Management information system

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UNIT II: Decision Support Systems (DSS) and Knowledge Management - Decision making and
information systems - systems for decision support - Executive Support Systems (ESS) - Group
Decision Support Systems (GDSS) - the process of developing DSS - individual and organizational
model - MIS and decision making concepts - GDSS – EDSS – knowledge management- enterprise-
wide knowledge management systems - knowledge work systems - intelligent techniques - Knowledge
Based Expert Systems (KBES) - general introduction to recent information system packages
DECISION MAKING AND INFORMATION SYSTEMS
Decision making is one of essential management tasks. Effective decision making is informed decision
making. Managers get informed via information systems, oral communication, and possibly in other
ways.
The word decision is derived from the 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 the choice out of several options made by the decision maker to achieve
some objective in a given situation.
Business decisions are those, which are made in the process of conducting business to
achieve its objectives in a given environment. In concept, whether we are talking about business
decisions or any other decision, we assume that the decision maker is a rational person who would
decide, with due regard to the rationality in decision making.
The major characteristics of the business decision making are:
(a) Sequential in nature.
(b) Exceedingly complex due to risks and trade offs.
(c) Influenced by personal vales
(d) Made in institutional settings and business environment.
Decision making is the process of making choices by setting goals, gathering information and
assessing alternatives.
The thought process of selecting a logical choice fromthe available options.
For effective decision making, a person must be able to forecast the outcome of each option as well,
and based on all these items, determine which option is the best for that particular situation.
A decision is about choice making.A decision maker needs to have two or more choices (options)
available and then choose (select) one of those that makes the decision.
Any decision is made for a purpose. When a manager faces some problem, she/he concentrates on it
in order to find a solution. As there is a start point (a problem) and the end point (a decision), there
must be some activities in between these. Altogether, they make a process
Once a decision is made, a decision maker needs to ensure that it will really solve the problem itwas
made for. This includes additional steps of monitoring decision effects and of adjusting the decision if
the effects are not as expected. Only when a decision really solves the problem, the problem solving
process is over.

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Decision making processes are data-intensive. A manager may need various reports, business
documents, analyses, and direct communication in order to get prepared for making effective decisions.
The scope of data coverage depends on the level of management and the problem dealt with. In
addition, decision making requires knowledge. In particular, knowledge of business is a part of
management competence.
DECISION MAKING PROCESS
MIS AND DECISION MAKING CONCEPTS
DECISION-MAKING CONCEPT:
A decision is choice out of several alternatives (options) made by the decision maker to achieve some
objective s in a given situation. Business decisions are those, which are made in the process of
conducting business to achieve its objective in a given environment. Managerial decision-making is a
control point for every managerial activity may be planning, organizing, staffing, directing, controlling
and communicating. Decision-making is the art of reasoned and judicious choice out of many
alternatives. Once decision is taken, it implies commitment of resources.
The business managers have to take variety of decision. Some are routine and others are long-term
implementation decision. Thus managerial decisions are grouped as:
(a) Strategic decision

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(b) Tactical decision
(c) Operation decision
1. Strategic Decision: these are known as major decision influence whole or major part of the
organization. Such decisions contribute directly to the achievement of common goals of the
organization; have long range effect upon the organization.
Generally, strategic decision is unstructured and thus, a manager has to apply his business judgment,
evaluation and intuition into the definition of the problem. These decisions are based on partial
knowledge of the environmental factors which are uncertain and dynamic, therefore such decision are
taken at the higher level of management.
2. Tactical Decision: tactical decision relate to the implementation of strategic decisions, directed
towards developing divisional plans, structuring workflows, establishing distribution channels,
acquisition of resources such as men, materials and money. These decisions are taken at the middle
level of management.
3. Operational Decision: operational decisions relate to day-to-day operations of the enterprise
having a short-term horizon and are always repeated. These decisions are based on facts regarding the
events and do not require much of business judgments. Operational decisions are taken at lower level of
management.
The need of information system in organization is to support the decision-making process. The
managers must be aware of problems before decision can be made. A problem exists when the real
situation is different than the expected one. After the problem has been identified, the cause of
existence of the problem must be identified and then the solution to the problem has to be found. The
decision-making process can be divided into three main phases:
(a) Intelligence: searching the environment for condition calling for decisions. The phase consists of
determining that a problem exists.
(b) Design: during this phase a set of alternative solution is generated and tested for feasibility.
(c) Choice: in this phase, the decision-maker select one of the solution identified in the design phase.
Thus, the decision process follows the sequence from intelligence to design and from design to choice.
It is possible to get back from one phase to another and whole process may be repeated. It is very
important to distinguish between programmed and non-programmed decision.
Three major reporting alternatives provided by MIS are :
Periodic scheduled reports.
E.g.: Daily,/weekly/monthly sales reports; Monthly financial statements.
Exception reports
Reports produced only when exceptional conditions occur.

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Reduces information overload.
Demand reports and responses.
Information available whenever a manager demands it.
To find and obtain customized reports as a result of their requests for the information they need. Thus,
managers do not have to wait for periodic reports to arrive as scheduled.
Decision Making and Decision Support Systems
 TYPES OF DECISIONS
1. Structured
2. Semi Structured
3. Unstructured
1. Unstructured decisions:
Are those in which the decision maker must provide judgment, evaluation and insight to solve the
problems which are non routine but important. There will be no well-understood or agreed-on
procedure for making these decisions.
2. Structured decisions
Are repetitive and routine. They involve a definite procedure for handling and need not be treated as if
they were new.
3. Semi structured decisions
Many decisions have elements of both Structured and Unstructured decisions, where only part of the
problem has a clear cut answer provided by an accepted procedure.
Levels of Decision Making
The levels of decision-making are:
Strategic Decision Making: These decisions are usually concerned with the major objectives of the
organization, such as ―Do we need to change the core business we are in?‖ They also concern policies
of the organization, such as ―Do we want to support affirmative action?‖
Management Control: These decisions affect the use of resources, such as ―Do we need to find a
different supplier of packaging materials?‖ Management-level decisions also determine the

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performance of the operational units, such as ―How much is the bottleneck in Production affecting the
overall profit and loss of the organization, and what can we do about it?‖
 Knowledge-Level Decision Making: These decisions determine new ideas or improvements to
current products or services. A decision made at this level could be ―Do we need to find a new
chocolate recipe that result in a radically different taste for our candy bar?‖
 Operational control: These decisions determine specific tasks that support decisions made at the
strategic or managerial levels.
Models of Decision Making
1. Rational Models /Individual model
An individual‘s management identifies goals, ranks all possible alternatives actions and chooses the
alternatives that contributes most to those goals.
2. Organizational Model
Considers the structural and political characteristics of an organization.
3. Bureaucratic Model
Whatever organization do is the result of routines and existing business process developed over years
of active use.
4. Political Model
What an organization does is a key result of political bargains struck among key leaders and interest
groups.
DECISION SUPPORT SYSTEMS
A decision support system (DSS) is a computer-based information system that supports business
or organizational decision-making activities. DSSs serve the management, operations, and planning

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levels of an organization and help to make decisions, which may be rapidly changing and not easily
specified in advance. DSSs include knowledge-based systems. A properly designed DSS is an
interactive software-based system intended to help decision makers compile useful information from a
combination of raw data, documents, personal knowledge, or business models to identify and solve
problems and make decisions.
• Decision Support Systems (DSS) help executives make better decisions by using historical and
current data from internal Information Systems and external sources. By combining massive amounts
of data with sophisticated analytical models and tools, and by making the system easy to use, they
provide a much better source of information to use in the decision-making process.
• DSS are interactive computer-based systems and subsystems intended to help decision makers
use communications technologies, data, documents, knowledge and/or models to successfully
complete decision process tasks.
• DSS is an application of Hebert Simon model, as discussed, the model has three phases :
i) Intelligence
ii) Design
iii) Choice
The DSS basically helps in the information system in the intelligence phase where the objective is to
identify the problem and then go to the design phase for solution. The choice of selection criteria varies
from problem to problem.
It is therefore, required to go through these phases again and again till satisfactory solution is found. In
the following three phase cycle, you may use inquiry, analysis, and models and accounting system to
come to rational solution.
These systems are helpful where the decision maker calls for complex manipulation of data and use of
several methods to reach an acceptable solution using different analysis approach. The decision support
system helps in making a decision and also in performance analysis. DSS can be built around the rule
in case of programmable decision situation. The rules are not fixed or predetermined and requires every
time the user to go through the decision making cycle as indicated in Herbert Simon model.
Computer based information systems that provide interactive information support to
managers and business professionals during the decision making process.
DSS use
1) Analytical models. (Mathematical and Statistical models)
2) Specialized databases - collections on particular subjects.
E.g.: company financial data, court decisions, census data, patents, medical journal article abstracts etc.
3) A decision makers own insights and judgments; and
4) An interactive computer based modeling process to support semi-structured business decisions

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 Programmed and Non-programmed Decisions
There are two types of decisions - programmed and non-programmed decisions.
Programmed decisions are basically automated processes, general routine work, where:
 These decisions have been taken several times.
 These decisions follow some guidelines or rules.
For example, selecting a reorder level for inventories, is a programmed decision.
Non-programmed decisions occur in unusual and non-addressed situations, so:
 It would be a new decision.
 There will not be any rules to follow.
 These decisions are made based on the available information.
 These decisions are based on the manger's discretion, instinct, perception and judgment.
For example, investing in a new technology is a non-programmed decision.
Decision support systems generally involve non-programmed decisions. Therefore,
there will be no exact report, content, or format for these systems. Reports are generated on the fly.
 Attributes :
i) DSS should be adaptable and flexible.
ii) DSS should be interactive and provide ease of use.
iii) Effectiveness balanced with efficiency (benefit must exceed cost).
iv) Complete control by decision-makers.
v) Ease of development by (modification to suit needs and changing environment) end users.
vi) Support modeling and analysis.
vii) Data access.
viii) Standalone, integration and Web-based
 DSS Characteristics :
i) Support for decision makers in semi structured and unstructured problems.
ii) Support managers at all levels.
iii) Support individuals and groups.
iv) Support for interdependent or sequential decisions.
v) Support intelligence, design, choice, and implementation.
vi) Support variety of decision processes and styles

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 Discuss in brief the Hebert A. Simon ‘Decision Support System Model’. Define the term
Intelligence, Design and Choice as Model.
OR
Discuss the essential steps in process of decision making.
Ans.: There are three phases in Hebert Simon model :
Hebert Simon Model
INTELLIGENCE
DESIGN
CHOICE
Intelligence: In this phase MIS collects the raw data. Further the data is sorted and merged with other
data and computation are made, examined and presented. In this phase, the attention of the manager is
drawn to the entire problem situation, calling for a decision.
Design: Manager develops a model of problem situation on which he can generate and test,
summarizing the different decision alternatives and test the feasibility of implementation. Assess the
value of the decision outcome.
Choice: In this phase the manager evolves a selection criterion and selects one alternative as decision
based on selection criteria.
In these three phases if the manager fails to reach a decision, he starts the process all over again from
intelligence phase where additional data and information is collected, the decision making process is
refined, the selection criteria is changed and a decision is arrived at.
Typical information that a decision support application might gather and present are:
 Inventories of information assets (including legacy and relational data sources, cubes, data
warehouses, and data marts),
 Comparative sales figures between one period and the next,
 Projected revenue figures based on product sales assumptions.
Passive DSS, • Active DSS, • Cooperative DSS

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 COMPONENTS OF DSS
Following are the components of the DSS:
 Data management sub system: Data management sub system includes a database that contains
relevant data for the situation and is managed by software called Database management system
(DBMS).
Data management sub system is composed of the following elements:
i. DSS database
ii. Database management system
iii. Data directory
iv. Query facility
 Model management sub system: this is a software packages that includes financial, statistical,
management science or quantitative models that provide the systems analytical capabilities and
appropriate software management
Model management sub system is composed of the following elements:
i. Model
ii. Model base management system
iii. Modelling language
iv. Model directory
v. Model execution, integration and command processor.
 User interface sub system: the user communicates with and commands the DSS through the
sub system. The user is considered part of the system.
 Knowledge base management sub system: this sub system can support any of the other sub
systems or act as an independent component.
 Benefits
• Improves personal efficiency
• Speed up the process of decision making
• Increases organizational control
• Encourages exploration and discovery on the part of the decision maker
• Speeds up problem solving in an organization
• Facilitates interpersonal communication
• Promotes learning or training
• Generates new evidence in support of a decision
• Creates a competitive advantage over competition
• Reveals new approaches to thinking about the problem space

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• Helps automate managerial processes
 Types of Decision Support System
The various types of DSS are:
1.  Data driven DSS
2.  Model driven DSS
3.  Knowledge driven DSS
4.  Documentdriven DSS
5.  Communications driven and Group DSS
6.  Inter-organizational and Intra-organizational DSS
7.  Function specific or General purpose DSS
1. Data-Driven DSS Notes
Data-Driven DSS take the massive amounts of data available through the company‘s TPS and
MIS systems and cull from it useful information which executives can use to make more informed
decisions. They don‘t have to have a theory or model but can ―free-flow‖ the data.
The first generic type of Decision Support System is a Data-Driven DSS. These systems include
file drawer and management reporting systems, data warehousing and analysis systems, Executive
Information Systems (EIS) and Spatial Decision Support Systems. Business Intelligence Systems are
also examples of Data-Driven DSS. Data- Driven DSS emphasize access to and manipulation of large
databases of structured data and especially a time-series of internal company data and sometimes
external data. Simple file systems accessed by query and retrieval tools provide the most elementary
level of functionality. Data warehouse systems that allow the manipulation of data by computerized
tools tailored to a specific task and setting or by more general tools and operators provide additional
functionality. Data-Driven DSS with Online Analytical Processing (OLAP) provide the highest level of
functionality and decision support that is linked to analysis of large collections of historical data.
• Data driven DSS helps users to extract useful information from available data by identifying
related patterns and helps taking decision. Data driven DSS uses OLAP (Online Analytical
Processing) Tools, Data Mining Tools to analyze large pools of data and extract related
information.
2. Model-Driven DSS
A second category, Model-Driven DSS, includes systems that use accounting and financial models,
representational models, and optimization models. Model-Driven DSS emphasize access to and
manipulation of a model. Simple statistical and analytical tools provide the most elementary level of
functionality. Some OLAP systems that allow complex analysis of data may be classified as hybrid
DSS systems providing modeling, data retrieval and data summarization functionality.
Model-Driven DSS use data and parameters provided by decision-makers to aid them in analyzing a
situation, but they are not usually data intensive. Very large databases are usually not needed for
Model-Driven DSS.
Model-Driven DSS were isolated from the main Information Systems of the organization and were
primarily used for the typical ―what-if‖ analysis. That is, ―What if we increase production of our

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products and decrease the shipment time?‖ These systems rely heavily on models to help executives
understand the impact of their decisions on the organization, its suppliers, and its customers.
• It consists of models such as Statistical Models that helps in establishing relationships, such as
relating product sales to differences in age or income.
• Optimization Models that is used to determine how efficiently and effectively operations can be
performed.
• Mathematical Models to make some mathematical calculations.
• Forecasting Models that is used to forecast sales.
• These models analyze the data available and help users to evaluate alternatives.
3. Knowledge-Driven DSS
The terminology for this third generic type of DSS is still evolving. Currently, the best term seems to be
Knowledge- Driven DSS. Adding the modifier ―driven‖ to the word knowledge maintains a parallelism
in the framework and focuses on the dominant knowledge base component. Knowledge-Driven DSS
can suggest or recommend actions to managers. These DSS are personal computer systems with
specialized problem-solving expertise. The ―expertise‖ consists of knowledge about a particular
domain, understanding of problems within that domain, and ―skill‖ at solving some of these problems.
A related concept is Data Mining. It refers to a class of analytical applications that search for hidden
patterns in a database. Data mining is the process of sifting through large amounts of data to produce
data content relationships.
4. Document-Driven DSS
A new type of DSS, a Document-Driven DSS or Knowledge Management System, is evolving to help
managers retrieve and manage unstructured documents and Web pages. A Document- Driven DSS
integrates a variety of storage and processing technologies to provide complete document retrieval and
analysis. The Web provides access to large document databases including databases of hypertext
documents, images, sounds and video.
Example: Documents that would be accessed by a Document-Based DSS are policies and procedures,
product specifications, catalogs, and corporate historical documents, including minutes of meetings,
corporate records, and important correspondence. A search engine is a powerful decision-aiding tool
associated with a Document-Driven DSS.
5. Communications-Driven and Group DSS
Group Decision Support Systems (GDSS) came first, but now a broader category of Communications-
Driven DSS or groupware can be identified. This fifth generic type of Decision Support System
includes communication, collaboration and decision support technologies that do not fit within those
DSS types identified. Therefore, we need to identify these systems as a specific category of DSS. A
Group DSS is a hybrid Decision Support System that emphasizes both the use of communications and
decision models. A Group Decision Support System is an interactive computer-based system intended
to facilitate the solution of problems by decision-makers working together as a group. Groupware
supports electronic communication, scheduling, document sharing, and other group productivity and
decision support enhancing activities
6. Inter-Organizational or Intra-Organizational DSS

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A relatively new targeted user group for DSS made possible by new technologies and the rapid growth
of the Internet is customers and suppliers. We can call DSS targeted for external users an Inter-
organizational DSS. The public Internet is creating communication links for many types of inter-
organizational systems, including DSS. An Inter-Organizational DSS provides stakeholders with access
to a company‘s intranet and authority or privileges to use specific DSS capabilities. Companies can
make a Data-Driven DSS available to suppliers or a Model-Driven DSS available to customers to
design a product or choose a product. Most DSS are Intra-Organizational DSS that are designed for use
by individuals in a company as ―standalone DSS‖ or for use by a group of managers in a company as a
Group or Enterprise-Wide DSS.
7. Function-Specific or General Purpose DSS
Many DSS are designed to support specific business functions or types of businesses and industries.
We can call such a Decision Support System a function-specific or industry- specific DSS. A Function-
Specific DSS like a budgeting system may be purchased from a vendor or customized in-house using a
more general-purpose development package. Vendor developed or ―off-the shelf. DSS support
functional areas of a business like marketing or finance; some DSS products are designed to support
decision tasks in a specific industry like a crew scheduling DSS for an airline. A task-specific DSS has
an important purpose in solving a routine or recurring decision task. Function or task-specific DSS can
be further classified and understood in terms of the dominant DSS component that is as a Model-
Driven, Data-Driven or Suggestion DSS. A function or task-specific DSS holds and derives knowledge
relevant for a decision about some function that an organization performs (e.g., a marketing function or
a production function). This type of DSS is categorized by purpose; function-specific DSS help a
person or group accomplish a specific decision task. General-purpose DSS software helps support
broad tasks like project management, decision analysis, or business planning.
 ROLE OF DSS IN BUSINESS
The roles of DSS are as follows:
What if analysis: in what if analysis an end user makes changes to variables or relationships among
variables and observes the resulting changes in the values of other variable.
Goal oriented: it is a process of determining the input values required to achieve a certain goal.
Risk analysis: risk is important factor which affects the business enterprises. It can be classified as low,
medium and high risk. A DSS is particularly useful in medium risk and high risk environments.
Model building: DSS allows decision markets to identify the most appropriate model for solving the
problems.
Graphical analysis: this helps managers to quickly digest larger volumes of data and visualize the
impact of various courses of action. They recommend the use of graph when:
Seeking a quick summary of data.
Forecasting activities
Detecting trends overtime
Composing points and patterns at different variables.

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 APPLICATIONS OF DSS
Application of a DSS can be classified into following three categories:
i. Independent problems- the independent problems are ―Standalone problems‖ whose solutions
are independent of other problems. The goal is to find the best solution to the given problem.
ii. Interrelated problem- in interrelated problems solutions are interrelated by each other to find
the most effective solution to the group of interrelated problem. These types of problems usually
require team effort.
iii. Organizational problems- in Organizational problems all departments within an organisation
are included. Such problem required team effort. TQM is a good example of an organizational
effort because for it to be effective it requires a joint effort from all departments units in the
organisation.
 ADVANTAGES AND DISADVANTAGES OF DSS
Advantages:
1. Improving personal efficiency: many DSS do not do anything. A person could not do himself or
herself. People prepared budgets for centuries before spreadsheet software came in to use. DSS help
them do it faster and with less change of error.
2. Improving problem solving: a DSS can make it possible for a person or a group to solve problem
faster or better, than they could without it.
3. Facilitating communications: after found that DSS facilitating interpersonal communication in
several ways. In addition technology developments that have occurred since his or her research have
opened up for DSS to provide this benefit.
4. Promoting learning or training: using a DSS can also help people learned more about using
computers and about software package that are in the DSS although this is seldom a specific
objective of developing the DSS it can be valuable by project.
5. Increasing organizational control: some DSS can also control information about an individual‘s
decision to his or her managers
Disadvantages:
1. Limited storage capability: due to its small memories and limited storage capabilities, DSS has
definite computational constraints.
2. Slow: it is slow compared to the speed of large mainframes.

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3. Limited information sharing: most DSSs are designed for individual use but they can be designed
so that several computers can be linked for limited information sharing.
4. Difficult: it is difficult to know interdependencies of functions provided by system.
5. Require extensive knowledge: there are applications that require extensive knowledge of specific
problem domain or technical knowledge.
6. Translation problems: users have to deal with several databases and model each with different
data models and resulting translation problems.
7. Confliction: users may have to work on several decision scenarios at same time. As a consequence
they have to keep track of what they done for each of them
 Type of Tools/Models
The decision support system can be based on the different types of tools and models.
1. Behavioral models
These models are useful in understanding the behavior amongst the business variables.
The decision maker can then make decisions giving due regard to such behavioral relationships.
The trend analysis, forecasting, and the statistical analysis models belong to this category. The
trend analysis indicates how different variable behave in trend setting in the past and hence in the
future. A regression models shows the correlation between one or more variables. It also helps in
identifying the influence of one variable on the other.
These types of models are largely used in process control, manufacturing, agricultural sciences,
medicines, psychology and marketing. The behavioral analysis can be used to set the points for alert,
alarm and action for the decision maker.
2. Management science models
These models are developed on the principles of business management, accounting and econometrics.
In many areas of management, the proven methods of management control are available which can be
used for the management decision. There are also several management systems, which can be
converted into the decision support system models.
For example, the budgetary systems, the cost accounting systems, the system of capital budgeting
for better return on the investment, the ABC analysis, the control of inventory through the maximum-
minimum levels, the MRP system, etc., are the examples of the use of the management science in the
materials management.
Production planning and control scheduling and loading systems are the examples in Production
Management. Manpower planning and forecasting is the example in Personnel Management.
Some of these models can be used straight away in the design of the decision support system. While
some others require the use of management principles and practices, most of the procedure based
decision making models belong to this category.
One can develop a model for selection of vendor for procurement of an item, based on the complex
logical information scrutiny. Such models take away the personal bias of the decision maker.
2. Operations research (OR) models

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The Operations Research (OR) models are mathematical models. These models represent a real life
problem situation in terms of the variables, constants and parameters expressed in algebraic equations.
Since, the models are mathematical; there is solution to these problems. In arriving the solution,
methods of calculus, matrix algebra, probability, and set theory are used. These models have clarity to
the extent that each of them has a set of assumptions which must be true in real life. Further, if the
assumptions are valid, the solutions offered are realistic and practical; the model represents the real life
problem situation.
The OR models address themselves to the resources usage optimization, by balancing two or more
aspects of the decision situation. The efforts are made to find the optimum solution. In business and
industry, there are a number of situations where one type of cost is controlled, the other cost goes up.
This play between the two costs has to be balanced at a point, which is known as an optimum point.
The OR models generally try to find a solution which maximize or minimize certain aspects of
business, under the conditions of constraints. In manufacturing business, the maximization of profit
with an appropriate product mix, within the capacity and the market constraint, is a common problem.
The allocation of an inventory to the various destinations with the least transportation costs is another
well known problem. The minimization of capital blocked in the inventory and simultaneously meeting
the market demand or the production requirement is also a problem constantly met with. The inventory
control models offer an optimum solution, where the cost of inventory and the cost of ordering or set up
are balanced.
In facility designing problem, the cost of facility, its running cost, the idle time of the facility, and
the waiting time of the customer are considered. These problems are solved by application of the
Queuing Theory. The theory considers two costs, namely, the cost of waiting time of customer and the
cost of idle time of the facility and decide on the facility design with a predetermined service standard.
Some problem do not precisely fall in the category of the standard OR models. In such cases, the
problems are solved by using a simulation approach.
This approach uses a random occurrence of a large number of events, determines the status of the
system and evaluates its cost of operations. The simulation techniques help to assess the quality of the
facility design before the investment is made in building such facility.
SYSTEMS FOR DECISION SUPPORT
▪ Decision support content of different types of information systems
a. Decision Support Systems
b. Executive Information Systems
c. Expert Systems
d. Information Reporting Systems
e. Workgroup Information Systems
f. Personal Information Systems
g. Office Information Systems
h. Transaction Processing Systems

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EXECUTIVE SUPPORT SYSTEMS (ESS) / ENTERPRISE INFORMATION
SYSTEMS (EIS)
Executive Information Systems are strategic-level information systems that are found at the top of the
Pyramid. They help executives and senior managers analyze the environment in which the organization
operates, to identify long-term trends, and to plan appropriate courses of action. The information in
such systems is often weakly structured and comes from both internal and external sources. Executive
Information System are designed to be operated directly by executives without the need for
intermediaries and easily tailored to the preferences of the individual using them.
• Combines many of the features of MIS and DSS.
• The first goal of executive information systems was to provide top executives with immediate
and easy access to information about a firm‘s critical success factors (CSFs), that is, key factors
that are critical to accomplishing an organization‘s strategic objectives.
• For example, the executives of a retail store chain would probably consider factors such as its e-
commerce versus traditional sales results or its product line mix to be critical to its survival and
success.
Definition:
According to Matthews and Shoe Bridge, ―EIS is a computer based information delivery and
communication system designed to support the needs of top executives‖.
 Characteristics of EIS:
The main characteristics of EIS are as follows:
Drill down capabilities: This capacity of an EIS allows the executives look for details on any specific
information. Each level of detail that is accessed by the user may involve submenus if the system is
menu driven.
Designed with management critical success factors in mind: every organisation has certain critical
factors that are important for achieving the organizational goals.
Status access, trend analysis, and exception reporting: this feature allows executives to access the
current executives to examine. The timing and relevance of information is very important.
Personalized analysis: This capability of an EIS allows executives to use built in functions to analyze
problematic situations.
Navigation of information: This feature allows the executives to access large amounts of data in a
quick and efficient manner.
 Functions of an EIS

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EIS organizes and presents data and information from both external data sources and internal MIS or
TPS in order to support and extend the inherent capabilities of senior executives.
• In an EIS, information is presented in forms tailored to the preferences of the executives using
the system.
• Information presentation methods used by EIS mostly include graphics displays, exception
reporting and trend analysis, usually customized to the information preferences of executives
using the EIS.
• The ability to drill down, which allows executives to retrieve displays of related information
quickly at lower levels of detail, is another important capability.
 Capabilities of executive support system (ESS)
An effective ESS should have the following capabilities:
-Support for defining an overall vision: one of the key roles of senior executive is to provide a broad vision
for the entire organisation.
-Support for strategic planning: EIS also support strategic planning. It is also planning the acquisition of
new equipment, analyzing merger possibilities and making difficult decisions concerning downsizing and
the sale of assets if required by unfavorable economic conditions.
-Support for strategic organizing and staffing: top level executive are concerned with organizational
structure, overall direction for staffing decisions and effective communication with labour unions are also
major decision areas for top level executives.
-Support for strategic control: another type of executive decision relates to strategic control, which involves
monitoring and managing the overall operation of the organisation.

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-Support for crisis management: even with careful strategic planning a crisis can occur. Major disasters,
include hurricane, tornadoes, floods, earthquakes, fires and terrorist activities can totally shut down major
parts of organisation.
 The role of EIS
o Are concerned with ease of use
o Are concerned with predicting the future
o Are effectiveness oriented
o Are highly flexible
o Support unstructured decisions
o Use internal and external data sources
o Used only at the most senior management levels
EIS components can typically be classified as:
 Hardware
 Software
 User interface
 Telecommunications
EIS helps executives find 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 evaluating 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
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-to-day operations, and
the central idea of this process is effectiveness and efficiency.
Marketing
In an organization, marketing executives‘ duty is managing available marketing resources to create a
more effective future. For this, they need make judgments about risk and uncertainty of a project and its
impact on the company in short term and long term. To assist marketing executives in making effective
marketing decisions, an EIS can be applied. EIS provides sales forecasting, which can allow the market
executive to compare sales forecast with past sales. EIS also offers an approach to product price, which

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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.
Financial
Financial analysis is one of the most important steps to companies today. Executives needs to use
financial ratios and cash flow analysis to estimate the trends and make capital investment decisions. An
EIS integrates planning or budgeting with control of performance reporting, and it can be extremely
helpful to finance executives. EIS focuses on financial performance accountability, and recognizes the
importance of cost standards and flexible budgeting in developing the quality of information provided
for all executive levels.
 EIS critical success factors.
▪ A committed and informed executive sponsor: a top level executive, preferable the CEO should
serve as the executive sponsor of the EIS by encouraging its implementation.
▪ An operating sponsor: the executive sponsor will most likely be too busy to devote much time to
implementation.
▪ An appropriate information services staff: information specialist should be available who
understand not only the information technology but also how the executive will use the system.
▪ Appropriate information technology`: EIS implements should not get carried away and incorporate
unnecessary hardware and software.
▪ Data management: it is not sufficient to simply display the data or information. The executive should
have some idea of how current the data is. The analysis can be accomplished by drill down by
following up with data managers or both.
▪ A clear link to business objectives: most successful EIS are designed to solve specific problems or
meet needs that can be addressed with information technology.
▪ Management of organizational resistance: when an executive resists the EIS efforts should be taken
to gain support. A good strategy is to identify a single problem that the executive faces and then
quickly implement an EIS using prototyping to address that problem.
Management of the spread and evolution of the system: experience has shown that when upper level
management begins receiving information from the EIS lower level managers want to receive the same
output
 Advantages and disadvantages of EIS

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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 understood
 EIS provides timely delivery of information. Management can make decisions promptly.
 Improves tracking information
 Offers efficiency to decision makers
 Ability to analyze trends
 Augmentation of managers leadership capabilities
 Enhanced personal thinking and decision making
 Contribution to strategic control flexibility
 Ease access to existing information
 Instruments of change
 Better reporting system
 Better understanding of enterprise operations.
Disadvantages:
 System dependent
 Limited functionality, by design
 Information overload for some managers
 Benefits hard to quantify
 High implementation costs
 System may become slow, large, and hard to manage
 Need good internal processes for data management
 May lead to less reliable and less secure data
 Functions are limited cannot perform complex calculations.
 Hard to quantify benefits and to justify implementation of an EIS.
 Executives may encounter information overload.
 System may become slow, large, and hard to manage.
 Difficult to keep current data.
 Small companies may encounter excessive costs for implementation.

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GROUP DECISION SUPPORT SYSTEMS (GDSS)
More and more, companies are turning to groups and teams to get work done. Hours upon hours are
spent in meetings, in group collaboration, in communicating with many people. To help groups make
decisions, a new category of systems was developed—the group decision support system (GDSS).
A group decision support system (GDSS) is an interactive computer based system that facilitates
a number of decision-makers (working together in a group) in finding solutions to problems that are
unstructured in nature. They are designed in such a way that they take input from multiple users
interacting simultaneously with the systems to arrive at a decision as a group.
The tools and techniques provided by group decision support system improve the quality and
effectiveness of the group meetings. Groupware and web-based tools for electronic meetings and
videoconferencing also support some of the group decision making process, but their main function is
to make communication possible between the decision makers.
i.e.
 GDSS is an interactive, computer based system used to facilitate the solution of unstructured problems
by a set of decision makers working together as a group. GDSS makes meeting more productive by
providing tools to facilitate planning, generating, organizing & evaluating ideas, establishing priorities
and documenting meeting proceedings which internally increases value in business.
GDSS have Hardware, Software &People components.

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Model of GDSS
List of elements that GDSS use to help organizations are:
Preplanning: A clear-cut agenda of the topics for the meeting.
Open, collaborative meeting atmosphere: Free flow of ideas and communications without any of the
attendees feeling shy about contributing.
Evaluation objectivity: Reduces ―office politics‖ and the chance that ideas will be dismissed because
of whom presented them instead of what was presented.
Documentation: Clear communication about what took place and what decisions were made by the
group.
Preservation of “organizational memory”: Even those unable to attend the meeting will know what
took place; great for geographically separated team members.

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 COMPONENTS OF GDSS
A GDSS is composed of 3 main components; namely hardware, software tools, and people.
1) Hardware:
It includes electronic hardware like computer, equipment used for networking, electronic display
boards and audio visual equipment.
It also includes the conference facility, including the physical setup – the room, the tables and the
chairs – laid out in such a manner that they can support group discussion and teamwork.
2) Software tools used in GDSS :
Electronic Questionnaire: The information generated using the questionnaires helps the organizers
of the meeting to identify the issues that need immediate attention, thereby enabling the organizers to
create a meeting plan in advance.
Electronic Brainstorming Tools: It allows the participants to simultaneously contribute their ideas
on the subject matter of the meeting. As identity of each participant remains secret, individuals
participate in the meeting without the fear of criticism.
 Idea Organizer: It helps in bringing together, evaluating and categorizing the ideas that are
produced during the brainstorming activity.
Tools for Setting Priority: It includes a collection of techniques, such as simple voting, ranking in
order and some weighted techniques that are used for voting and setting priorities in a group meeting.
 Policy Formation Tool: It provides necessary support for converting the wordings of policy
statements into an agreement.
3) People:
 It compromises the members participating in the meeting, a trained facilitator who helps with the
proceedings of the meeting and an expert staff to support the hardware and software.
The GDSS components together provide a favorable environment for carrying out group meetings.
 GDSS Characteristics and Software tools
persware
hardware software
In GDSS the hardware includes more than just computers and peripheral equipment. It also includes the
conference facilities, audiovisual equipment, and networking equipment that connect everyone.
The persware extends to the meeting facilitators and the staff that keeps the hardware
operating correctly. As the hardware becomes more sophisticated and widely available, many
companies are bypassing specially equipped rooms in favor of having the group participants ―attend‖
the meeting through their individual desktop computers. Many of the software tools and programs

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discussed, Groupware, can also be used to support GDSS. Some of these software tools are being
reworked to allow people to attend meetings through Intranets or Extranets.
Some highlights:
 Electronic Questionnaires: Set an agenda and plan ahead for the meeting.
 Electronic Brainstorming: Allows all users to participate without fear of reprisal or criticism.
 Questionnaire Tools: Gather information even before the meeting begins, so facts and information
are readily available.
 Stakeholder Identification:Determines the impact of the group‘s decision.
 Group Dictionaries: Reduce the problem of different interpretations.
 Features of GDSS
1) Ease of Use:
It consists of an interactive interface that makes working with GDSS simple and easy.
2) Better Decision Making:
It provides the conference room setting and various software tools that facilitate users at different
locations to make decisions as a group resulting in better decisions.
3) Emphasis on Semi-structured and Unstructured Decisions:
It provides important information that assists middle and higher level management in making semi-
structured and unstructured decisions.
4) Specific and General Support:
The facilitator controls the different phases of the group decision support system meeting (idea
generation, discussion, voting and vote counting etc.) what is displayed on the central screen and the
type of ranking and voting that takes place, etc. In addition, the facilitator also provides general support
to the group and helps them to use the system.
5) Supports all Phases of the Decision Making:
It can support all the four phases of decision making, viz intelligence, design, choice and
implementation.
6) Supports Positive Group Behavior:
In a group meeting, as participants can share their ideas more openly without the fear of being
criticized, they display more positive group behavior towards the subject matter of the meeting
Most versions of GDSS use special meeting rooms where each participant is seated at a
networked computer. A facilitator operates the network and keeps the discussion moving in the right
direction. Before the meeting, the primary decision maker meets with the facilitator to establish the
objective of the meeting. They setup sample questions and design the overall strategy.
Typical meetings begin with a brainstorming session, where participants are asked to think
of ideas, problems and potential solutions. They type each of these into categories on their computers.
The basic ideas and suggestions are stored in a database and shared with the group through the
networked computers.
In terms of discussions and comments, the facilitator can choose individual items and project
them on the screen for the entire group to analyze. Participants can write comments or criticisms of any

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idea at any time. This system is particularly helpful if many participants come up with many ideas and
comments at the same time. The computer enables everyone to enter comments at the same time, which
is faster than waiting for each person to finish speaking.
Another feature of using the computer for the entry of ideas and comments is that they can
be anonymous. Although each comment is numbered, they are not traced back to the original author, so
people are free to criticize their supervisor‘s ideas. Anonymity reduces embarrassment and encourages
people to submit riskier ideas.
At various points, the facilitator can call for participants to vote on some of the ideas and
concepts. Depending on the software package, there can be several ways to vote. In addition to
traditional one-vote methods, there are several schemes where you place weights on your choices. The
votes are done on the computer and results appear immediately. Because it is so easy to vote, the GDSS
encourages the group to take several votes. This approach makes it easier to drop undesirable
alternatives early in the discussion.
One useful feature of conducting the meeting over a computer network is that all of the
comments, criticisms, and votes are recorded. They can all be pointed at the end of the session.
Managers can review all of the comments and add them to their reports.
In theory, a meeting could be conducted entirely on a computer network, saving costs
and travel time if the participants are located in different cities. Also, if it is designed properly, a GDSS
can give each participant access to the corporate data while he or she is in the meeting. If a question
rises about various facts, the computer can find the answer without waiting for a second meeting.
 Goals of GDSS
The goals of GDSS are:
Mitigate the Problems of Group Work:
 Social pressures of conformity may result in ―groupthink‖.
 Lack ofco-ordination of work and poor planning of meetings.
 Inappropriate influence of group dynamics.
 Tendency of group members to rely on others to do most of the work.
 Tendency toward compromised solutions of poor quality.
 Social ―loafing‖
 Tendency to repeat what was already said.
 Larger costs of making decisions.
 Tendency of group to take riskier decisions than they should.
 Incomplete or inappropriate use of information.
 Inappropriate representation in group.
Accentuate the Benefits of Group Work:
 Groups are better than individuals at understanding problems.
 Groups are better than individuals at catching errors.
 A group has more knowledge/information than any one member.
 Working in a group may stimulate the participants and the process.
 The participation of the members in a decision means less likelihood to resist

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implementation.
 People are accountable for the decisions that they participate in.
Support Multiple Group Processes:
 Provide methods that aid the decision and judgmentprocess.
 Provide access to rules that will aid the choice between alternatives.
 Provide methods for reconciling conflict.
 Limitations of GDSS
Perhaps the greatest drawback to a GDSS is that it requires participants to type in their ideas, comments
and criticisms. Most people are used to meetings based on oral discussions. Even if they have adequate
typing skills, a GDSS can inhibit some managers.
Along the same lines, in a traditional meeting, only one person speaks at a time, and everyone
concentrates on the same issue at the same time. With a GDSS your focus is continually drawn to the
many different comments and discussions taking place at the same time. People who type rapidly and
fit from topic to topic will find that they can dominate the discussions.
In terms of costs, maintaining a separate meeting room with networked computers can be
expensive. Unless the facility is used on a regular basis, the computers will be idle a great deal of the
time. When you factor in the costs for network software, the GDSS software, and other utilities, the
costs multiply. One way to minimize this problem is to lease the facilities that have been established by
a couple of universities and some companies.
The use of GDSS also requires a trained facilitator – someone who can lead discussions, help
users, and control the GDSS software on the network. Hiring an in-house specialist can be very
expensive of there are only a few meetings a year. Again, using facilities are scrupulously honest; there
might be some topics that you do not want to discuss with non-employees.
One way to overcome these limitations is to alter the approach to the meetings. Instead of
requiring everyone to get together at the same time in on room, meetings could be held via network
discussion groups. Each participant could read the messages, add comments, and vote on issues
electronically at any time from any location. Again, the internet offers possibilities to provide these
facilities, but it could be a few years before organizations and managers can accept the changes
required.
 How GDSS is used in the meeting.
It is an electronic meeting.
 Each person is having a workstation.
 These workstations are connected to manager‗s workstationand also to the file server.
 The ideas are generated through brainstorming sessions.
The data or ideas that are sent by each person from their respective workstations are
saved in file server and the person‗s views and opinions are kept confidential.
 Prioritization and voting of ideas is performed and the solution to the problem is achieved. Ideas
generated are used to determine what impact they are having on stakeholders.

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 Finally policy is formulated on ideas generated and it is made in use.
Thus GDSS helps managers working together as a group to make decision by finding solution to
unstructured problems in a fast and easier way.
THE PROCESS OF DEVELOPING DSS
Steps in constructing a DSS:
1) Choosing the project or problem to be solved.
2) Selecting hardware and software.
3) Data acquisition and management.
4) Model subsystem acquisition and management.
5) Dialogue subsystem and its management.
6) Knowledge component.
7) Packaging.
8) Testing, evaluation and improvement.
9) User training.
10) Documentation and maintenance.
11) Adaptation.
Figure shows a recommended process hierarchy for DSS design and development. The process
begins with decision-oriented diagnosis and feasibility analysis and then moves to in-house or
outsourced development of the proposed Decision Support System using one of three development
approaches
Systems Development Life Cycle Approach The systems development life cycle (SDLC) approach is
based on a series of formal steps, including the following seven steps:

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1) Confirm user requirements;
2) Systems analysis;
3) System design;
4) Programming;
5) Testing;
6) Implementation; and
7) Use and Evaluation.
Rapid Prototyping All of the different versions of rapid prototyping accommodate and even
encourage changes in the requirements of a proposed Decision Support System. A typical prototyping
methodology usually includes five steps:
1. Identify user requirements.
2. Develop a first iteration DSS prototype.
3. Evolve and modify the next iteration DSS prototype.
4. Test DSS and return to step 3 if needed.
5. Full-scale implementation.
Prototyping evolved in response to perceived deficiencies and limitations of the SDLC approach. In a
prototyping development approach, DSS analysts sit down with potential users and develop
requirements. These requirements are specified in general terms and should evolve from the decision-
oriented diagnosis and design. The analyst then develops a prototype of a system that appears to work.
DSS analysts use tools such as Database Management Systems and DSS application generators that
support rapid development
End-User DSS Development End-user development of DSS puts the responsibility for building and
maintaining a DSS on the manager who builds it. Powerful end-user software is available to managers
and many managers have the ability and feel the need to develop their own desktop DSS. Managers
frequently use spreadsheets, like Microsoft Excel and Lotus 1-2-3, as DSS development tools. Using a
spreadsheet package, managers can analyze an issue like the impact of different budget options.
Following the analysis, managers select the alternative that best meets their department's needs. Also,
managers can develop tools to help them conduct market analyses and make projections and forecasts
at their desktop. The major advantage of encouraging end-user DSS development is that the person who
wants computer support will be involved in creating it. The manager/builder controls the situation and
the solution that is developed. End-user DSS development can also sometimes result in faster
development and cost savings.
EDSS EXECUTIVE DECISION SUPPORT SYSTEM
An executive decision support system is a type of computerized information system
designed to aid business executives in the decision-making process. Such executives are often asked to
process information coming from multiple sources before eventually taking all of the information and
attempting to make sense of it. By using an support system, executives have all of the necessary
information at their fingertips and can break that information down in a much more efficient manner.

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These systems are generally formatted as software which is easy to use, quickly responsive to new
information, and available to other managers who are expected to provide input to top executives.
The main goal of this type of support system is to synthesize all of the different
sources of pertinent information into concise form that is easily understood. This can take several
different forms, such as graphs, pie charts, or even reports prepared by lower employees who have
access to the information system as well. In this way, the executive can get down to the heart of the
matter and make a command decision.
Chief executive officers and other top business executives are often paid high salaries to
perform their roles as the heads of the lucrative companies and corporations. The main reason that they
command such high salaries is the fact that they are able to make important decisions involving large
sums of money and the financial fates of employees and stockholders. Those decisions wouldn't be
possible without some assistance, and, increasingly, that assistance comes for technology. An executive
decision support system is one example of this assistance.
KNOWLEDGE MANAGEMENT
Data Information Knowledge Wisdom
•Knowledge : To transform information into knowledge, firm must expend additional resources to
discover patterns, rules, and contexts where knowledge works
•Wisdom : It‘s a Collective and individual experience of applying knowledge to solve problems. It
Involves where, when, and how to apply knowledge.
•Knowing how to do things effectively and efficiently in ways others cannot duplicate is prime source
of profit and competitive advantage
•Knowledge is both an individual attribute and a collective attribute of the firm.
•Knowledge is a cognitive, physiological event that takes place inside people‘s heads.
• Its stored in libraries & records as well as in the form of business processes and employee knowhow.
•Knowledge residing in the minds of employees that has not been documented is called tacit
knowledge.
•Knowledge that is documented is called explicit knowledge.
•Knowledge is situational and contextual : One must know when to perform a procedure as well as
how to perform it.
Knowledge management is a critical component of an organizations success. Knowledge assets are the
knowledge that an organization owns or needs to own, to achieve its goals. Every company‘s
knowledge requirements are a unique combination of knowledge strategy, tools and technologies,
processes and procedures. Knowledge management technologies capture this intangible element in an
organization and make it universally available. This approach has become to be known as knowledge
management: the practice of capturing and organizing information to make it more accessible and
valuable to those who need it.

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 Types of Knowledge
Knowledge can be divided into two types –
▪ Tacit knowledge
▪ Explicit knowledge.
Tacit knowledge is implicit, whereas explicit knowledge is rule-based knowledge that is used to match
actions to situations by invoking appropriate rules. An organization promotes the learning of Tacit
knowledge to increase the skills and creative capacities of its employees and takes advantage of
Explicit knowledge to maximize efficiency.
Explicit Knowledge
Knowledge that can be more easily attained and is often expressed or documented in a formal,
systematic manner - frequently in words and numbers.
Example: Include Management Directives, Executive Orders, policy manuals, and reference guides.
 Explicit knowledge is used in the design of routines, standard operation procedures, andthe structure
of data records. These forms of knowledge can be found in any organization.
 It allows an organization to enjoy a certain levelof operational efficiency and control.
 Explicit knowledge promotes equable, consistent organizational responses
Tactic Knowledge
Knowledge that can also be attained, but is not as easily transferred. Tacit knowledge can be attained
through dialogue, job shadowing, story-telling, and sharing of best practices and lessons learned. It
usually is rooted in an individual‘s experiences, intuition, insight, judgment, and knowledge of
organizational values. Individuals with tacit knowledge are usually considered to be experts within their
organizations and frequently sought out for guidance and input.
 Tacit knowledge includes hands-on skills, best practices, special know-how, and intuitions.
Personal knowledge that is difficult to articulate.
 Tacit knowledge in an organization ensures task effectiveness. It also provides for a kind of creative
vitality – intuition and spontaneous insight can often tackle tough problems that would otherwise be
difficult to solve.
 Traditionally the transfer of Tacitknowledge is through shared experience, through apprenticeship
and job training.
 Tacit knowledge is cultivated in an organizational culture that motivates through sharedvision and
common purpose.
An organization must adopt a holistic approach to knowledge management that successfully
combines Tacit and Explicit knowledge at all levels of the organization.
 Definition of KMS
A knowledge management system comprises a range of practices used in an organization to identify,
create, represent, distribute, and enable adoption to insight and experience. Such insights and
experience comprise knowledge, either embodied in individual or embedded in organizational
processes and practices.

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 Purpose of KMS
 Improved performance
 Competitive advantage
Innovation
 Sharing of knowledge
 Integration
 Continuous improvement by:
o Driving strategy
o Starting new lines of business
o Solving problems faster
o Developing professional skills
o Recruit and retain talent
 IMPORTANT DIMENSIONS OF KNOWLEDGE
a. KNOWLEDGE IS A FIRM ASSET
Knowledge is an intangible asset.
The transformation of data into useful information and knowledge requires organizational resources.
Knowledge is not subject to the law of diminishing returns as are physical assets, but instead
experiences network effects as its value increases as more people share it.
b. KNOWLEDGE HAS DIFFERENT FORMS
Knowledge can be either tacit or explicit (codified).
Knowledge involves know-how, craft, and skill.
Knowledge involves knowing how to follow procedures.
Knowledge involves knowing why, not simply when, things happen (causality).
c. KNOWLEDGE HAS A LOCATION
Knowledge is a cognitive event involving mental models and maps of individuals.
There is both a social and an individual basis of knowledge.
Knowledge is ―sticky‖ (hard to move), situated (enmeshed in a firm‘s culture), and contextual (works
only in certain situations).
d. KNOWLEDGE IS SITUATIONAL
Knowledge is conditional; knowing when to apply a procedure is just as important as knowing the
procedure (conditional).
Knowledge is related to context; you must know how to use a certain tool and under what
circumstances
 Activities in Knowledge Management
 Start with the business problem and the business value to be delivered first.
 Identify what kind of strategy to pursue to deliver this value and address the KM problem.
 Think about the system required from a people and process point of view.
 Finally, think about what kind of technical infrastructure are required to support the people and
processes.

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 Implement system and processes with appropriate change management and iterative staged release.
 Levels of km
 THE KNOWLEDGE MANAGEMENT VALUE CHAIN
Knowledge management refers to the set of business processes developed in an organization to create,
store, transfer, and apply knowledge. Knowledge management increases the ability of the organization
to learn from its environment and to incorporate knowledge into its business processes. Figure 11.1
illustrates the five value-adding steps in the knowledge management value chain. Each stage in the
value chain adds value to raw data and information as they are transformed into usable knowledge.
In Figure, information systems activities are separated from related management and
organizational activities, with information systems activities on the top of the graphic and
organizational and management activities below
One apt slogan of the knowledge management field is ―Effective knowledge management
is 80% managerial & organizational & 20% technology‖.

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1) Knowledge Acquisition
Organisation acquire knowledge in a number of ways, depending on the type of knowledge they seek.
 Documenting tacit and explicit knowledge:
• By storing documents, reports, presentations & best practices.
• Storing unstructured documents (e.g., e-mails)
•Developing online expert networks, so that employees can ―find the expert‖ in the company who
has knowledge in his or her head.
 Creating new Knowledge by discovering patterns in corporate data or by using knowledge
workstations where engineers can discover new knowledge.
 By tracking data from Transaction processing systems (eg: tracking sales, payments, inventory ,
customers & other vital data) as well as data from external sources (eg: news feeds, industry reports,
legal opinions, scientific research & govt statistics)
2) Knowledge Storage
•Documents, patterns, and experts rules can be stored so they can be retrieved and used by employees.
•Knowledge storage involves the creation of a database.
•Document management systems that digitize, index, and tag documents according to a coherent
framework are large databases adept at storing collections of documents.
•Expert system also helps corporations preserve the knowledge that is acquired by incorporating that
knowledge into organizational processes and culture.
Role of management in Knowledge Storage:
Support development of planned knowledge storage systems
Encourage development of corporate-wide schemas for indexing documents
Reward employees for taking time to update and store documents properly
3) Knowledge Dissemination
Portals, e-mail, instant messaging , wikis, social networks, & search engine technologies are used for
sharing / disseminating calendars, documents, data , and graphics.
To help managers to discover required information and knowledge from a vast deluge or
flood of information; -training programs, informal networks and shared management experience help
managers focus attention on important information.
4) Knowledge Application
 Knowledge that is not shared and applied to the practical problems faced by managers; does not
add value to the business.
 To provide a return on investment, organizational knowledge must become systematic part of
management decision making & become situated in decision-support systems.
 New knowledge must be built into a firm‘s business processes and key application systems,
including enterprise application for managing key internal business processes & relationships
with customers and suppliers.
 Management supports this process by creating- based on new knowledge - new business
practices , new products & services and new markets for the firm.

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 TYPES OF KNOWLEDGE MANAGEMENT SYSTEMS
There are essentially three major types of knowledge management systems
1. Enterprise-wide knowledge management systems
2. Knowledge work systems (KWS)
3. Intelligent techniques.
1. Enterprise-wide knowledge management systems
General-purpose firm-wide efforts to collect, store, distribute, and apply digital content and knowledge.
These systems include capabilities for searching for information, storing both structured and
unstructured data, and locating employee expertise within the firm. They also include supporting
technologies such as portals, search engines, collaboration and social business tools, and learning
management systems.
2. Knowledge work systems (KWS)
Specialized systems built for engineers, scientists, other knowledge workers charged with discovering
and creating new knowledge.
3.Intelligent techniques
Includes a Diverse group of techniques such as
1) Data mining 4) Fuzzy Logic
2) Expert Systems 5) Genetic Algorithms &
3) Neural Networks 6) Intelligent Agents; used for various goals
These techniques have different objectives, from a focus on :
a) Discovering knowledge (Data Mining & Neural Networks)
b) Distilling knowledge in the form of rules for a computer program (Expert systems and Fuzzy Logic)
c) Discovering optimal solutions for problems (Genetic Algorithms)

35
ENTERPRISE-WIDE KNOWLEDGE MANAGEMENT SYSTEMS
Firms must deal with at least three kinds of knowledge. Some knowledge exists within the firm in the
form of structured text documents (reports and presentations). Decision makers also need knowledge
that is semi-structured, such as e-mail, voice mail, chat room exchanges, videos, digital pictures,
brochures, or bulletin board postings. In still other cases, there is no formal or digital information of
any kind, and the knowledge resides in the heads of employees. Much of this knowledge is tacit
knowledge that is rarely written down.
Enterprise-wide knowledge management systems deal with all three
types of knowledge. Enterprise-wide knowledge management systems are firm-wide efforts to collect,
store, distribute, and apply digital content and knowledge. They use an array of technologies for storing
structured and unstructured content, locating employee expertise, searching for information,
disseminating knowledge, and using data from key corporate systems
Enterprise-wide knowledge management systems use an array of technologies for storing structured
and unstructured documents, locating employee expertise, searching for information, disseminating
knowledge, and using data from enterprise applications and other key corporate systems.

36
There are three major categories of enterprise-wide KMS:
1. Structured knowledge systems
2. Semi-structured knowledge systems
3. Knowledge networks
o Structured knowledge systems provide databases and tools for organizing and storing structured
knowledge that exists in formal documents. KPMG International's KWorld is an example. It
provides online access to presentations, white papers, best practice guidelines, methodologies,
human resources information, professional resumes, research reports, and external news
sources. It also features a tool that permits collaboration among team members and clients in a
secure Web environment
o Semi-structured knowledge systems provide databases and tools for organizing and storing
semi-structured knowledge, such as e-mail, brochures, or rich media that is not in a formal
document or report. Such systems provide a database and technical infrastructure that tracks,
stores, and organizes semi-structured content.
o Knowledge network systems try to turn tacit, unstructured knowledge into explicit knowledge
that can be shared in a database. To disseminate tacit knowledge, knowledge network systems
may provide directories and tools for locating firm employees with special expertise, or provide
solutions to commonly found problems in a central knowledge database or FAQ repository
Enterprise knowledge management can be defined as a consistent and incorporated vision of the
sources and uses of knowledge across an association. Enterprise knowledge management considers into
account every knowledge source—from what employees identify to what clients tell us—and combines
it with traditional corporate knowledge like standard operating procedures.
To implement enterprise knowledge management initiate by creating a single corporate data
model that recognizes common data and objects and makes them usable across the whole enterprise.
The enterprise knowledge management scheme depends on a few simple principles and
traits:
1. The first principle of enterprise knowledge management is that there is no ―natural‖ view of data.
Each and every data object is generated in a manner that is autonomous of the eventual use of that data.
To generate data objects, standard definitions of them must be formed and adhered to. In the same way
that relational databases must normalize data— remove redundant data and replication of objects—so
as to be efficient and avoid maintenance confusion and errors, so too does enterprise knowledge
management rely on normalized knowledge for smooth operation and ease of management.
2. Enterprise knowledge management needs open architectures and standard protocols. The individual
applications that will maintain enterprise knowledge management all through the organization must be
able to converse with each other, which is why applications with proprietary data stores have no
position in enterprise knowledge management.

37
3. Enterprise knowledge management must incorporate internal and external data. The restrictions of
corporate knowledge go beyond internal knowledge. The knowledge shared by suppliers, distributors,
and clients in their transactions with us is significant company data that must, along with internal data,
be managed. Additionally, every company requires access to analyst reports, aggressive information,
macroeconomic information, and much more. While we typically have some control over the format of
internal data, we normally have much less control over external data. This is why standards like XML
tags will in the upcoming days be critical.
4. The enterprise knowledge management effort must embrace all electronic corporate
communications; intranets, extranets, and public Web sites must all sketch from the same data
resources. The traditional view of intranets, extranets, and Websites is that they represent diverse
repositories or networks. They were considered of as exclusive entities with little in common beyond
their shared infrastructure. But this view is incorrect.
To generate separate repositories of data for each of these entities is redundant and unnecessary. There
is plenty of data that can and should be shared across these sites: project data, phone numbers, news,
etc. It‘s the data that should be labeled as public or private, confidential or non-confidential, not the
network.
4. Ultimately, and most significantly, enterprise knowledge management must cross functional
boundaries inside the organization. Businesses are managed into functional groups (IT, HR,
Sales, Research) to make management simpler.
KNOWLEDGE WORK SYSTEMS
The enterprise-wide knowledge systems we have just described provide a wide range of capabilities
that can be used by many if not all the workers and groups in an organization. Firms also have
specialized systems for knowledge workers to help them create new knowledge and to ensure that this
knowledge is properly integrated into the business.
Knowledge work systems are there to help to deal with problems requiring technical
expertise or knowledge. Software includes:
 Word-processing for clerical staff;

38
 Spreadsheets for accounts, and sales staff;
 Database managements systems for keeping records;
 CAD for designers;
 Project management systems;
Expert systems for specialist staff. An example of this may be a system that enables an engineer to
select a particular metal alloy for a bearing. He could type in the parameters he needs and the system
can suggest several different alloys. It is then up to the engineer to us his knowledge and experience to
decide what alloy he will use. KWS help the professional specialist.
Requirements of KWS
 Help knowledge workers create and integrate knowledge into the organization.
 Need links worker to external and internal(organization) information.
 Software needs include powerful graphics, analytic capabilities, document management, and
communication capabilities.
 Usually need much processing power.
Examples of KWS
 Computer Aided Design (CAD) systems for sophisticated graphics,
 Virtual Reality systems for simulating the real world (entertainment and work, i.e. flightsimulators).
Use interactive graphics and sensor equipment.
 Virtual Reality Modeling Language (VRML) is a standardized modeling language to 3-D modeling
on the WWW. It can include multimedia types (image, animation, audio) to simulate real world
settings. Browsers by Netscape and Microsoft are VRML compliant.
Knowledge workers, include researchers, designers, architects, scientists, and engineers
who primarily create knowledge and information for the organization. Knowledge workers usually
have high levels of education and memberships in professional organizations and are often asked to
exercise independent judgment as a routine aspect of their work. For example, knowledge workers
create new products or find ways of improving existing ones. Knowledge workers perform three key
roles that are critical to the organization and to the managers who work within the organization:
• Keeping the organization current in knowledge as it develops in the external world—in technology,
science, social thought, and the arts
• Serving as internal consultants regarding the areas of their knowledge, the changes taking place, and
opportunities
Knowledge work systems require strong links to external knowledge bases in addition to specialized
hardware and software.
• Acting as change agents, evaluating, initiating, and promoting change projects
REQUIREMENTS OF KNOWLEDGE WORK SYSTEMS
Most knowledge workers rely on office systems, such as word processors, voice mail, e-mail,
videoconferencing, and scheduling systems, which are designed to increase worker productivity in the

39
office. However, knowledge workers also require highly specialized knowledge work systems with
powerful graphics, analytical tools, and communications and document management capabilities.
These systems require sufficient computing power to handle the sophisticated graphics or complex
calculations necessary for such knowledge workers as scientific researchers, product designers, and
financial analysts. Because knowledge workers are so focused on knowledge in the external world,
these systems also must give the worker quick and easy access to external databases. They typically
feature user-friendly interfaces that enable users to perform needed tasks without having to spend a
great deal of time learning how to use the system. Knowledge workers are highly paid—wasting a
knowledge worker‘s time is simply too expensive
Knowledge workstations often are designed and optimized for the specific tasks to be
performed; so, for example, a design engineer requires a different workstation setup than a financial
analyst. Design engineers need graphics with enough power to handle three-dimensional (3-D) CAD
systems. However, financial analysts are more interested in access to a myriad number of external
databases and large databases for efficiently storing and accessing massive amounts of financial data.
INTELLIGENT TECHNIQUES
BUSINESS INTELLIGENCE SYSTEM / ARTIFICIAL INTELLIGENCE
All human beings have intelligence, which they use for problem solving. Intelligence when supported
by knowledge and reasoning abilities becomes an artificial intelligence. When such an artificial
intelligence is packed into a database as a system, then what we have is AI system.
AI systems fall into three basic categories,
1) Expert Systems (knowledge based),
2) Natural Language (Native languages) Systems,

40
3) Perception System (vision, speech, touch).
Artificial intelligence is a software technique applied to the non-numeric data expressed in terms of
symbols, statements and patterns. It uses the methods of symbolic processing, social and scientific
reasoning and conceptual modeling for solving the problems. The AI systems are finding applications
in configurations, design, diagnosis, interpretation, analysis, planning, scheduling, training, testing and
forecasting.
The AI systems do not replace people. They liberate experts from solving common/simple
problems, leaving the experts to solve complex problems. Artificial intelligence systems help to avoid
making same mistakes, and to respond quickly and effectively to a new problem situation.
The knowledge-based Expert System is a special AI System. It has wide applications in
business and industry.
The term 'Business Intelligence' has evolved from the decision support systems and gained
strength with the technology and applications like data warehouses, Executive Information Systems and
Online Analytical Processing (OLAP).
Business Intelligence System is basically a system used for finding patterns
from existing data from operations.
 Characteristics of BIS
 It is created by procuring data and information for use in decision-making.
 It is a combination of skills, processes, technologies, applications and practices.
 It contains background data along with the reporting tools.
 It is a combination of aset of concepts and methods strengthened by fact-based support systems.
 It is an extension of Executive Support System or Executive Information System.
 It collects, integrates, stores, analyzes, and provides access to business information.
 It is an environment in which business users get reliable, secure, consistent, comprehensible, easily
manipulated and timely information.
 It provides business insights that lead to better, faster, more relevant decisions.
 Benefits of BIS
 Improved Management Processes.
 Planning, controlling, measuring and/or applying changes that result in increased revenues and
reduced costs.
 Improved business operations.
 Fraud detection, order processing, purchasing that results in increased revenues and reduced costs.
 Intelligent prediction of future
 Approaches of BIS
For most companies, it is not possible to implement a proactive business intelligence system at one go.
The following techniques and methodologies could be taken as approaches to BIS:
 Improving reporting and analytical capabilities
 Using scorecards and dashboards
 Enterprise Reporting
 On-line Analytical Processing (OLAP) Analysis
 Advanced and Predictive Analysis

41
 Alerts and Proactive Notification
 Automated generation of reports with user subscriptions and "alerts" to problems and/or
opportunities.
 Capabilities of BIS
 Data Storage and Management:
o Data ware house
o Ad hoc analysis
o Data quality
o Data mining
 Information Delivery
o Dashboard
o Collaboration /search
o Managed reporting
o Visualization
o Scorecard
 Query, Reporting and Analysis
o Ad hoc Analysis
o Production reporting
o OLAP analysis
Artificial intelligence and database technology provide a number of intelligent techniques that
organizations can use to capture individual and collective knowledge and to extend their knowledge
base. Expert systems, case-based reasoning, and fuzzy logic are used for capturing tacit knowledge.
Neural networks and data mining are used for knowledge discovery. They can discover underlying
patterns, categories, and behaviors in large data sets that could not be discovered by managers alone or
simply through experience. Genetic algorithms are used for generating solutions to problems that are
too large and complex for human beings to analyze on their own. Intelligent agents can automate
routine tasks to help firms search for and filter information for use in electronic commerce, supply
chain management, and other activities
A. Expert systems
An Expert System is a computer program that possesses or represents knowledge in a particular
domain, has the capability of processing/manipulating or reasoning with this knowledge with a view to
solving a problem, giving some achieving or to achieve some specific goal.
An expert system may or may not provide the complete expertise or functionality of a human
expert but it must be able to assist a human expert in fast decision making. The program might interact
with a human expert or with a customer directly.
Expert systems are an intelligent technique for capturing tacit knowledge in a very specific and
limited domain of human expertise. These systems capture the knowledge of skilled employees in the
form of a set of rules in a software system that can be used by others in the organization. The set of
rules in the expert system adds to the memory, or stored learning, of the firm.

42
Expert systems lack the breadth of knowledge and the understanding of fundamental principles
of a human expert. They typically perform very limited tasks that can be performed by professionals in
a few minutes or hours, such as diagnosing a malfunctioning machine or determining whether to grant
credit for a loan. Problems that cannot be solved by human experts in the same short period of time are
far too difficult for an expert system. However, by capturing human expertise in limited areas, expert
systems can provide benefits, helping organizations make high-quality decisions with fewer people.
Expert systems model human knowledge as a set of rules that collectively are called the
knowledge base. Expert systems have from 200 to many thousands of these rules, depending on the
complexity of the problem.
The traditional definition of a computer program is usually:
Algorithm + data structures = program
In an expert system, the definition changes to:
Inference engine + knowledge = expert system
 Basic Properties of an Expert System
The basic properties of expert system are:
 It tries to simulatehuman reasoning capability about a specific domain rather than the domain itself.
This feature separates expert systems from some other familiar programs that use mathematical
modeling or computer animation. In an expert system the focus is to emulate an expert‘s knowledge
and problem solving capabilities and if possible, at a faster rate than a human expert.
 It perform reasoning over the acquired knowledge, rather than merely performing somecalculations
or performing data retrieval.
 It can solve problems by using heuristic or approximate models which, unlike other algorithmic
solutions are not guaranteed to succeed.
AI programs that achieve expert-level competence in solving problems in different domains are
more called knowledge based systems. A knowledge-based system is any system which performs a job
or task by applying rules of thumb to a symbolic representation of knowledge, instead of employing
mostly algorithmic or statistical methods. Often the term expert systems is reserved for programs
whose knowledge base contains the knowledge used by human experts, in contrast to knowledge
gathered from textbooks or non-experts. But more often than not, the two terms, expert systems and
knowledge-based systems are taken us synonyms. Together they represent the most widespread type of
AI application.
 Characteristics of Expert System
1. Expert system is an application of artificial Intelligence which incorporates knowledge and problem
solving skills of a human being into an information system.
2. Expert system can replace human beings.
3. Expert systems are not designed for one level of management because their primary goal is to
provide expertise to whole organization.
4. Expert system has three components such as knowledge base, the inference engine and the user
interface.
 Need of Expert System

43
There are many reasons to use an expert system. Here are some of the primary reasons:
 Helps preserve knowledge-builds up the corporate memory of the firm.
 Helps if expertise is scarce, expensive, or unavailable
 Helps if under time and pressure constraints.
 Helps in training new employees.
 Helps improve worker productivity.
Expert systems are necessitated by the limitations associated with conventional human decision-making
processes, including:
 Human expertise is very scarce.
 Humans get tired from physical or mental workload.
 Humans forget crucial details of a problem.
 Humans are inconsistent in their day-to-day decisions.
 Humans have limited working memory.
 Humans are unable to comprehend largeamounts of data quickly.
 Humans are unable to retain large amounts of data in memory.
 Humans are slow in recalling information stored in memory.
 Humans are subject to deliberate or inadvertent bias in their actions.
 Humans can deliberately avoiddecision responsibilities.
 Building Block of Expert System
There are basically four steps to building an expert system:
 Analysis
 Specification
 Development
 Deployment
The spiral model is normally used to implement this approach. The spiral model of developing software
is fairly common these days. Expert system development can be modeled as a spiral, where each circuit
adds more capabilities to the system. There are other approaches, such as the incremental or linear
model, but we prefer the spiral model.
B. Fuzzy Logic Systems
Fuzzy Logic Systems are defined as computer-based systems that can access data that are incomplete or
only partially accurate. These systems can solve unstructured problems with incomplete knowledge by
producing approximate inferences and solutions.
Fuzzy logic is a method of reasoning that resembles human reasoning, in that it allows for
approximate values and inferences (fuzzy logic) and incomplete or ambiguous data (fuzzy data) instead
of relying only on crisp data, such as binary (yes/no) choices.
 Fuzzy Logic in Business
Instances of applications of fuzzy logic are various in Japan, but rate in the United States. The

44
United States has tended to favor by means of AI solutions such as expert systems or neural networks.
Japan has executed many fuzzy logic applications, especially the use of special-purpose fuzzy logic
microprocessors chips, known as fuzzy process controllers.
C. Neural Networks Artificial Neural Systems
Neural networks are defined as computing systems modeled on the human brain‘s mesh-like network of
interlinked processing elements, known as neurons. Neural networks can be executed on
microcomputers and other computer systems through software packages, which simulate the actions of
a neural network of many processing elements. Specialized neural network co-processor circuit boards
are also obtainable. Special-purpose neural net microprocessor chips are used in some application areas.
Uses comprise:
 Military weapons systems
 Voice recognition
Check signature verification
 Manufacturing quality control
 Image processing
 Credit risk assessment
 Investment forecasting
 Data mining
D. Genetic Algorithm
The use of genetic algorithms is a rising application of artificial intelligence. Genetic algorithm
software accesses Darwinian (survival of the fittest); randomizing, and other mathematical functions to
create an evolutionary process that can capitulate increasingly better solutions to a problem. Genetic
algorithms were initially used to create millions of years in biological, geological, and ecosystem
evolution in just a few minutes on a computer. Now genetic algorithm software is being accessed to
model numerous scientific, technical, and business processes. Genetic algorithms are especially useful
for conditions in which thousands of solutions are probable and must be calculated to form a best
possible solution. Genetic algorithm software accesses sets of mathematical process rules (algorithms)
that mention how combinations of process components or steps are to be produced. This may comprise:
 Trying random process combinations (mutation)
 Merging parts of several good processes (crossover)
 Choosing good sets of processes and discarding poor ones (selection).
E. Virtual reality (VR)
Virtual reality (VR) is computer-simulated reality. Virtual reality is a fast-growing area of artificial
intelligence that had its origins in efforts to build more natural, realistic, multisensory human–computer
interfaces. So virtual reality relies on multisensory input/output devices such as a tracking headset with
video goggles and stereo earphones, a data glove or jumpsuit with fiber-optic sensors that track your
body movements, and a walker that monitors the movement of your feet. Then you can experience
computer-simulated ―virtual worlds‖ three-dimensionally through sight, sound, and touch. Virtual

45
reality is also called tele-presence. For example, you can enter a computer-generated virtual world, look
around and observe its contents, pick up and move objects, and move around in it at will. Thus, virtual
reality allows you to interact with computer-simulated objects, entities, and environments as if they
actually exist
F. Hybrid AI Systems
Hybrid system is defined as a software system which employs, in parallel, a mixture of methods and
techniques from artificial intelligence subfields as Neuro-fuzzy systems, hybrid connectionist-symbolic
models, Fuzzy expert systems, etc.
Every natural intelligent system is considered as hybrid since it accomplishes mental functions
on both the symbolic and sub symbolic stages. From the previous few years there has been an growing
conversation of the significance of A.I. Systems Integration. Relying on ideas that there have already
been produced simple and particular AI systems and now is the time for integration to generate broad
AI systems.
G. Intelligent Agents
An intelligent agent (also called intelligent assistants/wizards) is software replacement for an end user
or a process that accomplishes a specified requirement or activity. An intelligent agent accesses an
incorporated and learned knowledge base regarding a person or process to make decisions and finish
tasks in a manner that accomplishes the intentions of a user. One of the most well recognized uses of
intelligent agents is the wizards located in Microsoft Office and other software suites.
 TYPES OF INTELLIGENCE AGENTS
A. User Interface Agents
• Interface Tutors. Observe user computer operations, correct user mistakes, and provide hints and
advice on efficient software use.
• Presentation Agents. Show information in a variety of reporting and presentation forms and media
based on user preferences.
• Network Navigation Agents. Discover paths to information and provide ways to view information
that are preferred by a user.
• Role-Playing Agents. Play what-if games and other roles to help users understand information and
make better decisions.
B. Information Management Agents
• Search Agents. Help users find files and databases, search for desired information, and suggest and
find new types of information products, media, and resources.
• Information Brokers. Provide commercial services to discover and develop information resources
that fit the business or personal needs of a user.
• Information Filters. Receive, find, filter, discard, save, forward, and notify users about products
received or desired, including e-mail, voice mail, and all other information media.

46
H. ORGANIZATIONAL INTELLIGENCE: CASE-BASED REASONING
Expert systems primarily capture the tacit knowledge of individual experts, but organizations also have
collective knowledge and expertise that they have built up over the years. This organizational
knowledge can be captured and stored using case-based reasoning. In case-based reasoning (CBR),
descriptions of past experiences of human specialists, represented as cases, are stored in a database for
later retrieval when the user encounters a new case with similar parameters. The system searches for
stored cases with problem characteristics similar to the new one, finds the closest fit, and applies the
solutions of the old case to the new case. Successful solutions are tagged to the new case and both are
stored together with the other cases in the knowledge base. Unsuccessful solutions also are appended to
the case database along with explanations as to why the solutions did not work.
KNOWLEDGE BASED EXPERT SYSTEMS (KBES)
Decision making or problem solving is a unique situation riddled with uncertainty and complexity,
dominated by the resource constraints and a possibility of several goals. In such cases, flexible systems
(open systems) are required to solve the problems. Most of such situations, termed as the unstructured
situations, adopt two methods of problem solving, generalized or the knowledge based expert system
(KBES).
The generalized problem solving approach considers the generally applicable constraints, examines
all possible alternatives and selects one by trial and error method with reference to a goal. The
knowledge based problem solving approach considers the specific constraints within a domain,
examines the limited problem alternatives within a knowledge domain and selects the one with
knowledge based reasoning with reference to a goal.
In a generalized approach, all alternatives are considered and the resolution of the problem is by
trial and error, with no assurance, whether it is the best or the optimum, while, in the knowledge based
approach, only limited alternatives are considered and resolution is made by a logical reasoning with
the assurance of the local optimum. The generalized approach is dominated by a procedure or method,
while the knowledge based approach is dominated by the reasoning process based on the knowledge.
Since, the KBES considers knowledge as the base; the question arises whose knowledge is to be
considered as a basis. It is generally agreed that an expert has knowledge, and therefore, he becomes
the source of knowledge. An expert is difficult to find and the difficulty of no-knowledge or limited
knowledge on the subject is always expressed. Knowledge is with experienced people and experience,
is wide and distributed. Hence, a system is required which will hold the knowledge of experienced
people and provide an application path to solve the problem. Such a system eliminates the knowledge
bottleneck.
To build a knowledge-based system certain prerequisites are required. The first prerequisite is that
a person with the ability to solve the problem with knowledge-based reasoning should be available. The
second prerequisite is that such an expert should be able to articulate the knowledge to the specific
problem characteristics.

47
Knowledge in the KBES is defined as a mix of theory of the subject, knowledge of its
application, organized information and the data of problems and its solution, and an ability to generate
new avenues to solve the problem.
The KBES has three basic components which are necessary to build the system as shows in
Fig.
USER CONTROL
MECHANISM
KNOWLEDGE INFERENCE
BASE MECHANISM
• Knowledge base
It is a database of knowledge consisting of the theoretical foundations, facts, judgments, rules,
formulae, intuition, and experience. It is a structural storage with facilities of easy access.
• Inference mechanism
It is a tool to interpret the knowledge available and to perform logical deductions in a given situation
• User control mechanism
It is a tool applied to the inference mechanism to select, interpret and deduct or infer. The user control
mechanism user the knowledge base in guiding the inference process.
In the KBES, three components are independent of each other. This helps in modifying the system
without affecting all the components. Like in the database application, where the data is independent of
its application, in KBES, knowledge is independent from application, i.e., inference process. The KBES
database, stores the data, the cause-and effect relation rules, and the probability information on event
occurrences.
For example, the knowledge base of Health Care would have knowledge such as obesity leads to
high blood pressure. There are 60 per cent chances that smokers may suffer from cancer.. The KBES,
therefore, stores and uses knowledge, accepts judgments, questions intelligently, draws inferences,
provides explanation with reasons, offers advice and prompts further queries for confirmation.
In the KBES, the knowledge data base uses certain methods of knowledge representation. These
methods are-Semantic Networks, Frames and Rules.

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