the adequacy of the DSS model for full management, as well as excessive complexity of the system and

At the same time, there is a need to develop system friendliness, which coincides with the possibilities of developing computer technology.

The desired qualities of flexibility and adaptability of a learning system require it to be deeply parameterized, which makes it extremely complex. Therefore, solutions are needed that would allow, having a basic learning algorithm, to ensure its individual character. To do this, you can use the Markov chain approach. At any given time, the amount of ignorance does not depend on the previous learning process. Then, to eliminate ignorance, there is no need to go back a step, but the learner must have convenient tools and the necessary information to deal with ignorance on their own. This solution is best suited to the EPSS (Electronic performance support system) approach - the use of electronic performance support systems, which ensures the acquisition of basic knowledge and provides decision support for the development of skills and abilities.

For EPSS, the characteristic trends compared to DSS are:

 increase in little-formalized information flow passing through the EIS;

 more friendly interface;

 more complete consideration of the user’s requirements, his psychological characteristics, mentality;

 more flexible system of technological settings;

 a more flexible and more complete system for training the user of a new functional information technology.

EPSS deepens DSS making it more comfortable for the learner by improving the tools and provides the user with the opportunity to constantly improve knowledge. EPSS is characterized by a combination of functional information technology and technology, which we will call educational. Any functional information technology in EPSS is unthinkable without an additive, which, in our case, is educational technology. The synthesis of functional information and educational technologies forms educational information technology, which forms the basis of the MRIS training system.

A particularly important distinctive feature of EPSS is the systemic integration of ascertaining, modeling, training and advising technologies into a single system.

Inside the training system, there must be a built-in EPSS block that would evaluate the fundamental possibility of the decision made by the student and its effectiveness, as well as recognize the mistakes made and determine for the system as a whole ways to eliminate their sources, i.e. methodology and form of knowledge transfer that is most successful for the learner (see Fig. 2.10).

Detailing should be carried out with a certain emphasis on the area of ​​ignorance of the student. Thus, the learning strategy can constantly change, being a function of the psychological characteristics of the student (imaginative, logical thinking) and the amount of knowledge about the object of knowledge that the student has.

Thus, the EPSS must contain:

1. Ascertaining software, that is, relevant data. For example, educational material, examples, cases, etc.

2. Simulation software that prepares an answer to the question: – “What will happen if...?”

3.Advising software that can answer the question “How to make...?”.

Typically, a good quality teaching system will change its teaching strategy depending on the context of the test questions. In this case, the student follows a certain learning algorithm, which contains a number of trajectory goals for the fulfillment of which, always the same, the system must remove any students and, having recognized ignorance, try to localize it and eliminate it by pumping up the necessary knowledge and consolidating it. If this cannot be done, the system rises to a higher concept and acts according to the same algorithm. Localization of ignorance comes down to its detailing. However, the direction of detail can be different, and this difference depends, first of all, on the characteristics of the classification of concepts that we put into the system. Simplified, the training circuit consists of two blocks. The first block, using any strategy, provides a dosed presentation of knowledge to the learner. This representation can occur in a linear or network pattern. As it moves through the learning graph, the system periodically switches to a control unit, which can be constructed in various ways.

The learning strategy does not change depending on the answers, although the correctness of the answers is checked. In traditional teaching systems, this is exactly the scheme used and alternative answers, one or more, are offered for each question. The disadvantage of this solution is that it is necessary to formulate questions and define answers very clearly, without ambiguity. It is difficult to identify the meaning of misunderstanding from the alternative answers, although in principle this shortcoming can be overcome by increasing the number of control questions.

If the learning strategy changes, then we can talk about managing the learning process, which is no different in function from managing any other object: accounting - answering test questions; analysis - recognition of the content of answers; planning system actions to adapt the learning strategy; regulation – presentation of the next piece of knowledge, the required level and meaning.

Thus, EPSS is a powerful tool for improving the efficiency of MRIS by providing personalized training, improving system management by strengthening support functions, and improving the adaptive properties of the system to the requirements of a specific user. The development of society and business requires adequate management tools. Knowledge of trends and main directions in the development of computer science allows us to develop scientifically based strategies for purposeful management of the process of its development. The global informatization of society is one of the reasons for its development, therefore the closest attention should be paid to the issues of mutual adaptation and transformation of natural structures and artificially created information systems.

33. What are the features, positive and negative sides

implementation of DSS systems?

In a small infrastructure, not every process should be described in detail using the third level, only the most important ones, the order of execution of which is critical from a security point of view, or external requirements, for example, the PCI DSS standard, contain a direct condition for their detailed detailing. In all other cases, the level of decomposition should be determined by common sense.

34. What is custom IP?

Custom or unique systems usually mean systems created for a specific enterprise, which have no analogues and are not subject to further replication. Such systems are used either to automate the activities of enterprises with unique characteristics, or to solve an extremely limited range of special tasks. Basically, such systems are used in government agencies, education, healthcare, and military organizations. Custom systems, as a rule, either do not have prototypes at all, or the use of a prototype requires significant changes of a qualitative nature. In this regard, the development of a custom system By essentially is R&D. Like any R&D, it is characterized by an increased risk in terms of obtaining the required results. To reduce development risks and costs, it is advisable to use a practice-tested methodology. It is desirable that the methodology include the following elements:

· model of the technological process (sequence of technological operations, requirements for input and output information and results);

· model of the process of managing the technological process itself (stages, processes of quality management, results, requirements for the qualifications of specialists);

· tools used in development.

One example of such a technique is the integrated use of the approach CDM Advantage of the PJM project management method and Designer/2000 CASE tool as a corporate tool Oracle.

35. What is unique IP?

36. What is replicable IP?

The replicated system does not require modification by the developer, and the user must accept it as such. For example, a replicated information system (although it is not perceived as such) is Microsoft BackOffice. This system exists on its own and can solve certain corporate problems, but try to force Microsoft to change something in it! Corporate replicable systems also include 1C:Torgovlya and, to a lesser extent, Ekipazh. The higher we rise, the more flexibility we see - the system turns into a semi-custom one. I will no longer call “Galaktika” replicable. This requires setup and implementation steps.

In principle, replicable systems are intended for small businesses. But there is some critical scale of the enterprise, starting from which it is cheaper, more correct and faster to spend on the costs associated with updating the software than on the costs caused by the need to reorganize activities. Small companies are able to “adjust” their business processes to the requirements of replicated systems - their business processes are also short. Bigger ones can't do that.

- What then is the measure for you - what does “large enterprise” mean?

A large enterprise means hundreds of documents per month and more than five people in business process chains.

Usually they also call the number of jobs in the system, but this is nonsense, not a criterion. Because if I have four economists working for me, then the conditions for creating a corporate system are much more difficult than if there are hundreds of cash register cashiers.

37. What is a design system?

From a technological (architectural) point of view, the design system is a software product that: includes a core in which the fundamental model of the subject area is defined, as well as a basic set of classes (as abstract as possible) and basic methods for working with them; includes a configuration, which is an implementation of an information system built from kernel classes and methods; includes tools that allow the user to build their own configuration option

Management tasks in each organization are undoubtedly unique, but, as a rule, typical tasks can be identified for any specific type of activity. A detailed list of typical and specific tasks and their relationships can become a prototype of the technical specifications for the system.

When analyzing the functionality of the design system, it is advisable to divide all the required functions into a number of categories: a) functions already implemented in standard configurations of the design system; b) functions that are not implemented in standard configurations, but which can be implemented using configuration tools; c) functions that cannot be implemented (on our own) without a radical redesign of the system.

IS – transformer, implements basic functionality for data management, implementation of business logic and provision of a graphical user interface, but does not have an implemented business model for starting operation within any subject area.

38. What is IP adaptation?

39. What is adaptable IS?

Adaptable systems

The problem of adapting automated control system software, i.e. adapting it to working conditions at a particular enterprise, was recognized from the very beginning work on control automation.

The content and methods of adaptation have evolved along with the methodology for creating and implementing systems. The essence of the problem is that, ultimately, each automated control system is unique, but at the same time it also has common, typical properties. Any software subsystem displays both of these sides of the automated control system. In a technological sense adaptation automated control system software is a transition from a basic system that displays the typical properties of the system to a final solution adapted to work in a given automated control system.

Requirements for adaptation and the complexity of their implementation significantly depend on the problem area, the scale of the system, and the degree of correlation between the formalized and non-formalized when solving management problems.

Even the first programs that solved individual control problems were created taking into account the need to customize them By parameters. Since at an early stage there was an acute issue of providing computing power, the main attention was paid to setting up the needs for RAM, methods of stopping when solving optimization problems, and managing the program to bypass software modules not used in a specific calculation.

With the advent of standard solutions in the form of application software packages ( PPP) there was a need for special pre-generation procedures. The procedures covered parameters that determined the operating mode of the software, requirements for information support, conditions for connecting and using external programs. Application PPP as basic systems led to an increase in the formalized component in the enterprise management system. Has become more complicated adaptation systems to the conditions of the enterprise. Appeared divisions operation of software, dealing, among other things, with issues of adaptation of software systems. It became obvious that adaptation in automated control systems is not only a software and hardware problem, but also an organizational problem.

Interactive systems, which have made managers at all levels direct users of computer systems, have also led to a new understanding of the problem of adaptation. The underlying reasons were the same - a shift in the relationship between the formalized and the informal towards the formalization of the Management process. The main difficulty was that formalization affected not only standard, but also unique functionality in the enterprise management system.

Of everything sets The difficulties that emerged at this stage of development of automated control systems should be focused on two. The first is the organization of a friendly interface between the user and the computing environment. During the development of control systems, the arsenal of interface organization tools included menu various types, electronic boards and panels, diagrams such as Chernoff and Ishikawa diagrams, graphic arts and much more. The second difficulty was systemic in nature. The previous approach - setting up the system by consultants with virtually no participation from managers - has become impossible. It turned out that in many cases the organization of implementation turns out to be ineffective, in which future users first formulate the requirements for the system, taking into account the specifics of the enterprise in all details, and then consultants configure the system for the conditions of use. There are a number of reasons for this ineffectiveness. Firstly, as a rule, practicing managers do not master systems analysis methodologies. Secondly, volume information regarding the details of the organization of management at a particular enterprise turns out to be too large. Thirdly, this is not always information It also turns out to be useful for consultants due to its “one-time” nature. Fourthly, with such an organization it is difficult to implement the principle of new tasks; this would require additional iterations during the implementation process.

Therefore, methods for developing and implementing software were proposed, based on new principles:

· involving users in system development, including software development;

· software prototyping;

· combining the process of training users to work with the basic system for creating a software prototype.

An example is the approach proposed by the company Computer Associates in the early 90s for projects like MRPII/ ERP based on the CA system CAS.

Prototype BY Automatic control system can be used in the future in the following works:

· when training a wider range of personnel;

· during trial operation;

· when modified in order to obtain the final version of the software.

This approach made it possible, to a certain extent, to solve the problem of adapting the control system in dynamics, since the enterprise employees, during the creation of the prototype, acquired skills in working with the means of designing and modifying the system.

Further development of methods and means for adapting basic systems is aimed at achieving the following goals:

· increasing the level of automation of system design and implementation;

· ensuring continuous management of the configuration and parameters of the system at all stages of its life cycle;

· reducing the time required to make changes to the configuration and parameters of the system as the production process and management are modernized;

· combination of standard solutions, proven by practice, with solutions depending on the specific conditions of the enterprise.

An example of one of the many means of adapting basic systems is the Orgware methodology used by BAAN.

The development of automated control systems at an enterprise can be carried out both “from scratch” and on the basis of a reference model ( Reference Model).

The reference model is a description of the appearance of the system, functions, organizational structures and processes that are typical in some sense (industry, type of production, etc.). It reflects the typical features inherent in a certain class of enterprises. A number of companies producing adaptive automated control systems, together with large consulting firms, have been developing reference models for various industries for a number of years. There are similar models for enterprises in the automotive, aviation and other industries. Each model is standard design solution, on the basis of which specific projects can be built.

It should be noted that adaptations and reference models are included in many MRPII/ class systems. ERP, which can significantly reduce the time required for their implementation at the enterprise.

If the enterprise does not have a reference model at its disposal, then a model of its level must be created during the design process as an initial one. Based on the initial model, design, refinement and detailing control systems. Reference model at the beginning work on automation of enterprise management can be a description of the existing system and thus serve as a starting point from which work begins By improvement of the management system.

The system design process may involve several phases.

Results of the first phase: the boundaries of the future system and conceptual business model, which reflects in an enlarged form the functional structure of the management system and the combination of management functions for various types of orders passing through the system.

During the second phase, a reference document is created and documented in the repository business model. Typically, a reference model includes the following components:

· hierarchy of business functions, which is a descending hierarchical structure that describes in an enlarged form the functional structure of the future system. At the same time, it is possible to specify several implementation options for the lower elements of the structure;

· business process models. These are deeper models that show how functions should be implemented. Outwardly, they resemble traditional flowcharts and describe a sequence of elementary actions that can be performed by the system, other applications, manual actions, and deeper-level business processes;

· a model of organizational structure, which describes the structure of the organization, the relationships between departments and people, and the roles assigned to managers.

In the next phase, a design model of the enterprise is created ( Project Model), which is the development and clarification of the functional structure for a specific enterprise. It can be created bypassing the reference model, but this approach is not effective for complex projects.

The final phase is linking the project model to the roles specified by the detailed model of the organizational structure, to the system functions and technical means. As a result, a comprehensive configuration software and organizational support, technical means.

40. What are the different ways to acquire IP?

purchase of ready-made IP;

purchase and modification of IP;

IP outsourcing.

41. What are the advantages and disadvantages of buying IP?

The method of acquiring IP is a sequence of actions from identifying and formalizing the needs for an information system until the IP is implemented in the enterprise.

Classification of methods for acquiring IP:

purchase of ready-made IP;

IP development (independent or custom);

purchase and modification of IP;

IP outsourcing.

The advantages of purchasing ready-made IP are: development time equal to zero; the system has been replicated (availability of documentation)

The disadvantages of purchasing ready-made IP are: the system has been replicated (Issues of information security); adaptation to the requirements is necessary.

Disadvantages of developing IP by a specialized company are: long development time

The disadvantages of independent IS development are: long development time; lack of proper qualifications of developers; the need to create an IT department.

The advantages of independent IS development are: the system is unique; good adaptation to requirements

The advantages of developing IP by a specialized company are: the system is unique; good adaptation to the requirements; Availability of appropriate qualifications of developers.

IS outsourcing is: ordering an information system by a consumer company from an IS manufacturer; leasing of IP by the manufacturing company to the IP consumer company; performance by a third party of processing information for a consumer company.

: the ability to focus the company’s attention on its core business; the ability to respond flexibly to changes in the market and within the company; no need to expand the company's staff; reduction of operating costs.

Disadvantages of IP outsourcing are: the possibility of losing a supplier (reliability)

42. What are the advantages and disadvantages of developing an IP by a company-

IS developer?

Advantages:

The project is carried out by a highly qualified team of professionals;

Complete documentation of the project;

They are developed taking into account the specifics of a particular enterprise, the requirements and wishes of the enterprise specialists who will use this IS;

Non-standard, exotic functions that will never appear in boxed systems can be implemented;

It happens that an enterprise has another IP that the customer does not want to change (or even several). In this case, means of integrating these systems into one can be ordered in order to preserve business processes and accumulated data;

There is no unnecessary functionality. The interface is not overloaded and working with such a system is usually easier;

Custom systems are more productive than universal ones and place lower demands on equipment;

A custom system can be developed by the developer in the direction required by the customer;

Development, configuration and maintenance are in the hands of professionals, which increases the stability of the system;

Flaws:

Often the highest cost;

Often takes the longest time;

The presence of the development period itself;

There is no opportunity to get acquainted with the system in advance, to “touch it with your hands”;

43. What are the advantages and disadvantages of IP development

on your own?

Advantages:

Good adaptation to the requirements;

The product is not replicable (individual);

Ability to quickly change functionality

Flaws:

Development will never end;

Poor adaptability;

It is necessary to create a team, or remove current IT department employees from work;

The wishes of management are often taken into account at the expense of the quality of development;

The project may choke:

– due to lack of qualifications of internal specialists;

– due to the departure of leading specialists;

– due to lack of internal resources;

Often the system is poorly documented;

44. What are the advantages and disadvantages of purchasing and modifying

Buying IP:

When purchasing IP you must:

Evaluate the software product itself (functionality and other properties);

Assess the enabling technology and platform;

Evaluate the quality of service (HotLine, ambulance, new versions, training, etc.);

Evaluate the supplier company;

Advantages:

Development time is zero;

The system has been replicated;

There is often a choice of several ready-made systems;

In addition to IP, you also buy business processes;

Flaws:

Adaptation to the organizational structure, functional requirements, etc. is necessary;

The system is replicable: issues of protection, novelty, etc. raise certain concerns;

High risk;

The needs of employees for the functionality of the system will most likely not be fully satisfied;

Purchase and modification of IP:

In this case, the core of the system is purchased (for example, in accounting, these are postings), and the rest is completed.

Advantages:

The purchased kernel is a debugged and complete component;

Possible modification of exactly the required functionality;

There is no need to pay for what the company does not need;

Flaws:

An information technology department is required;

The scheme is effective if the amount of finishing work is relatively small;

Often it is possible to develop only within the framework of the kernel information model;

45. What are the advantages and disadvantages of custom, unique and

replicated information systems?

IP systems are divided into individual and replicable systems, as well as independent and custom developments.

The main arguments for and against these options are given in
table.

Self-development

"+" Full compliance with the current requirements of the organization

Availability of previous developments

"-" High development cost (especially compared to the cost of “boxed” products)

Problems arising due to system modification

Ready-made (replicated) system (adapted)

"+" Support and update versions

Compliance with Russian and international standards

"-" High cost of ready-made systems (middle and especially high class)

Dependence on the developer company

Disadvantages of foreign IP

46. ​​What are the advantages and disadvantages of domestic and

foreign information systems?

Domestic or foreign replicable system.

There are two polar opinions:

1) no matter how much the domestic system costs, it is preferable to an imported one, the implementation of which is incomparably more expensive. In addition, domestic systems are better adapted to the conditions of Russian business;

2) the only systems that allow you to fully automate all aspects of enterprise management are foreign systems such as ERP. Therefore, despite their higher cost, enterprises should choose ERP systems, the viability of which is confirmed by international experience.

The advantages of foreign IP are: high quality; great functionality; high reliability

The advantages of domestic IP are: adaptability to Russian conditions.

Disadvantages of foreign IP are: the need to adapt to Russian conditions

Disadvantages of domestic IP are: not high enough quality; not enough functionality; not high enough reliability

47. What are the advantages and disadvantages of outsourcing?

IP outsourcing- This:

ordering an information system by a consumer company from an IP manufacturer;

leasing of IP by the manufacturing company to the IP consumer company;

performance by a third party of processing information for a consumer company.

Outsourcing goals

Reducing costs (however, this is more relevant for foreign countries, where the hourly wage rate is much higher than in Russia);

If it is necessary to sharply reduce the work period (with a high workload of IT specialists);

In the event that it is impossible to complete the task with the help of our employees;

Functions and tasks of outsourcing

Development and implementation of large information systems;

Consulting services (conducting tenders, searching for partners, expert assessments, assistance in development strategy, preparation of regulations, IT audit, etc.);

Maintenance and repair of computer and server equipment;

Telecommunications services;

Local network support;

Maintenance of telephone and office equipment;

Development of information security;

Support of expensive business processes from an IT point of view (processing, issuance of plastic cards);

The benefits of IP outsourcing are:

the ability to focus the company's attention on its core business;

the ability to respond flexibly to changes in the market and within the company;

no need to expand the company's staff;

reduction of operating costs.

Disadvantages of IP outsourcing are:

possibility of losing a supplier (reliability)

48. What components does the IP acquisition price include?

Information system acquisition price includes the cost of the software product, the cost of the DBMS, the cost of the operating system

cost of services: estimating the cost of related professional services and then the relationship between the cost of licenses and the cost of those services.

The cost of installation depends on the degree to which the solution is customized in accordance with the individual requirements of the customer; who conducts it and how it is assessed.

Hardware cost: necessary additional devices and equipment (servers, storage devices, network equipment, etc.).

The cost of updating versions and technical support: what percentage of the license cost is the cost of updating and technical support during the year; possible discounts

There are the following types (sectors) of software:

Universal – created for mass sale to numerous users;

Specialized – designed for a specific group of users;

Unique - developed to order to solve a specific problem.

The type of software determines the ratio of material and intellectual components and explicit and implicit costs in TCO.

Cost of IP and hardware upon purchase - modern licensing policies. The Software is protected against unauthorized copying by copyright laws. Copyright laws provide for the author (publisher) of software to retain several exclusive rights, the most important of which is the right to make copies of the software.

Purchasing a software product is acquiring a license (right) to use it. Software on a computer is "in use" when it is stored in permanent memory (usually on a hard drive, but possibly on a CD-ROM or other storage device) or loaded into random access memory (RAM).

The acquisition price never exhausts all the costs associated with the use of information resources, and in some cases it may even turn out to be a minor item.

49. What are the components of the total cost of IP ownership?

Total Cost of Ownership(TCO - Total Cost of Ownership) of an information system is: the sum of direct and indirect costs borne by the owner of the IP during its life cycle

Decision support systems(DSS) are computer systems, almost always interactive, designed to assist a manager (or executive) in decision making. DSSs include both data and models to help the decision maker solve problems, especially those that are poorly formalized. Data is often retrieved from a conversational query system or database. The model can be a simple profit-and-loss type to calculate profit under certain assumptions, or a complex optimization model to calculate the load for each machine on the shop floor. 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 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 conversation management to facilitate user access to the DSS. The user interacts with the DSS through a user interface, selecting a particular model and dataset to use, and the DSS then presents the results to the user through the same user interface. The control model and data management operate largely behind the scenes and range from a relatively simple generic spreadsheet model to a complex, complex planning model based on mathematical programming.

Rice. 1.4. Components of a decision support system

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 forecast various elements of an organization or financial condition. The data used is the organization's previous financial statements. The initial model includes various assumptions about future trends in the categories of expenditure and income. After reviewing the results of the baseline model, the manager conducts a series of “what-if” studies, changing one or more assumptions to determine their impact on the baseline. For example, a manager might probe the impact on profitability if sales of a new product grew by 10% annually. Or the manager could explore the impact of a larger than expected increase in the price of raw materials, such as 7% instead of 4% annually. This type of financial statement generator is a simple yet powerful DSS to guide financial decision making.

An example of DSS for calculating data transactions is the system used to determine the amount of appropriations for police tours used by cities in California. This system allows the police officer to see a map and displays geographic area data, showing the police call volumes, call types and call times. The system's interactive graphics capability allows the officer to manipulate the map, area, and data to quickly and easily suggest variations in police call alternatives.

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

All examples of DSS given are called specific DSS. They are actual applications that help 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. DSS Generator is a software package designed to be executed on a partially computer basis. In our example of a financial statement, Microsoft Excel or Lotus 1-2-3 can be considered as DSS generators, while models for designing financial statements for a private branch of a company based on Excel or Lotus 1-2-3 are specific DSS.

DSS are discussed in more detail in Section. 2.2.