Notes On Mis Prepared By:

Notes on MIS Prepared By:

Sinhgad Institute of Management and Computer Application

 Explain how quality of information is decided? (Minal Javale)  Differentiate between data, information and knowledge giving suitable examples. Indicate how systems related to each of these can be developed. (Naveen Jain) “Information is data that has been processed into a form that is meaningful to the recipient and is of real or perceived value in current or prospective actions or decisions.” This definition recognizes both the value of information in a specific decision and the value of information in motivation, model building and background building affecting future decisions and actions. The relation of data and information is that of raw material to finished products. In other words, an information processing system processes data into information. Information resources are reusable. When information is retrieved and used, it doesn’t lose value, in fact, it may gain value through the credibility added by use. This characteristic of stored data makes it different from other resources. Quality of information: The quality of information is determined by how it motivates human action and contributes to effective decision making. Utility of information: There are four utilities of information that are identified: 1) Form Utility: As the form of information more closely matches the requirement of the decision maker, its value increases. 2) Time Utility: Information has greater value to the decision maker if it is available when needed. 3) Place Utility (Physical accessibility): Information has greater value if it can be accessed or delivered easily. Online systems maximize both time and place utility. 4) Possession Utility (Organizational location): The possession of information strongly affects its value by controlling its dissemination to others.

Data, Information, Knowledge, and Wisdom There is probably no segment of activity in the world attracting as much attention at present as that of knowledge management. What follows is the current level of understanding regarding data, information, knowledge, and wisdom. According to Russell Ackoff, a systems theorist and professor of organizational change: 1. Data: symbols 2. Information: data that are processed to be useful; provides answers to "who", "what", "where", and "when" questions 3. Knowledge: application of data and information; answers "how" questions 4. Understanding: appreciation of "why" 5. Wisdom: evaluated understanding. A further elaboration of these terms are as follows: Data...Data, the raw material for information is defined as groups of nonrandom symbols which represent quantities, actions, objects, etc. Data items in information systems are formed from characters. These may be alphabetic, numeric, or special symbols such as *, $. Data items are organized for processing purposes into data structures, file structure and databases. Data relevant to information processing, and decision making may also be in form of text, images or voice. In computer parlance, a spreadsheet generally starts out by holding data. Information... information is data that has been given meaning by way of relational connection. This "meaning" can be useful, but does not have to be. In computer parlance, a relational database makes information from the data stored within it. Knowledge... knowledge is the appropriate collection of information, such that it's intent is to be useful. Knowledge is a deterministic process. When someone "memorizes" information (as less-aspiring test-bound students often do), then they have amassed knowledge. This knowledge has useful meaning to them, but it does not provide for, in and of itself, an integration

such as would infer further knowledge. For example, elementary school children memorize, or amass knowledge of, the "times table". They can tell you that "2 x 2 = 4" because they have amassed that knowledge (it being included in the times table). But when asked what is "1267 x 300", they can not respond correctly because that entry is not in their times table. To correctly answer such a question requires a true cognitive and analytical ability that is only encompassed in the next level... understanding. In computer parlance, most of the applications we use (modeling, simulation, etc.) exercise some type of stored knowledge.

Data represents a fact or statement of event without relation to other things. Ex: It is raining. Information embodies the understanding of a relationship of some sort, possibly cause and effect. Ex: The temperature dropped 15 degrees and then it started raining. Knowledge represents a pattern that connects and generally provides a high level of predictability as to what is described or what will happen next. Ex: If the humidity is very high and the temperature drops substantially the atmospheres is often unlikely to be able to hold the moisture so it rains. Value of information in decision making: Decision theory provides approaches for making decisions under certainty, risk and uncertainty. Decision making under certainty assumes perfect information as to outcomes; risk assumes information as to the probability of each outcome but not which outcome will occur in any given case; and uncertainty assumes knowledge of possible outcomes but no information as

to probabilities. A value of information can be computed for decisions which fit these frameworks for analysis. In decision theory, the value of information is the value of change in behavior caused by information less the cost of obtaining information. In other words, giving a set of possible decisions, a decision maker will select one on the information at hand. If new information causes a different decision to be made, the value of the new information is the difference in value between the outcome of the old decision and that of the new decision, less the cost of obtaining new information. If the new information doesn’t cause a different decision to be made, the value of new information is zero. Value of information and sensitivity analysis: Sensitivity analysis consists of analytical procedures to determine the degree of impact on a solution algorithm or model of changes in one or more variables. The following questions illustrate the reasons for sensitivity analysis: 1) What is the effect on profit of a 10% increase in sales or a 10% decrease in sales (from the best estimates). 2) What is the effect on rate of return from extending the useful life to 12yrs instead of 10yrs. 3) Will the project still be justified if cost increased by 10%. Sensitivity analysis can be used to determine the changes in factors such as estimated costs, revenues, and obsolescence that are large enough to cause a change in decision. In doing so, it identifies variables for which more information will be valuable.

Value of information other than in decision making: Some other reasons for value of information are motivation, model building, and background building. Motivation: Some information is motivational; it provides the persons receiving the information with a report on how well they are doing. This

feedback information may motivate decisions, but its connection is often indirect. Model Building: The management and operation of an enterprise function with models of enterprise within the minds of managers and operations personnel. The models may be simple or complex, correct or incorrect, etc. Information that is received by these individuals may result in change or reinforcement in their models. This process is a form of organizational learning and expertise building. Background Building: In decision theory, the value of information is the value of change in decision behavior, but the information has value only to those who have the background knowledge to use it in a decision. The most qualified person generally uses information most effectively but may need less information since experience has already reduced uncertainty when compared with the less experienced decision maker. Types of information: The information can be classified in a number of ways for better understanding. Some classifications are as follows: i.

ii.

iii.

Action vs. No Action information: The information which induces action is called action information. The information which communicates only the status of a situation is no-action information. Recurring vs. non recurring information: The information generated at regular intervals is recurring information. The monthly sales reports, the stock statements, the trial balance, etc are recurring information. The financial analysis on the report on the market research study is non recurring information. Internal vs. external information: The information generated through the internal sources of the organization is termed as an internal information, while the information generated through the government reports, the industry surveys, etc is termed as an external information, as the sources of the data are outside the organization.

The action information, the recurring information and the internal information are the prime areas for computerization and they contribute qualitatively for the MIS. The timing and accuracy of the action information is usually important. The mix of the internal and the external information changes, depending on the level of management decision. At the top management level, the stress is more on the external information and at the operation and the middle level management, the stress is more on the internal information. External

Low

Source of information

structural information Middle Management Operational Management

Internal

High

Information can also be classified in terms of its application: 1. Planning Information: Certain standards, norms and specifications are used in the planning of any activity. Hence, such information is called the planning information. The time standards, the operational standards, the design standards are examples of the planning information. 2. Control Information: Reporting the status of an activity through a feedback mechanism is called the control information. When such information shows deviation from the goal or the objective, it will induce a decision or an action leading to control. 3. Knowledge information: A collection of information through library reports and the research studies to build up knowledge base as information source for decision making is known as knowledge

information. Such a collection is not directly connected to decision making but the need of knowledge is perceived as a power or strength of the organization. The information can also be classified based on its usage. When the information is used by everybody in the organization, it is called the organization information. When then information has a multiple use and application, it is called the database information. When information is used in the organization in operation of business, it is called functional or operational information. 4. Formal and informal information: Formal information is the information which is provided to the users through authentic channels. It carries some value for the user. Informal information is also referred to as grapevine. It is unorganized information and may or may not carry value for the user.

Q. Describe types of information required to different functional level of management. (Bhushan) A. Breakup of Information Needs according to Decisions and User Different kinds of decisions are made at different levels in the organization. The decision-makers at each level of the organization can be said to be the users of the MIS at that level. Since different users make different decisions at each level in the organization, their individual information needs will be different. Top management is concerned with the achievement of goals, anticipation of changes in the external environment and readiness of the organization to meet the challenges of the environment. Therefore, the top management will need information about changes in the environment. It will need information about business policy of the government, actions of rivals, general social and economic climate, etc. The Information Needs of top management therefore can be said to be long term and more external than internal. However, top management will also need information on the existing state within the organization. Profitability trends, state of quality consciousness, etc. will have to be taken into account by top

management. Their information needs will thus be for this kind of information. Middle management is concerned with the achieving of interim objectives. It has the task of translating management goals into achievable objectives. Therefore it will need information on the performance of various functional areas, achievement of previous objectives, etc. Thus, the information needs of middle management can be said to be more internal than external to the organization and on a time-scale that is smaller than that required by top management. Operational management is concerned with the day-to-day working of the organization. Thus, it will require information that is more immediate. Operational management is often called upon to make snap decisions about various operations that are being carried out within the organization. Therefore, it needs a quicker “turn-around time” in the information that is being supplied to it. Thus the Information Needs vary according to the users or the decision-makers in the organization. Each set of users has a different set of information needs and it would be unwise to use a common model for all levels of users. The unique information needs of different users must be taken into account when enumerating the total information needs of the organization.

External

Low

Top Source of Information

Structured Information Middle Management

Internal

Operational Management

High

Fig. Types of Information required to different functional level of Management.

Relation between Information Needs and Type of Decision At the very bottom of the decision-making step are the operational managers. Operational managers, make decisions related to the day-to-day functioning of the organization. Thus, they operate in an atmosphere of near total certainty. They have full knowledge of the processes, the functioning of individuals and the day-to-day targets to be achieved. Since they make decisions in this atmosphere of certainty, the amount of information they require is basic and minimal. Their information needs are restricted to the output of men and machines within their span of control. Middle management makes decisions in an atmosphere of lesser certainty (greater uncertainty). Middle management are concerned with decisions whose outcome may be uncertain or cannot be predicted with one hundred percent accuracy. Therefore, they need a larger amount of information and the information they receive must be from a greater number of sources. Middle management will be less concerned with the output of a particular machine and more concerned with the performance of a particular department. The information required by middle management must be related to a greater span of time. Middle management is concerned with the objectives in addition to targets, and therefore middle managers need information that will enable them to determine if the organization is moving towards the achievement of its objectives. Top management is active in an atmosphere of almost total uncertainty. At this level, it is difficult to determine the actual information requirements. In such situations, it may be necessary to provide top management with the resources to call upon specific items of information as and when required, rather than to try and ‘dump’ all seemingly relevant information on the manager’s table. Thus, the top level management needs more of tools to query the information system on the basis of ‘available when required’ rather than ‘it’s there somewhere’. The information system must be designed in such a way that it is able to process

and format the information on an instant basis, depending on the specific requirement of top management at that particular point in time.

Que. Sort note on- (Sumit Joshi) 1. Value of information. 2. Intelligence-Design-Choice Model. 3. Types of Decisions. 4. Low of Requisite Variety. Ans: 1. Value of information:Value of information in decision making:In decision theory, the value of information is the value of change in behaviour caused by information less the cost of obtained information. In other words, given a set of possible decisions, a decision maker will select one on the information at hand. If new information causes a different decision to be made, the value of the new information is different in value between the outcome of the old decision and that of the new decision, less the cost of obtaining the new information. If the new information does not cause a different decision to be made, the value of new information is zero. Value of information other than of decision making:Some other reasons for value of information are motivation, model building and background building. Motivation:- It provides the person receiving the information with a report on how well they are doing. Model building:- The information received by individuals may result inching or reinforcement in there models. This process is a form of organizational learning and expertise building. Background building:- The information has value only to those who have background knowledge to use it in a decision. The most qualified person generally uses information most effectively. 2. Intelligence-Design-Choice Model:How are the decision made? The

answer affect the decision of computer based information system to support the decision making process. A well known model proposed by Herbert- Simon is used as the bases for describing decision making process. The model consist three phases-

Phases of decision making process Intelligence

Design Choice

Explanation

Searching the environment for conditions calling for decisions. Data inputs are obtained, process and examined for clues that may identify problems and opportunities. Inventing, developing and analyzing possible courses of action. This involves processes to understand a problem, to generate solves and to test solution for feasibility. Selecting of action from those available. A choice is made and

implement. Flow of activity from intelligence to design to choice, but at any phase there may be a return to the previous phase. 3.

Types of Decisions:Decisions can be classified as programmed and non programmed on the bases of the ability of the organization or individual to preplan the process of making the decision. Programmed decisions are those decisions that can be prespacified by a set of rules or decision procedures. Programmed decisions are reflected in rule books, decision tables and regulations. Programmed decisions imply decision making under certainty because all outcomes must be known. Non programmed decisions have no pre-established decision rules or procedures. Non programmed decisions may range from one time decisions relating to a crisis to decision relating to recurring problems where conditions change so much that decision rule can not be formulated. Programmed decisions can be delegated to lower levels. In an organization or automated; Non-program decisions generally can not. One strategy for increasing the no of decisions which can be programmed is to specified rules for all normal conditions and let the programmed decision rules handle these normal cases. When conditions or actions do not fit the decision rules, the decision is considered non-programmed and is passed to a higher level of decision making.

4.

Low of Requisite Variety:Low of requisite variety means that for a system to be controlled, every controller (human or machine) must be provided with1. Enough control responses (what to do in each case) to cover all possible conditions the system may face. 2. The decision rules for generating all possible control responses or 3. The authority to become a self organization system in order to generate control responses. Enumerating all responses is possible in simple cases. In complex systems providing control responses is very difficult.

Executive information system (Ravi) An Executive Information System (EIS) is a computer-based system intended to facilitate and support the information and decision making needs of senior executives by providing easy access to both internal and external information relevant to meeting the strategic goals of the organization. It is commonly considered as a specialized form of Decision Support System (DSS). The emphasis of EIS is on graphical displays and easy-to-use user interfaces. They offer strong reporting and drill-down capabilities. In general, EIS are enterprise-wide DSS that help top-level executives analyze, compare, and highlight trends in important variables so that they can monitor performance and identify opportunities and problems. EIS and data warehousing technologies are converging in the marketplace. History of EIS Traditionally, executive information systems were developed as mainframe computer-based programs. The purpose was to package a company’s data and to provide sales performance or market research statistics for decision makers, as such financial officers, marketing directors, and chief executive officers, who were not well acquainted with computers. The objective was to develop computer applications that would highlight information to satisfy senior executives’ needs. Typically, an EIS provides the data that would only need to support executive level decisions instead of the data for all the company. Today, the application of EIS is not only used in typical corporation hierarchies, but also installed at the personal computer levels or workstation levels on a local area network. EIS now cross computer hardware platforms and integrate information stored on mainframes, personal computer systems, and minicomputers. As some client service companies adopt the latest enterprise information systems, employees can now use their personal computers to get access to the company’s data and decide which data are relevant for their decision makings. This arrangement makes all users capable to customize their access to the proper company’s data and provide relevant information to both upper and lower levels in companies

EIS Components The components of an EIS can typically be classified into the following categories: Hardware When talking about hardware for an EIS environment, we should focus on the hardware that meet executive’s needs. The executive must be put the first and the executive’s needs must be defined before the hardware can be selected. The basic computer hardware needed for a typical EIS includes four components: (1) Input data-entry devices. These devices allow the executive to enter, verify, and update data immediately; (2) The central processing unit (CPU), which is the kernel because it controls the other computer system components; (3) Data storage files. The executive can use this part to save useful business information, and this part also help the executive to search historical business information easily; (4) Output devices, which provide a visual or permanent record for the executive to save or read. This device refers to the visual output device or printer. In addition, with the advent of local area networks (LAN), several EIS products for networked workstations became available. These systems require less support and less expensive computer hardware. They also increase access of the EIS information to many more users within a company. Software Choosing the appropriate software is vital to design an effective EIS. Therefore, the software components and how they integrate the data into one system are very important. The basic software needed for a typical EIS includes four components: (1) Text base software. The most common form of text is probably the word processing document; (2) Database. Heterogeneous databases residing on a range of vendor-specific and open computer platforms helps executives access to both company internal and external data; (3) Graphic base. Graphics can turn volumes of text and statistics into visual information for executives. Typical graphic types are: time series charts, scatter diagrams, maps, motion graphics, sequence charts, and comparison-oriented graphs (i.e., bar charts); (4) Model base. The EIS models contain routine and special statistical, financial, and other quantitative analysis. Now perhaps the more difficult problem to those executives is how to choose EIS software rather than how to use them, because the latest EIS software packages are more intelligible to

nontechnicians, self-documenting, and more flexible. Therefore, when we evaluate EIS software, we should think about if the package is easy to use, if the package responds readily to the executive’s requests, and if the package is reasonably priced. Furthermore, we need consider if the package can run on the current hardware we have. Interface An EIS needs to be efficient to retrieve relevant data for decision makers, so the interface is very important. Several types of interfaces can be available to the EIS structure, such as scheduled reports, questions/answers, menu driven, command language, natural language, and input/output. It is crucial that the interface must fit the decision maker’s decision-making style. If the executive is not comfortable with the information questions/answers style, the EIS will not be fully utilized. The ideal interface for an EIS would be simple to use and highly flexible, providing consistent performance, reflecting the executive’s world, and containing help information and error messages. Telecommunications As decentralizing becoming the current trend in companies, telecommunications will play a pivotal role in networked information systems. Transmitting data from one place to another has become crucial for establishing a reliable network. In addition, telecommunications within an EIS can accelerate the need for access to distributed data. EIS Applications EIS enables executives to find those data according to user-defined criteria and promote information-based insight and understanding. Unlike a traditional management information system presentation, EIS can distinguish between vital and seldom-used data, and track different key critical activities for executives, both which are helpful in evaluate if the company is meeting its corporate objectives. After realizing its advantages, people have applied EIS in many areas, especially, in manufacturing, marketing, and finance areas. Manufacturing Basically, manufacturing is the transformation of raw materials into finished goods for sale, or intermediate processes involving the production or finishing of semi-manufactures. It is a large branch of industry and of secondary production. Manufacturing operational control focuses on day-today operations, and the central idea of this process is effectiveness and

efficiency. To produce meaningful managerial and operational information for controlling manufacturing operations, the executive has to make changes in the decision processes. EIS provides the evaluation of vendors and buyers, the evaluation of purchased materials and parts, and analysis of critical purchasing areas. Therefore, the executive can oversee and review purchasing operations effectively with EIS. In addition, because production planning and control depends heavily on the plant’s data base and its communications with all manufacturing work centers, EIS also provides an approach to improve production planning and control. Following are some real-world EIS applications related to manufacturing. Marketing In an organization, marketing executives’ role is to create the future. Their main duty is managing available marketing resources to create a more effective future. For this, they need make judgment s about risk and uncertainty of a project and its impact on company in short term and long term. To assist marketing executives in making effective marketing decisions, an EIS can be applied. EIS provides an approach to sales forecasting, which can allow the market executive to compare sales forecast with past sales. EIS also offers an approach to product price, which is found in venture analysis. The market executive can evaluate pricing as related to competition along with the relationship of product quality with price charged. In summary, EIS software package enables marketing executives to manipulate the data by looking for trends, performing audits of the sales data, and calculating totals, averages, changes, variances, or ratios. All of these sales analysis functions help marketing executives to make final decisions. Following are some real-world EIS applications related to marketing. Financial A financial analysis is one of the most important steps to companies today. The executive needs to use financial ratios and cash flow analysis to estimate the trends and make capital investment decisions. An EIS is a responsibility-oriented approach that integrated planning or budgeting with control of performance reporting, and it can be extremely helpful to finance executives. Basically, EIS focuses on accountability of financial performance and it recognizes the importance of cost standards and flexible budgeting in developing the quality of information provided for all executive levels. EIS enables executives to focus more on the long-term basis of current year and beyond, which means that the executive not only can

manage a sufficient flow to maintain current operations but also can figure out how to expand operations that are contemplated over the coming years. Also, the combination of EIS and EDI environment can help cash managers to review the company’s financial structure so that the best method of financing for an accepted capital project can be concluded. In addition, the EIS is a good tool to help the executive to review financial ratios, highlight financial trends and analyze a company’s performance and its competitors. Following are some real-world EIS applications related to finance. Advantages and Disadvantages of EIS Advantages Easy for upper-level executives to use, extensive computer experience is not required in operations Provides timely delivery of company summary information Information that is provided is better understanding Filters data for management Improves to tracking information Disadvantages Functions are limited, can not perform complex calculations Hard to quantify benefits and to justify implementation of an EIS Executives may encounter overloaded information System may become slow, large, and hard to manage Difficult to keep current data May lead to less reliable and secure data Small companies may encounter excessive costs for implementation Future Trends in EIS The future of executive info systems will not be bound by mainframe computer systems. This trend allows executives escaping from learning different computer operating systems and substantially decreases the implementation costs for companies. Because utilizing existing software applications lies in this trend, executives will also eliminate the need to learn a new or special language for the EIS package. Future executive information systems will not only provide a system that supports senior executives, but also contain the information needs for middle managers. The future executive information systems will become diverse because of integrating potential new applications and technology into the systems, such as

incorporating artificial intelligence (AI) and integrating multimedia characteristics and ISDN technology into an EIS.

Q) Open systems and closed systems. (Vamshi Priya) Open systems are computer systems that provide either interoperability, portability, or freedom from proprietary standards, depending on user's perspective. It can also be defined as a system that allows access by other systems, hence 'open' system. Systems that do not interact with their environment are called closed systems .The closed systems neither takes any input from the environment nor gives any output to the environment. It does not react to any threat from the environment, nor does it pose any threat to another system in the environment. As can be imagined ,such systems are extreamly rare. An open system is one that interacts with its environment. It accepts input from the environment and gives output to the environment. It racts to changes in the environment and is itself responsible for changes in the environment, no matter how small such changes may be. It has a selfregulatory mechanism by which it senses in the envirionment and reacts to such changes. Q) Feedback. In computer system, feedback is essential for determining the accuracy of data sent to various subsystems. In human beings, a similar feedback system is necessary. Howevere, the reasons for this are different. In human information, processing system, feedback is also essential to satisfy the psychological need to confirm if ddata has indeed deen entered into the system. For example if the data entry operator is unable to see data endteredon the screen or if there is no other feed back such as deep or beel to signal that input has been received, the operater may enter the data again to ensure that it has indeed been entered. Such a lack of deed back also

increases irritation and frustration, increasing the changesof error in data entry. Related to the issue of deedback is response time. Response time teken by the system to responde to users request, examples are . The time taken for a programme to begin after the comman has been given . The time taken for the systewm to display data has it is being entered . The time taken by a system to return the results of a quarry etc. Response time should neither be too fast nopr too slow. If the response time is too slow, the user may get restless due to increase stress level may occur. Another instresting aspect of the human information processing system i9s that the respose time is highly relative factor. If the system take, say 10 seconds to responde the user may feel this is a long time. I there is no visible activity on the screen. If on the other hand the system periodically display measages stating which file is open or what is being done. To the data at the movement, the response time may seem faster to the user. This is the reason modern computer system have “ status bars” and visual indicators of the progress of the information processing.

Qu: Discus characteristics of human Information processing also discus Newell Siman model.(Pravin Gawde) Ans: Allen Newell & Herbert Siman proposed model of human problem solving which makes use of analogy between computer processing and human information processing this is not to say that human solve problems like computer but analogy is very important in understanding human information processing. Human information processing system:-

The human information system consists of a processor, sensory input, motor output and three different memories: long term memory (LTM) , short term memory (STM) and external memory. The system operates in a serial fashion rather than in parallel. This means that the human can perform only one information processing at a time, where as a computer may operate in either serial or parallel design. The fact that human is a serial processor does not mean that he or she can not work on more than one task concurrently. Although this is not described by Newell Siman model, the human probably does it rapidly by switching from one task to another with short burst of processing each other this is analogous to time sharing in which a computer works on several programs at once by switching from one to another. Also, the human processor utilizes pattern matching which is not explained by the computer analogy. The long term memory has essentially unlimited capacity. It contains symbols and structures of chunks. The chunk is the unit of stored information - it can be a digit, word, an image, etc. Storage may be quite compact so that entire configuration of stimuli may be design by a single symbol. It requires only a few hundred milliseconds from long term memory but the right time is fairly long. The short term memory is a part of the processor and is quite small. It holds only 5 to 7 symbols. However, only about 2 can be retained while another task is being performed, which suggest that part of the short term memory is used for input and output processing read and write time are very fast. The external memory in the human processing system consist of external media such as a pad of a paper or chalkboard. The access time for the eye to locate the symbol at a known location is quite fast. The write times are much less than the write times for long term memory, which accounts for the efficiency of using external memory in problem solving procedures. It also eases the constraints of short term memory. Limits of human information system:- The Newell Simon Model suggest that are limitations on the ability of human as information process. One set of limits concerns the processing of data and is directly related to short term memory, another set of limits is the ability of humans to detect differences. Humans are also related to there ability to generate, integrate and interpret probabilistic data. Human Information Processing Strategies :Human adopts the strategies for dealing with there limitations as information processors for easing the strain of integrating information. Two examples are correctness and also anchoring and adjustment.

The concept of correctness is that the decision maker tends to use only information that is readily available and only in the form in which it is displayed. There will be a tendency not search for a data stored in a memory or to transfer or manipulate the data that is present. Explicit, available information thus has an advantage over data that must be obtained or manipulated before use. In particular, individuals relies on concrete observable characteristics of evidence and neglect other information related to the process or context of evidence, leading to possible errors in judgment. The idea of anchoring and adjustment is that humans tends to make adjustments establishing an anchor point and making adjustments from this point. The anchoring and adjustment behavior reduces information processing requirements. It is a common phenomenon in budgeting, planning and pricing. The anchoring and the adjustment process can have negative effect on judgments in two ways. First, individual may use inappropriate criteria for choosing an anchor. One common criteria is past experience and due to phenomenon of give response interaction already described, they may tend to use a scale or a unit of measure which is the same as the value to be judge. Second, when a value is compared to the anchor, the adjustment process tends to undervalue the importance of the new evidence. Since it is only considered relative to some what arbitrary anchor point.

Q)How does MIS differs from a) Managerial Accounting b) Computer Science (Priya Doshi)  a) Managerial Accounting: The field of accounting has two major areas: financial and managerial accounting. Financial accounting is concerned with the measurement of income for specific periods of time such as a month or a year and the reporting of financial status at the end of the period. These reports are investor oriented. As a result, financial accounting has limited usefulness for managerial decision making.

Managerial accounting is concerned with determining relevant costs and performing other analysis useful for managerial control and managerial decisions. It tends to be the focus for the preparation of budgets and performance analysis based on budgets. Historically the accounting department was always responsible for data processing because the first applications were related to accounting functions. The MIS concept includes much of the content of managerial accounting; however, the support systems which provide users with access to data and models are beyond the scope of traditional managerial accounting. Current organizational practice is usually to retain cost and budget analysis within the managerial accounting function, and to have the MIS function provide data and model support. b) Computer Science: Computer science is important to management information systems because it covers topics such as algorithms, computation, software, and data structures. However, the academic field of management information system is not an extension of computer science; rather it is an extension of management and organizational theory. The fundamental processes of management information system are more related to organizational processes and organizational effectiveness than computational algorithms. The emphasis in MIS is on the application of the technical capabilities computer science has made possible.

Que: What is decision making? Explain the Simon’s model for decision making. (Pravin Yadav) Ans: The systems approach is ideal to study the decision making. It is important to study how decision are made, what inputs are necessary for decision making and what impedes the decision making process. Too much information can be worse than too little information. Swamping the manager with too much information may force the manager to rush into the decision making process or may force him to ignore certain vital bits of information while making the decision . Therefore, it is very important to have a

thorough understanding of the decision making process so that the manager can make an informed, timely decision. Herbert Simon model for decision making: A well known model proposed by Herbert A. Simon is used as the basis for describing decision making process. Intelligence Design

Choice

The model consists of three major phases, Intelligence, Design and Choice. Let us understand these terms in detail: 1 . Intelligence: Intelligence in this context does not refer to native intelligence(or the brains one is born with), but with the process of gathering information. It involves an awareness of the environment, an active attempt to gather information from the environment . This phase may be a continuous, ongoing phases or an intermittent effort, depending upon the requirements of the decision to be made. For example, a marketing executive may make periodic visits to key customers to review possible problems and identify new customer needs. Or, a driver on the road continuously scans the traffic in front, at the sides and behind him to see if there is any need for corrective action while driving. Intelligence involves Problem Finding and Problem Formulation. Problem finding means finding a difference between the existing state and a desired state. For example, f the desired state is customer satisfaction and the existing state is mild customer dissatisfaction, then there exists a problem Problem Formulation. On the other hand, is to identify and clarify the exact problem. In our previous example, the fact that a problem exists means little. There is need to identify why there is gap between th e current state and customer satisfaction. In other words, some complexity has to be reduced and a manageable problem formulated. As Bring. N.B Grant, Management Expert, often commented, “Gentlemen, define the problem well and solution will suggest itself.”

2. Design: Design indicates the generation of alternatives to solve the problem formulated. This is a creative process. For a long time, it was thought that creativity is inborn. However, Management Expert have proved that creativity can indeed be taught and nurtured. There are several have techniques that can be used for “Ideation(another term for the generation of alternatives),including Free Thinking, Analogy, Brainstorming, Checklists, etc.” 3.Choice: Once the manager has enough ideas or alternatives to work with, he can apply a rational process to choose the most viable alternatives to work with. He thus makes a choice from the alternatives available to select the path that would most likely solve the problem at hand.

Q. What is a Decision Support System ? What are the characteristics of Decision support system? (Rajiv Niyogi) Ans. The term decision support system refers to a class of systems which support the Process of decision making . The emphasis is on “support” rather than on automation of decisions . Decision support systems allow the decision maker to retrieve data and test alternative solutions during the process of problem solving . Characteristics of decision support sytem : The concept Decision support system is based on several assumptions about the role of the computer in effective decision making : (1) The computer must support the manager but not replace his /her judgement . It should therefore neither try to provide the “answers” nor “inputs” a Predefined sequence of analysis . (2)The main payoff of computer support is for semistructured problems , Where parts of the analysis can be systematized for the computer , But where the decision makers insight and the judgement are needed To control the process .

(3)Effective problem solving is interactive and enhance by a dialog between the user and the system . The users explores the problem situation using analytic and information providing capabilities of the system as well as human experience and insights . The decision support system should provide ease of access to the Database containing relevant data and interactive testing solutions . The designer must Understand the process of decision making for each situation in order to design a System to support it .

Question-Write short note on decision making concept. (Vivek Pophale) AnswerThe word “decision” is derived from Latin root decido, meaning to cut off. The concept of decision, therefore is settlement, a fixed intention bringing to a conclusive result, a judgment, and a resolution. A decision is choice out of several options made by the decision maker to achieve some objective in a given situation. Decisions differ in a number of ways. These differences affect the formulation of alternatives and the choice among them. They also affect the design of information system support for decision activities. Four dimensions of decision types which are useful for information systems are level of programmability, criteria for decision and level of decision impact. The decision making process is a complex process in the higher hierarchy of management. The complexity is the result of many factors, such as the inter-relationship among the experts or decision makers, a job responsibility, a question of feasibility, the codes of morals and ethics, and a probable impact on business. The personal values of the decision maker play a major role in decision making. The decision making process requires creativity, imagination and a deep understanding of human behavior. The process covers a number of tangible and intangible factors affecting the decision process. It also requires foresight to predict the post-decision implications and a willingness to face those implications. All decisions solve a ‘problem’ but over a period of time they give rise to a number of other ‘problems’.

Que:- Short notes on:1. LAW OF REQUISITE VARIETY 2. HERBERT SIMON MODEL 3. LIMITS ON HUMAN INFORMATION PROCESSING:4. SYSTEM APPROACH TO MIS 5. Life cycle Approach (Rajendra Brar)

(A) LAW OF REQUISITE VARIETY :The law of requisite variety means that for a system to be controlled, every controller(human and machine) must be provided with:(i) Enough control responses (what to do in each case) to cover all possible conditions the system may face. (ii) The decision rules for generating all possible control responses OR (iii) The authority to become a self organizing system in order to generate control responses. Enumerating all responses is possible in simple cases. In complex systems providing control responses is very difficult.

(B) HERBERT SIMON MODEL :-

A well known model proposed by HERBERT SIMON MODEL is used as the bases for describing decision making process.

(i) INTELLIGENCE (ii)DESIGN (iii)CHOICE

INTELLIGENCE

DESIGN

CHOICE

The model consist of 3 major phases :1. Intelligence in this context does not refer to native intelligence (or the brains one in born with), but with the process of gathering information. It involves an awareness of the environment, an active attempt to gather information from the environment. This phase may be a continuous, ongoing phase or an intermittent effort depending upon the requirements of the decisions to be made. 2. Design indicates the generation of alternatives to solve the problem formulated. This is a creative process. For a long time, it was thought that creativity is born. However, Management Experts have proved that creativity can indeed be taught and nurtured. There are several techniques that can be used for “Ideation”, including Free Thinking, Analogy, Brainstorming, Checklists, etc.

3. Choice: Once the manager has enough ideas or alternatives to work with, he can apply a rational process to choose the most viable alternative to work with. He thus makes a choice form the alternatives available to select the path that would most likely solve the problem at hand.

(C) LIMITS ON HUMAN INFORMATION PROCESSING :The Newell simon model suggests that means are limitation on the ability of human as information processor one set of limits concerns the processing Of data and is directly related to short term memory. another set of limits is the ability if human to detect differences. Humans are also limited to their ability to generate integrate and interpret probably data.

(D) System approach to MIS:MIS is, by definition, a system. How, it helps if we study mis in the light of sub system theory and understand exactly how mis behaves as a system. This will also help in the design of mis system. (i) Information system as a system the information system it self can be understood as a system with in the environment of the org. , data forms the input which is processed by the information system into information which Forms the output. data

process

informati on

There is a flow of activities from intelligence to design to choice. But at any phase, there may be a return to the previous phase. The intelligence phase of the model includes activities to identify problem situations or opportunity situation requiring design and choice.

Intelligence entails scanning the environment, either intermittently or continuously depending on the situation.

The above diagram, however, makes the assumption that all data flows in at the same time. This is rarely true. Data flows into the system in bits and pieces. Often, the system has to wait for related items of data to flow in before processing can begin. Therefore, the basic model has to be modified to include a Data Storage element. The addition of this element means that the Information System not only processes data into information, but also stores data for future use. Below figure makes this clear :-

Data Storage

Input Data

Output Process

Info

The Information system has five major sub-systems :1. Hardware and Systems Software. 2. Management and Administration. 3. Operations. 4. Application System Development and Maintenance. 5. Application System.

Systems Analysis and Design Systems analysis refers to the study of an existing system to understand its functioning in the light of its objectives. It seeks to understand the criteria for system effectiveness, as well as the various subsystems and

their interactions. This gives a clearer understanding of the desired features of the proposes system. Systems Design refers to the designing of a new system to replace an existing system. The new system must fulfill all the objectives of the old system, as well as new objectives that may have been defined at the Systems Analysis stage. The following systems concepts may be applied in Systems Analysis and Design :1.

Definition of the Information System and assignment of overall responsibility. 2. Definition of major Information processing subsystems. This includes specification of boundaries and interfaces. 3. Preparation of a development schedule. 4. Assignment of Subsystem development to various teams by the Project Leader. 5. Monitoring progress through a control subsystem. This process is called Structured Design.

(E) Life cycle Approach :While several organizations are moving to prototyping in developing Decision Support Systems, certain large projects still demand the System Development Life Cycle (SDLC) approach. These projects have many users and require the expertise of several different people. The prototyping approach does not work in such a case because it is difficult to have the prototype development by a team of experts and tested by several different users. The Life Cycle approach offers a structured, well-defined methodology to design, test and implement a new system. It structures the creative process and avoids wastage of time due to excessive testing and reworking. Control procedures can be laid down for each step of the cycle and there is a general agreement on the inputs, outputs and processing methodology. In general, the SDLC has the following phases :1. 2. 3. 4.

Definition. Development. Installation. Operation.

Definition refers to the phase where the information needs are defined. Feasibility studies and cost-effectiveness studies are also undertaken at this stage. These requirements are then translated into a physical system consisting of input forms, procedures, programs, output reports, etc. this is the development stage and it consists of using system design, computer programming and procedure development to construct a new system. Once constructed and tested, the third phase begins, where the new system is installed and operators/users are trained to use the new system. Once training is over, the fourth phase, operation, begins. Operation also consists of maintenance procedures, where changes may be made to the system based on new requirements or where existing parts of the system are not as efficient as they.

ANURAG DADHEECH SHORT NOTES :(A) Phase in the decision making:“The new science of mgt. decision “,postulates a model for decision making. In this model for decision-making. In his model, decisions-making follows three distinct phases, Intelligence, Design and Choice. Let us understand these terms in detail: 1. Intelligence in this context does not refer to native intelligence (or the brains one in born with), but with the process of gathering information. It involves an awareness of the environment, an active attempt to gather information from the environment. This phase may be a continuous, ongoing phase or an intermittent effort depending upon the requirements of the decisions to be made.

2. Design indicates the generation of alternatives to solve the problem formulated. This is a creative process. For a long time, it was thought that creativity is born. However, Management Experts have proved that creativity can indeed be taught and nurtured. There are several techniques that can be used for “Ideation”, including Free Thinking, Analogy, Brainstorming, Checklists, etc. 3. Choice: Once the manager has enough ideas or alternatives to work with, he can apply a rational process to choose the most viable alternative to work with. He thus makes a choice form the alternatives available to select the path that would most likely solve the problem at hand.

(B) CONTROL BY EXCEPTION Control by Exception refers to process of reporting to a manager to enable him to make an informed decision. If a manger is inundated with data, he may have to plough through this data in order to find the right elements on which to base his decisions. Most of his time would be spent in reading and discarding data that reports that “everything is okay” with the particular subsystem on which it is reporting. Instead, Control by Exception focuses only on things that are going wrong, enabling the manager to make a decision much faster. Thus, as long as things are going right with the subsystem, the manager may receive only summary reports on periodical bases. However, if anything goes wrong, the information is flashed immediately to the manager, who can then make a quicker decision since he is not distracted by volumes of data. We shall be studying this concept in greater detail when we study the design of Information System.

(C) PROTOTYPING :Prototyping is a method of developing a new system through and evolutionary process. It consists of building a basic system and then adding or taking away features and components bases on user feedback.

Prototyping is based on the observation that people are better able to tell what they like and don’t like about an existing system rather than list the features they would like in an imaginary future system. Prototyping is a very useful method to use when it is difficult to specify requirements in advance or where specifications may change during the development period itself. Prototyping is usually initiated by a user who comes with a specific problem to the system designer. The designer, an expert in prototyping, then builds a basic system that addresses the problem, based on the expert’s understanding of the problem. The user then tests the prototype and gives feedback to the expert, who changes the prototype based on this feedback. Thus, the prototype goes through several iterations of modification by the expert and testing by the actual user until most if not all, aspects of the problem are addressed. Prototyping usually results in better system acceptance, since the user is involved in the entire process.

(D) MANAGEMENT LEVEL AND INFORMATION NEEDS AT DIFFERENT LEVELS :-

Top level Middle level Lower level

(i) Action v/s non action:The information which include action , is called an action the information which communicate only the status of a situation is non action info.

(2)Recurring v/s non recurring information:The information generate at regular intervals is a recurring info the monthly sales report ,the stock statements , materials balance etc are recurring info the financial analysis or the report on the marketing research study is non recurring information.

(3)Internal v/s non internal information:The information generated to the internal sources of the org is as internal information , while the information generated through govt. reports the industry surveys ,etc is termed as external information .

Question: What is support required for design phase? Explain. (Ashok Dudhade) Answer: Following the intelligence phase which result in problem or opportunity recognition, the design phase involves inventing, developing analyzing possible courses of action. Support for design phase should provide for iterative procedures in considering alternatives. The following iterative steps are typical: 1) Support in Understanding the problem A correct model of situation to be applied or created, and the assumption of the model tested. 2) Support for generating solution A generation of possible courses of action is aided by: • The model itself. The manipulation of model frequently provides inside leading to generation of solution ideas. • The database retrieval system. The retrieval capabilities yield data useful in generating solution ideas. In many cases, the decision model will provide a suggested solution. For example, in an inventory reorder model many suggest a solution to the problem of how much to order. This quantity is a suggestion

that can be modified, but it represents a feasible solution (an perhaps an optimal solution based on the factors in the model). Often the decision support system will lead the user in rational search strategy for solutions. For example, the solution search procedure might begin with a set of questions relating to common solutions. These questions might be followed by a series of questions which assist the decision maker to consider all alternatives. The advantage of structured approaches is that they assist in systematically exploring the normal decision space; the disadvantage is the tendency to suppress search of outside the normal decision space. 3) Support for testing feasibility of solutions. A solution is tested for feasibility by analyzing it in terms of environment it affects- problem area, entire organization, competitors and society. The analysis may be performed judgmentally against broad measures of these environments. Another approach is to analyze to proposed solutions using models of different environments. These models will generally involve computer programs and a database.

Functions of MIS (Abhishek Rastogi)

“A management information system (MIS) is a formalized computer information system that can collect, store, process and report data from various sources to provide the information necessary for managerial decision making.” It can also be called Data Base Management System wherein maximum utilization in an efficient manner based on the organizational needs is obtained by analyzing, processing and referencing of Data Base. Not only for the business, but for all kinds of organizations such as Disaster Management Organization for using it as Disaster Management Information System. Business processes and operations support function are the most basic. They involve collecting, recording, storing, and basic processing of data. Information systems support business processes and operations by:

• • •

•

• • •

• •

•

Recording and storing accounting records including sales data, purchase data, investment data, and payroll data. Processing such records into financial statements such as income statements, balance sheets, ledgers, and management reports, etc. recording and storing inventory data, work in process data, equipment repair and maintenance data, supply chain data, and other production/operations records processing these operations records into production schedules, production controllers, inventory systems, and production monitoring systems recording and storing such human resource records as personnel data, salary data, and employment histories, processing these human resources records into employee expense reports, and performance based reports recording and storing market data, customer profiles, customer purchase histories, marketing research data, advertising data, and other marketing records processing these marketing records into advertising elasticity reports, marketing plans, and sales activity reports recording and storing business intelligence data, competitor analysis data, industry data, corporate objectives, and other strategic management records processing these strategic management records into industry trends reports, market share reports, mission statements, and portfolio models

The bottom line is that the information systems use all of the above to implement, control, and monitor plans, strategies, tactics, new products, new business models or new business ventures.

3. Information Systems Development Function (Methodologies)

Q:What is an expert system?explain?(Onkar) Ans:An expert system also known as a knowledge based system, is a computer program that contains some of the subject-specific knowledge of one or more human experts. This class of program was first developed by researchers in artificial intelligence during the 1960s and 1970s and applied commercially throughout the 1980s. The most common form of expert systems is a program made up of a set of rules that analyze information (usually supplied by the user of the system) about a specific class of problems, as well as providing analysis of the

problem(s), and, depending upon their design, recommend a course of user action in order to implement corrections. It is a system that utilizes reasoning capabilities to reach conclusions. The primary goal of expert systems research is to make expertise available to decision makers and technicians who need answers quickly. There is never enough expertise to go around -- certainly it is not always available at the right place and the right time. Portable with computers loaded with indepth knowledge of specific subjects can bring decades worth of knowledge to a problem. The same systems can assist supervisors and managers with situation assessment and long-range planning. Many small systems now exist that bring a narrow slice of in-depth knowledge to a specific problem, and these provide evidence that the broader goal is achievable. These knowledge-based applications of artificial intelligence have enhanced productivity in business, science, engineering, and the military. With advances in the last decade, today's expert systems clients can choose from dozens of commercial software packages with easy-to-use interfaces. Each new deployment of an expert system yields valuable data for what works in what context, thus fueling the AI research that provides even better applications.

1. Types of problems solved by expert systems 2. Application 3. Expert systems versus problem-solving systems 4. Individuals involved with expert systems 1 The end user 2 The knowledge engineer 5 The inference rule 1 Chaining 6 Confidences 1 The user interface 2 Procedure node interface 7 Advantages and disadvantages

We will discuss one by one 1.Types of problems solved by expert systems: Expert systems are most valuable to organizations that have a high-level of know-how experience and expertise that cannot be easily transferred to other members. They are designed to carry the intelligence and information found in the intellect of experts and provide this knowledge to other members of the organization for problem-solving purposes. Typically, the problems to be solved are of the sort that would normally be tackled by a medical or other professional. Real experts in the problem domain (which will typically be very narrow, for instance "diagnosing skin in human teenagers") are asked to provide "rules of thumb" on how they evaluate the problems, either explicitly with the aid of experienced systems developers, or sometimes implicitly, by getting such experts to evaluate test cases and using computer programs to examine the test data and (in a strictly limited manner) derive rules from that. Generally expert systems are used for problems for which there is no single "correct" solution which can be encoded in a conventional algorithm — one would not write an expert system to find shortest paths through graphs, or sort data, as there are simply easier ways to do these tasks.Simple systems use simple true/false logic to evaluate data, but more sophisticated systems are capable of performing at least some evaluation taking into account realworld uncertainties, using such methods as fuzzy logic. Such sophistication is difficult to develop and still highly imperfect. 2.Application: Expert systems are designed and created to facilitate tasks in the fields of accounting, medicine, process control, financial service, production, human resources etc. Indeed, the foundation of a successful expert system depends on a series of technical procedures and development that may be designed by certain technicians and related experts. When a corporation begins to develop and implement an expert system project, it will use selfsourcing, insourcing and / or outsourcing techniques. While expert systems have distinguished themselves in AI research in finding practical application, their application has been limited.

Expert systems are notoriously narrow in their domain of knowledge —as an amusing example, a researcher used the "skin disease" expert system to diagnose his rustbucket car as likely to have developed measles—and the systems were thus prone to making errors that humans would easily spot. Additionally, once some of the mystique had worn off, most programmers realized that simple expert systems were essentially just slightly more elaborate versions of the decision logic they had already been using. Therefore, some of the techniques of expert systems can now be found in most complex programs without any fuss about them. An example of an expert system used by many people is the Microsoft Windows operating system troubleshooting software located in the "help" section in the taskbar menu. Obtaining expert / technical operating system support is often difficult for individuals not closely involved with the development of the operating system. Microsoft has designed their expert system to provide solutions, advice, and suggestions to common errors encountered throughout using the operating systems. Another 1970s and 1980s application of expert systems — which we today would simply call AI — was in computer games. For example, the computer baseball games Earl Weaver Baseball and Tony La Russa Baseball each had highly detailed simulations of the game strategies of those two baseball managers. When a human played the game against the computer, the computer queried the Earl Weaver or Tony La Russa Expert System for a decision on what strategy to follow. Even those choices where some randomness was part of the natural system (such as when to throw a surprise pitch-out to try to trick a runner trying to steal a base) were decided based on probabilities supplied by Weaver or La Russa. Today we would simply say that "the game's AI provided the opposing manager's strategy." 3.Expert systems versus problem-solving systems: The principal distinction between expert systems and traditional problem solving programs is the way in which the problem related expertise is coded. In traditional applications, problem expertise is encoded in both program and data structures.

In the expert system approach all of the problem related expertise is encoded in data structures only; none is in programs. Several benefits immediately follow from this organization. An example may help contrast the traditional problem solving program with the expert system approach. The example is the problem of tax advice. In the traditional approach data structures describe the taxpayer and tax tables, and a program in which there are statements representing an expert tax consultant's knowledge, such as statements which relate information about the taxpayer to tax table choices. It is this representation of the tax expert's knowledge that is difficult for the tax expert to understand or modify. In the expert system approach, the information about taxpayers and tax computations is again found in data structures, but now the knowledge describing the relationships between them is encoded in data structures as well. The programs of an expert system are independent of the problem domain (taxes) and serve to process the data structures without regard to the nature of the problem area they describe. For example, there are programs to acquire the described data values through user interaction, programs to represent and process special organizations of description, and programs to process the declarations that represent semantic relationships within the problem domain and an algorithm to control the processing sequence and focus. The general architecture of an expert system involves two principal components: a problem dependent set of data declarations called the knowledge base or rule base, and a problem independent (although highly data structure dependent) program which is called the inference engine 4.Individuals involved with expert systems: There are generally three individuals having an interaction with expert systems. Primary among these is the end-user; the individual who uses the system for its problem solving assistance. In the building and maintenance of the system there are two other roles: the problem domain expert who builds and supplies the knowledge base providing the domain expertise, and a knowledge engineer who assists the experts in determining the representation of their knowledge, enters this

knowledge into an explanation module and who defines the inference technique required to obtain useful problem solving activity. Usually, the knowledge engineer will represent the problem solving activity in the form of rules which is referred to as a rule-based expert system. When these rules are created from the domain expertise, the knowledge base stores the rules of the expert system. @ The end user: The end-user usually sees an expert system through an interactive dialog, an example of which follows: Q. Do you know to which restaurant you want to go? A. No Q. Is there any kind of food you would particularly like? A. Unknown Q. Do you like spicy food? A. No Q. Do you usually drink wine with meals? A. Yes Q. When you drink wine, is it French wine? A. Why As can be seen from this dialog, the system is leading the user through a set of questions, the purpose of which is to determine a suitable set of restaurants to recommend. This dialog begins with the system asking if the user already knows the restaurant choice (a common feature of expert systems) and immediately illustrates a characteristic of expert systems; users may choose not to respond to any question. In expert systems, dialogs are not pre-planned. There is no fixed control structure. Dialogs are synthesized from the current information and the contents of the knowledge base. Because of this, not being able to supply the answer to a particular questions does not stop the consultation. Another major distinction between expert systems and traditional systems is illustrated by the following answer given by the system when the user answers a question with another question, "Why", as occurred in the above example. The answer is: A. I am trying to determine the type of restaurant to suggest. So far Chinese is not a likely choice. It is possible that French is a likely choice. I know that

if the diner is a wine drinker, and the preferred wine is French, then there is strong evidence that the restaurant choice should include French. It is very difficult to implement a general explanation system (answering questions like Why and How) in traditional systems. The response of the expert system to the question WHY is an exposure of the underlying knowledge structure. It is a rule; a set of antecedent conditions which, if true, allow the assertion of a consequent. The rule references values, and tests them against various constraints or asserts constraints onto them. This, in fact, is a significant part of the knowledge structure. There are values, which may be associated with some organizing entity. For example, the individual diner is an entity with various attributes (values) including whether they drink wine and the kind of wine. There are also rules, which associate the currently known values of some attributes with assertions that can be made about other attributes. It is the orderly processing of these rules that dictates the dialog itself. @ The knowledge engineer Knowledge engineers are concerned with the representation chosen for the expert's knowledge declarations and with the inference engine used to process that knowledge. He / she can use the knowledge acquisition component of the expert system to input the several characteristics known to be appropriate to a good inference technique including: 1. A good inference technique is independent of the problem domain. In order to realize the benefits of explanation, knowledge transparency, and reusability of the programs in a new problem domain, the inference engine must not contain domain specific expertise. 2. Inference techniques may be specific to a particular task, such as diagnosis of hardware configuration. Other techniques may be committed only to a particular processing technique. 3. Inference techniques are always specific to the knowledge structures. 4. Successful examples of rule processing techniques include: (a) Forward chaining (b) Backward chaining 5.The inference rule:

An understanding of the "inference rule" concept is important to understand expert systems. An inference rule is a statement that has two parts, an if-clause and a thenclause. This rule is what gives expert systems the ability to find solutions to diagnostic and prescriptive problems. An example of an inference rule is: If the restaurant choice includes French, and the occasion is romantic, Then the restaurant choice is definitely Paul Bocuse.An expert system's rulebase is made up of many such inference rules. They are entered as separate rules and it is the inference engine that uses them together to draw conclusions. Because each rule is a unit, rules may be deleted or added without affecting other rules (though it should affect which conclusions are reached). One advantage of inference rules over traditional programming is that inference rules use reasoning which more closely resemble human reasoning. Thus, when a conclusion is drawn, it is possible to understand how this conclusion was reached. Furthermore, because the expert system uses knowledge in a form similar to the expert, it may be easier to retrieve this information from the expert. @Chaining: There are two main methods of reasoning when using inference rules: backward chaining and forward chaining. Forward chaining starts with the data available and uses the inference rules to conclude more data until a desired goal is reached. An inference engine using forward chaining searches the inference rules until it finds one in which the if-clause is known to be true. It then concludes the then-clause and adds this information to its data. It would continue to do this until a goal is reached. Because the data available determines which inference rules are used,this method is also called data driven. Backward chaining starts with a list of goals and works backwards to see if there is data which will allow it to conclude any of these goals. An inference engine using backward chaining would search the inference rules until it finds one which has a then-clause that matches a desired goal. If the if-clause of that inference rule is not known to be true, then it is added to the list of goals.

For example, suppose a rulebase contains two rules: (1) If Fritz is green then Fritz is a frog. (2) If Fritz is a frog then Fritz hops. Suppose a goal is to conclude that Fritz hops. The rulebase would be searched and rule (2) would be selected because its conclusion (the then clause) matches the goal. It is not known that Fritz is a frog, so this "if" statement is added to the goal list. The rulebase is again searched and this time rule (1) is selected because its then clause matches the new goal just added to the list. This time, the if-clause (Fritz is green) is known to be true and the goal that Fritz hops is concluded. Because the list of goals determines which rules are selected and used, this method is called goal driven. 6.Confidences: Another advantage of expert systems over traditional methods of programming is that they allow the use of confidences. When a human reasons he does not always conclude things with 100% confidence. He might say, "If Fritz is green, then he is probably a frog" (after all, he might be a chameleon). This type of reasoning can be imitated by using numeric values called confidences. For example, if it is known that Fritz is green, it might be concluded with 0.85 confidence that he is a frog; or, if it is known that he is a frog, it might be concluded with 0.95 Confidence that he hops. These numbers are similar in nature to probabilities, but they are not the same. They are meant to imitate the confidences humans use in reasoning rather than to follow the mathematical definitions used in calculating probabilities. The following general points about expert systems and their architecture have been illustrated. 1. The sequence of steps taken to reach a conclusion is dynamically synthesized with each new case. It is not explicitly programmed when the system is built.

2. Expert systems can process multiple values for any problem parameter. This permits more than one line of reasoning to be pursued and the results of incomplete (not fully determined) reasoning to be presented. 3. Problem solving is accomplished by applying specific knowledge rather than specific technique. This is a key idea in expert systems technology. It reflects the belief that human experts do not process their knowledge differently from others, but they do possess different knowledge. With this philosophy, when one finds that their expert system does not produce the desired results, work begins to expand the knowledge base, not to reprogram the procedures. There are various expert systems in which a "rulebase" and an "inference engine" cooperate to simulate the reasoning process that a human expert pursues in analyzing a problem and arriving at a conclusion. In these systems, in order to simulate the human reasoning process, a vast amount of knowledge needed to be stored in the knowledge base. Generally, the knowledge base of such an expert system consisted of a relatively large number of "if then" type of statements that were interrelated in a manner that, in theory at least, resembled the sequence of mental steps that were involved in the human reasoning process. Because of the need for large storage capacities and related programs to store the rulebase, most expert systems have, in the past, been run only on large information handling systems. Recently, the storage capacity of personal computers has increased to a point where it is becoming possible to consider running some types of simple expert systems on personal computers. In some applications of expert systems, the nature of the application and the amount of stored information necessary to simulate the human reasoning process for that application is just too vast to store in the active memory of a computer. In other applications of expert systems, the nature of the application is such that not all of the information is always needed in the reasoning process. An example of this latter type application would be the use of an expert system to diagnose a data processing system comprising many separate components, some of which are optional. When that type of expert system employs a single integrated rulebase to diagnose the minimum system configuration of the data processing system, much of the rulebase is not required since many of the components which are optional units of the system will not be present in the system. Nevertheless, earlier expert

systems require the entire rulebase to be stored since all the rules were, in effect, chained or linked together by the structure of the rulebase. When the rulebase is segmented, preferably into contextual segments or units, it is then possible to eliminate portions of the Rulebase containing data or knowledge that is not needed in a particular application. The segmenting of the rulebase also allows the expert system to be run with systems or on systems having much smaller memory capacities than was possible with earlier arrangements since each segment of the rulebase can be paged into and out of the system as needed. The segmenting of the rulebase into contextual segments requires that the expert system manage various intersegment relationships as segments are paged into and out of memory during execution of the program. Since the system permits a rulebase segment to be called and executed at any time during the processing of the first rulebase, provision must be made to store the data that has been accumulated up to that point so that at some time later in the process, when the system returns to the first segment, it can proceed from the last point or rule node that was processed. Also, provision must be made so that data that has been collected by the system up to that point can be passed to the second segment of the rulebase after it has been paged into the system and data collected during the processing of the second segment can be passed to the first segment when the system returns to complete processing that segment. The user interface and the procedure interface are two important functions in the information collection process. @The user interface: The function of the user interface is to present questions and information to the operator and supply the operator's responses to the inference engine. Any values entered by the user must be received and interpreted by the user interface. Some responses are restricted to a set of possible legal answers, others are not. The user interface checks all responses to insure that they are of the correct data type. Any responses that are restricted to a legal set of answers are compared against these legal answers. Whenever the user enters an illegal answer, the user interface informs the user that his answer was invalid and prompts him to correct it. As explained in the cross referenced application, communication between the user interface and the inference engine is performed through the use of a User Interface Control Block (UICB) which is passed between the two.

@Procedure node interface: The function of the procedure node interface is to receive information from the procedures coordinator and create the appropriate procedure call. The ability to call a procedure and receive information from that procedure can be viewed as simply a generalization of input from the external world. While in some earlier expert systems external information has been obtained, that information was obtained only in a predetermined manner so only certain information could actually be acquired. This expert system, disclosed in the cross-referenced application, through the knowledge base, is permitted to invoke any procedure allowed on its host system. This makes the expert system useful in a much wider class of knowledge domains than if it had no external access or only limited external access. In the area of machine diagnostics using expert systems, particularly selfdiagnostic applications, it is not possible to conclude the current state of "health" of a machine without some information. The best source of information is the machine itself, for it contains much detailed information that could not reasonably be provided by the operator. The knowledge that is represented in the system appears in the rulebase. In the rulebase described in the cross-referenced applications, there are basically four different types of objects, with associated information present. 1. Classes--these are questions asked to the user. 2. Parameters--a parameter is a place holder for a character string which may be a variable that can be inserted into a class question at the point in the question where the parameter is positioned. 3. Procedures--these are definitions of calls to external procedures. 4. Rule Nodes--The inferencing in the system is done by a tree structure which indicates the rules or logic which mimics human reasoning. The nodes of these trees are called rule nodes. There are several different types of rule nodes. The rulebase comprises a forest of many trees. The top node of the tree is called the goal node, in that it contains the conclusion. Each tree in the forest has a different goal node. The leaves of the tree are also referred to as rule nodes, or one of the types of rule nodes. A leaf may be an evidence node, an external node, or a reference node. An evidence node functions to obtain information from the operator by asking a specific question. In responding to a question presented by an

evidence node, the operator is generally instructed to answer "yes" or "no" represented by numeric values 1 and 0 or provide a value of between 0 and 1, represented by a "maybe." Questions which require a response from the operator other than yes or no or a value between 0 and 1 are handled in a different manner. A leaf that is an external node indicates that data will be used which was obtained from a procedure call. A reference node functions to refer to another tree or subtree. A tree may also contain intermediate or minor nodes between the goal node and the leaf node. An intermediate node can represent logical operations like And or Or. The inference logic has two functions. It selects a tree to trace and then it traces that tree. Once a tree has been selected, that tree is traced, depth-first, left to right. The word "tracing" refers to the action the system takes as it traverses the tree, asking classes (questions), calling procedures, and calculating confidences as it proceeds. As explained in the cross-referenced applications, the selection of a tree depends on the ordering of the trees. The original ordering of the trees is the order in which they appear in the rulebase. This order can be changed, however, by assigning an evidence node an attribute "initial" which is described in detail in these applications. The first action taken is to obtain values for all evidence nodes which have been assigned an "initial" attribute. Using only the answers to these initial evidences, the rules are ordered so that the most likely to succeed is evaluated first. The trees can be further reordered since they are constantly being updated as a selected tree is being traced. It has been found that the type of information that is solicited by the system from the user by means of questions or classes should be tailored to the level of knowledge of the user. In many applications, the group of prospective uses is nicely defined and the knowledge level can be estimated so that the questions can be presented at a level which corresponds generally to the average user. However, in other applications, knowledge of the specific domain of the expert system might vary considerably among the group of prospective users. One application where this is particularly true involves the use of an expert system, operating in a self-diagnostic mode on a personal computer to assist the operator of the personal computer to diagnose the cause of a fault or

error in either the hardware or software. In general, asking the operator for information is the most straightforward way for the expert system to gather information assuming, of course, that the information is or should be within the operator's understanding. For example, in diagnosing a personal computer, the expert system must know the major functional components of the system. It could ask the operator, for instance, if the display is a monochrome or color display. The operator should, in all probability, be able to provide the correct answer 100% of the time. The expert system could, on the other hand, cause a test unit to be run to determine the type of display. The accuracy of the data collected by either approach in this instance probably would not be that different so the knowledge engineer could employ either approach without affecting the accuracy of the diagnosis. However, in many instances, because of the nature of the information being solicited, it is better to obtain the information from the system rather than asking the operator, because the accuracy of the data supplied by the operator is so low that the system could not effectively process it to a meaningful conclusion. In many situations the information is already in the system, in a form of which permits the correct answer to a question to be obtained through a process of inductive or deductive reasoning. The data previously collected by the system could be answers provided by the user to less complex questions that were asked for a different reason or results returned from test units that were previously run. 7.Advantages and disadvantages: Expert systems exercise information technology to acquire and utilize human expertise. It can be beneficial for organizations that have clear objectives, rules and procedures. Expert systems can: Provide consistent answers for repetitive decisions, processes and tasks Hold and maintain significant levels of information Reduce employee training costs Centralize the decision making process Create efficiencies and reduce time needed to solve problems Combine multiple human expert intelligences Reduce the amount of human errors Give strategic and comparative advantages creating entry barriers to competitors Review transactions that human experts may overlook

Although significantly advantageous to many entities, limitations of expert systems may arise through: The lack of human common sense needed in some decision makings The creative responses human experts can respond to in unusual circumstances Domain experts not always being able to explain their logic and reasoning The challenges of automating complex processes The lack of flexibility and ability to adapt to changing environments Not being able to recognize when no answer is available How it works? Expert Systems consist of: knowledge base (facts) production rules ("if.., then..") inference engine (controls how "if.., then.." rules are applied towards facts) Actually there are two methods to make conclusions. -------------------------------------------------------------------------------------------------------------------------------------------------------Method name short explanation use example systems -------------------------------------------------------------------------------------------------------------------------------------------------------Forward chaining facts driven can find new ideas CLIPS, Jess Backward chaining hypothesis driven usually used for diagnosis Prolog, Mycin

Knowledge Base Expert Systems (Sandeep A)

Knowledge-based expert systems, or simply expert systems, use human knowledge to solve problems that normally would require human intelligence. These expert systems represent the expertise knowledge as data or rules within the computer. These rules and data can be called upon when needed to solve problems. Books and manuals have a tremendous amount of knowledge but a human has to read and interpret the knowledge for it to be used. Conventional computer programs perform tasks using conventional decision-making logic containing little knowledge other than the basic algorithm for solving that specific problem and the necessary boundary conditions. This program knowledge is often embedded as part of the programming code, so that as the knowledge changes, the program has to be changed and then rebuilt. Knowledge-based systems collect the small fragments of human know-how into a knowledge-base which is used to reason through a problem, using the knowledge that is appropriate. A different problem, within the domain of the knowledge-base, can be solved using the same program without reprogramming. The ability of these systems to explain the reasoning process through back-traces and to handle levels of confidence and uncertainty provides an additional feature that conventional programming doesn’t handle. Most expert systems are developed via specialized software tools called shells. These shells come equipped with an inference mechanism (backward chaining, forward chaining, or both), and require knowledge to be entered according to a specified format. They typically come with a number of other features, such as tools for writing hypertext, for constructing friendly user interfaces, for manipulating lists, strings, and objects, and for interfacing with external programs and databases. These shells qualify as languages, although certainly with a narrower range of application than most programming languages

1. What are the components of Information System?(Vaibhav)

The information system of an organization is basically its physical components. The physical components required for an organizational information system are hardware, software, database, procedures and operations personnel. These elements described below, Physical Component Hardware

Description

Software

Software is a broad term given to the instructions that direct the operation of the hardware. The software can be classified into two major types: system software and application software.

Hardware refers to physical computer equipment and essential devices. Hardware must provide for five major functions. 1. Input or entry 2. Output 3. Secondary storage for data and programs 4. Central processor (computation, control and primary storage) 5. Communications

Database The database contains all data utilized by application software. An individual set of stored data is often referred to as a file. The physical existence of stored data is evidenced by the physical storage media (computer tapes, hard-disk drives etc.) used for secondary storage. Procedures

Operations personnel

Formal operating procedures are physical components because they exist in a physical form such as a manual or instruction booklet. Three major types of procedures are required. 1. User instructions (for uses of the application to record data, employ a terminal to enter of retrieve data, or use the result) 2. Instructions for preparation of input by data preparation personnel 3. Operating instructions for computer operations personnel

Computer operators, system analysts, programmers, data preparation personnel, information systems management, data administrators, etc.

2. What are the functional aspects of Information System?(Vaibhav) There may be a common support system used by more than one subsystem but each functional system is unique in its procedures, programs, models etc. Major functional subsystems

Some Typical uses

1. Marketing

Sales forecasting, sales planning, customer and sales analysis

2. Manufacturing

Production, planning and scheduling, cost control analysis

3. Logistics

Planning & control of purchasing inventories & distribution

4. Personnel

Planning personnel requirements, analyzing performance, salary administration

5. Finance & Accounting

Financial Analysis, cost analysis, capital requirements planning, income measurement

6. Information Processing

Information system, planning, cost effective analysis

7. Top Management

Strategic planning, resource allocation

MANAGEMENT SUPPORT SYSTEM (Prashant) Q. Explain support for the intelligence phase in decision making . Ans. The intelligence phase of the decision making process consists of problem finding activities related searching the environment for conditions calling for decisions. Analysis and choice cannot proceed until the problem has been identified and formulated. The intelligence phase, therefore , consists of searching or scanning the internal and external environment for conditions which suggest an opportunity or a problem. The existence of an opportunity or a problem initiates the design and choice phases of decision making.

The database needed in the intelligence phase is very comprehensive. In general, it should cover three environments: Environments Societal Competitive Internal

Description The economical, social , and legal environment in which the organization operates The characteristics and behavior of the marketplace in which the organization operates. The capabilities, strengths, weaknesses, constraints, and other

factors affecting the ability of the organization to perform its functions. The data on internal environment is generally available through processing of operational data. Some societal competitive data is available through published data banks. The concept of decision support systems does not imply that all data is in computerized database . It does imply , however, that the data is systematically collected and stored and is accessible to the user of the system. In some cases , the database can store a pointer the data such as reference to a government statistical report. The primary requirement of decision support for intelligence is the ability to search the database for opportunities and problems. The search process has different characteristics depending on whether it can be structured and whether it is continuous or ad hoc . There are three types of search 1. Structured , continuous search 2. Structured ad hoc search 3. Unstructured search.

Deterministic v/s Probabilistic system:- (Vikas) A deterministic system operates in a predictable manner is known with certainly. If one has a description of the state of the system at a given point in time plus a description of its operation, the next state of the system can be given exactly without error. The probabilistic system can be described in terms of probable behavior, but a certain degree of error is always attached to the prediction of what the system will do. Closed & Open system:A closed system is defined in physics as a system which is self contained. It doesn’t exchange material information or energy with its environment such closed system will finally run down or become

disorganized. This movement to disorder is termed as increase in entropy. Open system exchange into material or energy with environment including random and undefined inputs. Open system tend to have formed and structured to allow them to adapt to change in their environment in such a way as to continue their existence. They are self organizing. In the sense that they can change their organization in response to change in conditions. Organizations are open system; the critical feature of their existence is their capability to adapt in the change. Organization illustrates the system concept of equifinality. Artificial system are system that are created rather than occurring in nature organization information system and computer programs are all example of artificial system. The artificial systems are designed to support the objective of designers and users. They exhibit character of system that they support principles that apply to living system are also applicable to artificial system that support human or other living system. DBMS is a feature of MIS:- The MIS is supported by database in its endeavour to support the management in decision making. The database models be it the NDBM, the HDBM or RDBM, play the same role in the MIS. With e the latest computer hardware and software capabilities the RDBMS have become popular. The concept of the end used computing can be implemented easily with the database approach to the information system. The major problems, which the MIS designers had to face earlier, were on account of the different definitions of data by the different users, and its applications. Theses problems have automatically dispappeared with the database approach. Another problem which the designers faced was that of data concurrency and redundancy. Once the entity is defined and located in the database, it is same and common to all. All the users using the database will get the same results on account of the concurrency and hence avoid data redundancy. With the RDBMS and development of SQL, it is possible to interact with the database and satisfy the queries by using the SQL. The

attributes of a good information, viz., accuracy, scope, timeliness, form and so on can be easily achieved with the database approach to the MI System. The database has strengthened the foundations of the MIS due to the following: (a) The database can be evolved to the new needs of the MIS. (b) The multiple needs can be met with easily. (c) The data design and the output design is flexible\ (d) Open system design of the MIS is possible. (e) The query handling becomes easier due to the Standard SQL. (f) User- friendly end user computing is possible (g) The data is freed from its ownership and its use has become universal (h) The Information Technology provides tools to handle distributed multiple databases making the MIS richer. Modern MIS uses databases and SQL, 4GL programs and decision support systems extensively fro information generation as shown in Figure.

USER’S VIEW PROGRAMMER’S VIEW DESIGNER’S VIEW

MIS

SQL

4GL PROGRAMMES

DSS

SQL

4GL PROGRAMMES

DSS

Figure: - Database & MIS

Q. Write short note on:- (Shailesh) 2. Deterministic and Probabilistic System:A Deterministic system operates in a predictable manner the interaction among the parts is known with the certainty. If one has a description of its operation, the next state of the system can be given exactly, without error.

Probabilistic system can be described in terms of probable behaviour but a certain degree of error is always attatched to the prediction of what the system will do. 3. Value of Information (In Decision Making):In Decision theory, the value of information is the value of change in behaviour caused by information less; the cost of obtaining information. In other words given a set of possible decisions a decision maker will select one on the information at hand. If new information causes a different decision to be made, the value of new information is the difference in value of outcome old decision and that of new decision and if not then the value of new information is zero. Value of Information (& Sensitivity Analysis):Sensitivity Analysis consists of analytical procedures to determine the degree of impact on a solution algorithm or model of changes in one or more variables. It can be used to determine the changes in factors such as estimated costs, revenues, and obsolescence that are large enough to cause a change in decision. In doing so, it identifies variables for which more information will be variable. Value of Information (Other than in Decision Making):Some other reasons for value of information are Motivation, Model Building and Background Building.  Motivation:Some information is motivational; it provides the persons receiving the information with a report on how well they are doing. This feedback information may motivate decisions, but its connection is often indirect.



Model Building:-

The models may be simple or complex, correct or incorrect, etc. Information that is received by these individuals may result in change or reinforcement in their models. This process is a form of organizational learning and expertise building. 

Background Building:In decision theory, the value of information is the value of change in decision behaviour but the information has value only to those who have the background knowledge to use it in a decision. The most qualified person generally uses information most effectively but may need less information since experience has already reduced uncertainty when compared with the less experienced decision maker.

HEURISTIC PROGRAMMING (Ranjana) Traditionally, procedures for determining information requirements are designed to establish a complete and correct set of requirements before the information system is designed and built. However in a large

number

of

cases,

user

may

not

be

able

to

formulate

information requirements because they have no existing model on which to base requirements. They may find it difficult to deal in abstract requirements or to visualize new systems users may be more comfortable working with concrete systems on which they can make modifications. Therefore,

another

approach

to

information

requirements

determination is to capture an initial set of requirements and implement a 'bare- bones' information system to provide those requirements. The system is designed to be changed quickly and

easily, based on user or changes to the system. This is called "heuristic development" it is simply another term for prototyping. Although heuristics are employed primarily for solving ill-structured problems, they can also be used to provide satisfactory solutions to certain complex, well-structured problems much more quickly and cheaply than optimization organization. The main difficulty in using heuristics is that they are not as general as algorithms. Therefore, they can normally be used only for the specific solution for which they where intended. Another problem with heuristics is that they may produce a poor solution. Heuristic programming is the approach of using heuristics to arrive at feasible and good enough solutions to some complex problems. Good enough is usually in the range of 90-99.9% of the objective value of an optimal solution. Heuristics thinking does not necessarily proceed in direct manner. It involves searching, learning, evaluating, and then researching, relearning, and reappraising as exploring and probing take place. The knowledge gained from success or failure at some point is fed back to and modifies the search process.



When To Use Heuristics I. The input data are inexact or limited. II. Reality is so complex that optimization models cannot be used. III. A reliable, exact algorithm is not available. IV. Quick decisions are to be made, and computerization is not feasible.



Limitation of Heuristics I. An optional solution cannot be guaranteed.

II. There may be too many exceptions to the rules. III. Sequential decision choices can fail to anticipate the future consequence of each choice.

Composition between heuristic & Analytical Approaches: Problem-solving

Heuristic Approach

Analytical Approach

Dimension 1. Approach to

Learn more by aching

It employs a planned

then by analyzing the

sequential approach to

learning

situation, and places more problem solving, learns emphasis on feedback

more analyzing the situation then by acting; and places less emphasis on feedback.

Uses trial-and-error, and 2. Searching

spontaneous action It uses formal rational analysis and no spontaneous actions takes place

3. Approach to analysis

Use common sense, institution and feelings

Develops explicit models for the situations. 4. Scope of

It views totality of the

Analysis

problem situation. It reduces the problem to smaller tasks.

Operation Research Techniques: Management science is a scientific approach to the solution of operational problems. It is concerned with providing management with decision rules devised from the following: I. A total system orientation. II. Scientific methods of investigation. III. Models of reality, generally base on quantitative measurements and techniques. This technique of management science is also known as operation research. The difference between ordinary techniques and operation research can be seen in following: KEYS

GENERAL APPROACH

OR APPROACH

 Problem Solving

 Observation

 Search for problems.

 Statement of a problem

 Statement of a

 Collection of data  Development of hypotheses for solution of the problem.  Evaluation of alternative hypothesis

problem.  Collection of data  Development and testing of a model representing the problem solution.  Manipulation of the model to determine the outcomes of various

 Decision

Selection of best alternative

making and action

input conditions  Selection of best course of action.  Implementation of best alternative.  Implementation of the solution.  Review of results.  Control of the model by maintaining a check on its validity as time goes by.

Q. Short note on Expert systems? (Rahul Joshi)

Answer. Expert systems attempt to capture the knowledge of human expert and make it available through a computer system. An expert system is: “One which addresses problems normally thought to require human expertise for their solution.” 1) Expert systems are expected to achieve significant actual performance in a specialized area that normally requires a human expert for successful performance, e.g. medicine, geology, investment, counseling, etc 2) Expert systems have been some of the most successful applications A.I. Since these programs must perform in real world, they encounter important issues for A.I.: i) Lack of sufficient input information. ii) Probrabilistic reasonsing. Q. Differentiate between DSS and MIS? Answer. A DSS is a computer based system, using hardware and software. It is used by decision makers for managerial decision making. It helps to arrange information for decision making. DSS is part of MIS. Thus, it forms a subset of MIS, with MIS providing the general enviroment within which the DSS operates. The spectic difference between MIS and DSS are as follows: 1. MIS support decision making in both structured and unstructed problem enviroments. DSS, on the other hand, only works with structured problems. 2. MIS supports decision-making at all levels of the organisation. DSS is usually used at the executive level and above to provide a basis for decision-making. 3. MIS is intended to be integrated into the organization, not standing alone. A DSS system may even be apart from the general MIS, created by or for a specific executive. 4. MIS supports all aspects of the decision making process. DSS only tackles the routine and repetitive decisions and provides data nonrepetive decisions. 5. MIS is composed of people, computer, procedures, database, interactive query use. While DSS also also has these elements, it does not face the constraint of having to be made easy to use.

Q: - Explain the application of negative feedback in a) A management reporting system using budget b) A management reporting system reporting actual figure with no comparison. (Siraj) Ans:Negative feedback control in system means keeping system operating within certain limit of performance. A system which is out of control functions outside the available limit because the regulatory mechanisms are not operative. Control using Negative feedback normally involves 4 steps. 1. A characteristics or condition to be control The characteristic or condition may be measurable some output. 2. A sensor for measuring the characteristic or condition. 3. A control unit which compare the measurement with standard for that characteristic or condition. 4. An activity unit which generates a corrective input signal to the process. Feedback control loop are frequently classified as closed or open .A Closed control loop is an automated process. An Open control loop one with random disturbances. Law of Requisite of Variety:The Law of Requisite of Variety means that for a system to control every controller (human or machine) must be provided with 1. Enough control responses (what to do in each case) to cover all possible condition, the system may face. 2. The decision rules for generating all possible control responses or 3. The authority to become a self-organizing system in order to generate control responses. Enumerating all response is possible in simple cases. In complex system providing control responses is very difficult.

NOTICE

Following is the list of the students along with the questions allotted for Management Support System Question Bank. The students are supposed to type the answers and mail it to [email protected] latest by Saturday, i.e, 12th August, 2006. Naveen Jain

Differentiate between data information and knowledge giving suitable examples. Indicate how systems related to each of these can be developed. Tanmay Discuss Newell Simon model – Human as a Information Processor in Patel detail along with its emulation. Abhishek a) Define system. Explain control by exception in system. Kanetkar b) Compare MIS with Data Processing. Minal Javale a) Explain how quality of information is decided. b) Explain coupling and decoupling of subsystems. Sumit Joshi Short notes on a) Value of information b) Intelligence-Design-Choice Model c) Types of decision d) Law of requisite variety. Priya Doshi How does MIS differ from a) Managerial Accounting b) Management Science Vamshi Priya Differentiate between a) Open and Closed System b) Negative and positive feedback. Ravi Kumar Comment on the Information needs of executives. Rajendra Short notes on: Brar a) Herbert Simon Model b) Life cycle approach in MIS design. c) Limitation on human as information processor. d) System approach to MIS. e) Law of requisite variety. Siraj Sache Explain the application of negative feedback in a) A management reporting system using budget. b) A management reporting system reporting actual figure with no cmparison. Vaibhav a) What are the components of Information System. Verma b) Comment on functional aspects in information system.

Vikas

a) Why is a database generally a feature of MIS. b) Differentiate between i) Open and closed System

Abhishek Rastogi Anurag Dhadich

Pravin Gawde Pravin Yadav Bhushan Belsare Shailesh Yalkar

Vivek

ii) Deterministic and probabilistic system. What are the functions of MIS.

Write short notes on: a) Management levels and Information needs at different levels b) Prototyping c) Phases in Decision making d) Control by exception Discuss characteristics of Human Information Processing. Also discuss Newell Simon model What is decision making? Explain simon’s model for decision making Describe types of information required to different function levels of management. Short note on: a) Entropy b) Prototyping c) Deterministic and Probabilistic System d) Value of Information e) Project Management approach for MIS Explain the concept of decision making

Instructions: Font Face: Times New Roman Font Size = 14 Questions should be bold and italicized. Underline wherever required. Put diagrams where needed.

Col V.V.Jadav

Extra: A White Paper on : Text Mining and the Knowledge Management Space Factors Driving Knowledge Management The biggest business story of the 90's is the tremendous flow, or glut, of information we face every day. The fundamental nature of information has changed in terms of volume, availability and importance. With the Internet, Intranets, email and GroupWare systems more data is available to the knowledge worker than ever before. Customer comments and communications, trade publications, internal research reports and competitor web sites are just a few examples of available electronic data. Intellectual property and assets are contained within the volumes of information. Leveraging this value is increasingly important in the competitive market. Even if you do not make the most of your information, you can rest assured your competitor will. Making sense of all this information has become a difficult proposition. What information is needed and what can be done with it? Information Publishers need to differentiate their information, enhance its value and increase revenue. Information Managers need to lower the cost for tasks that require document analysis, to provide better service to their “customers” and improve the quality of management information systems. Information Users need to have direct access to relevant information, to gain rapid awareness of content, and to discover new ideas and relationships. These needs have given rise to a rapidly growing class of software products: enterprise knowledge management products. Recognizing the enormous need to manage and control great quantities of textual and other information that drive businesses, numerous vendors have entered the knowledge management market with a wide variety of products. One of the challenges with emerging, dynamic markets is that they are inherently confusing. Many vendors are eager to be in this market; all offering a wide range of products covering different parts of the overall problem. The already overburdened knowledge worker, looking into knowledge management products, must sort through a range of document management systems, text scanning products, collaborative workgroup

products, and search engines. It can be difficult to determine which products serve your needs. Knowledge Management Market Many companies define their products as knowledge management solutions. The reason is clear; everyone needs a solution for handling the large volume of unstructured information they confront each day. According to The Gartner Group, businesses have paid $1.5 billion to consultants for knowledge management advice. This is expected to grow to $5 billion by 2001. A market that size is an attractive opportunity for many companies. Knowledge management has a broad definition that evolves as new products come to market. Information Week describes knowledge management as "… the process of capturing a company's collective expertise wherever it resides —in databases, on paper, or in people's heads—and distributing it to wherever it can help produce the biggest payoff." Because they handle knowledge instead of data, knowledge management products either work with existing bodies of text or encourage collaborative work (to share the unwritten texts in individuals' heads). The tools and products in the knowledge management market include search engines, document management systems, and groupware products, among many others. One way to consider the market is from the individual knowledge worker's perspective. What kind of information are you handling, and where is it located? Do you know what you're looking for or not? Are you looking for a specific fact (something you know you need) or do you need to discover what you're missing? Do you know what information you have? Let’s look briefly at these categories as they relate to the products on the market.

Knowledge Repository

This category includes products for creating, storing and managing a corporate knowledge repository. Ideally, the users know what information is available in the repositories. File systems and document management products fit into this category, as do products which provide a means of converting information (such as text scanning products). Knowledge Sharing These products help with the collection and exchange of information in an organization. Collaborative, groupware products belong in this category. Lotus Notes is the most well-known of the groupware products. Other products in this category, such as traditional search engines, help you retrieve specific facts from the repository. An indexed search presupposes a conditioned repository of text. When effectively using a search engine you must start with a known fact or keyword to start the search process – you know what you don’t have. Knowledge Discovery This category helps you discover information when you're not exactly sure what information you have. Examples of discovery include: • • • • • •

What new markets are there for my existing products? What is really out there on my Intranet? What are my competitors doing? What do my customers think about my products and services? What are the new developments in my market? Who is doing research that might be related to my project?

Until recently, the only technique available for knowledge discovery was data mining. Data mining works with structured data, often numerical in nature, stored in a cleansed, static database (the data warehouse or data mart). This required significant pre-processing and organization efforts. Text mining is analogous to data mining in that it uncovers relationships in information. Unlike data mining, text mining works with information stored in an unstructured collection of text documents. A specific example of a text mining technique is a discovery engine. Unlike a search engine, a discovery engine does not require you to know what you have in order to find valuable information. Search vs. Discovery How do search and discovery engines compare? As shown in the diagram below, both a search engine and a discovery engine deal with words and documents.

A search engine provides the sole function of locating documents. The end user must provide key word(s) to begin the search process. While a discovery engine does provide a means of locating documents, that is not its primary value. A discovery engine automatically extracts valuable and relevant textual data and then provides a graphical, dynamic and navigable index. The visual presentation of the concepts enables rapid understanding of the underlying content and structure of textual data, leading to the improved productivity of the knowledge worker. Search engines and discovery engines are complementary solutions. Knowledge Management Market Overview The following diagram summarizes the different categories of the Knowledge Management market and gives examples of products in each category. Each of the categories provides functionality that is essential for a comprehensive Knowledge Management initiative. Why Use Text Mining? Text mining is best suited for "discovery" purposes; learning and discovering information that was previously unknown. Example reasons for using text mining include: • • • •

Uncovering a "narrative" in an unstructured mass of text Learning about a topic Exploring how a market is evolving Looking for new ideas or relations in topics

While a valuable tool, text mining is not suited to all purposes. Just as you would not use data mining technology to do a simple query of your database, text mining is not the most efficient way to isolate a single fact. Text mining is not an end in itself; it is a support tool. A text mining product supports and enhances the knowledge worker's creativity and innovation with open-ended exploration and discovery. The individual applies intelligence and creativity to bring meaning and relevance to information, turning information into knowledge. Text mining advances this process, empowering the knowledge worker to explore and gain knowledge from the knowledge base. Text mining is particularly relevant today because of the enormous amount of knowledge, either within an organization or outside of it, that resides in text documents. The advent of online publishing has enormously increased the amount of textual information. Our most frequent and most comfortable

form of formal communication—the written word—is very difficult to manage and mine. In organizations that rely on textual information, both from outside and inside the organization, working with this sea of text can become extremely difficult. The whole collection of text is simply too large to read and analyze easily. Furthermore, it changes constantly and requires ongoing review and analysis if one is to stay current. Text mining addresses these problems, giving you a tool to analyze and learn from this kind of dynamic information. Key Elements to Consider when Selecting Text Mining Solutions Since the text mining arena is rapidly evolving, the following will guide potential users in what to consider when selecting among text mining solutions. • • • • • • • •

Should not require large up front, manual categorization, tagging or building of thesauri. Delivers automatic identification and indexing of concepts within the text. Visually presents a high level view of the entire scope of the text, with the ability to quickly drill down to relevant details. Enables users to make new association and relationships, presenting paths for innovation and exploration. Integrates with popular collaborative workflow solutions. Avoids lengthy, labor-intensive integration. Scales to process any size data set quickly. Handles all types of unstructured data formats and runs on multiple formats.

What Kinds of Information Work Well with Text Mining? While text mining may work with almost any kind of information, it delivers the best results when used with information that meets the following criteria: 1. Knowledge worker value. Closely related to the previous point, text mining is meant to support the individual knowledge worker. It delivers for those who may themselves make an important contribution based on the new knowledge derived from text mining. By providing new insights and a strong foundation of understanding, text mining lets you add value to the knowledge base through innovation and decision making. 2. Text-based content. For text-mining, the information must be textual. Numerical data residing within a database structure are best served by existing data mining technologies.

3. Valuable content. The value of text mining is directly proportional to the value of the data you are mining. The more important the knowledge contained in the text collection, the more value you will derive by mining the data. 4. Explicit text. The content should be explicitly stated within the text. Scientific and technical information are good examples of explicitly stated material. 5. Unstructured. Highly structured information already resides within a navigable organization; text mining is not as valuable in those cases, provided the structure of the information makes some sense. Text mining is most useful for unorganized bodies of information, particularly those that have an ongoing accumulation and change. Bodies of text that accumulate chronologically are typically unorganized, and therefore good candidates for text mining. Fitting Text Mining into the Enterprise The goal of text mining is to empower and support the knowledge worker. It displays the relationships of concepts in text collections; it is up to the knowledge worker to provide the meaning and relevance to that information. By facilitating the transfer of information into knowledge, text mining provides a means of not only handling, but staying current with and in control of the vast amounts of information affecting your business.