1. Chapter 10
Knowledge
Management
and
Intelligent
Systems

2. Learning Objectives
• Intelligent Systems
• Artificial Intelligence (AI)
• Applications of AI
• Knowledge Management
• Value of Knowledge Management
• Components of Knowledge Management
• Business Intelligence
• Intelligent Business
• Competitive Intelligence

3. Intelligent System
• An intelligent system learns how to act so it
can reach its objectives. An intelligent system
learns during its existence.
• One can create intelligent systems by
embedding the intelligence artificially into the
machine, so that the machine starts behaving
intelligently.

4. Artificial Intelligence (AI)
• Artificial intelligence is the science of making
machines does things that would require
intelligence.
• Artificial intelligence (AI) is concerned with two
basic ideas - first, it involves studying the thought
processes of humans; second, it deals with
representing those processes via machines.
• AI is concerned with the studying of thought
processes of humans and representing those
processes via machines.

5. Applications of Artificial Intelligence
• Expert systems
– Human knowledge stored on machine for use in problem-
solving
• Natural language processing
– Allows user to use native language instead of English
• Speech recognition
– Computer understanding spoken language
• Sensory systems
– Vision, tactile, and signal processing systems
• Robotics
– Sensory systems combine with programmable
electromechanical device to perform manual labor
5

6. Applications of AI
• Artificial Neural Network
• Fuzzy Logic
• Genetic Algorithm
• Expert Systems

7. Artificial Neural Networks (ANN)
• ANN attempts to emulate the processing
patterns of the biological bra ins.
• Artificial Neural Networks (ANN) simulates neural
networks found in nature, such as the human
brain. The term artificial is used to distinguish
ANNs from their biological counterparts.
• An ANN is trained through a learning process,
and knowledge is retained through synaptic
weights.
• Synaptic weights between nodes are adjusted
based on the desired output.

8. Fuzzy Logic
• Fuzzy logic is an AI technique that deals with
uncertainties by simulating the process of human
reasoning, allowing the computer to behave less
precisely and logically than conventional computers do.
• Computers are not been able to recognize "maybe", or
"slightly". The concept is based on feeding the
computer "fuzzy sets," or groupings of concrete
information and relative concepts.
• The Fuzzy Logic model is empirically-based, relying on
an operator's experience rather than their technical
understanding of the system.
• It uses imprecise and yet very descriptive terms of what
must actually happen.

9. Genetic Algorithms
• A genetic algorithm is a type of search
algorithm that takes input and computes an
output, where multiple paths might be taken.
• Genetic algorithms are a part of evolutionary
computation that use concepts borrowed
from nature to conduct the search, including
selection, mutation, and crossover rate.

10. Expert Systems
• Expert systems (ES) are computer-based
systems that transfer expertise from an expert
to a computer and then on to other humans.
• An expert system is a form of artificial
intelligence that uses a knowledge base (KB)
and inference engine to make decisions.
• Building an expert system is known as
knowledge engineering and its practitioners
are called knowledge engineers.

11. Components of Expert Systems
• Every expert system consists of:
• Knowledge Base
• Inference Engine
• Blackboard
• User interface
• Justifier
• Knowledge acquisition system
• Knowledge refining system.

12. Business Applications of AI
• Finance
– Insurance evaluation, credit analysis, tax planning, financial planning and
reporting, performance evaluation
• Data processing
– Systems planning, equipment maintenance, vendor evaluation, network
management
• Marketing
– Customer-relationship management, market analysis, product planning
• Human resources
– HR planning, performance evaluation, scheduling, pension management, legal
advising
• Manufacturing
– Production planning, quality management, product design, plant site
selection, equipment maintenance and repair
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13. Businesses Challenges
• Growing emphasis on creating customer value and
improving customer service;
• An increasingly competitive marketplace with a rising rate
of innovation;
• Reduced cycle times and shortened product development
times;
• Need for organizational adaptation because of changing
business rules and assumptions;
• Requirement to operate with a shrinking number of assets;
• Reduction in the amount of time employees are given to
acquire new knowledge; and
• Changes in strategic directions and workforce mobility that
lead to knowledge loss.

14. Some Facts
– A collection of data is not information.
– A collection of information is not knowledge.
– A collection of knowledge is not wisdom.
– A collection of wisdom is not truth.
• The idea is that data, information, knowledge,
and wisdom are more than simple collections.

15. Data-Information-Knowledge-Wisdom
Relationship

16. Definition Summary
• Information relates to description, definition,
or perspective (what, who, when, where).
• Knowledge comprises strategy, practice,
method, or approach (how).
• Wisdom embodies principle, insight, moral, or
archetype (why).

17. Knowledge Management
• Knowledge management (KM) is the access,
retrieval and distribution of human experiences
and relevant information between related
individuals or workgroups.
• Human knowledge and interaction is the key:
sharing ideas, solutions and relevant information
in an effort to create new solutions.
• Knowledge Management (KM) emphasizes
human interactions as the focal point
surrounding the collection, distribution and reuse
of information

18. Knowledge Management
• The key to knowledge management is to get the necessary
knowledge to the necessary person(s) within a specified workgroup
across the company infrastructure.
• KM doesn't mean enterprise-wide distribution; very few business
processes take place on an enterprise-wide level. And very few
employees work on an enterprise-wide level, except for top level
executives. Most action is planned and implemented through real
or virtual workgroups, departments or separate groups of person(s)
working together on a common project.
• It is the goal of knowledge management to help those people work
better together, using and managing increasing amounts of
information.
• The result of a successful knowledge management implementation
is a knowing, learning and growing enterprise.

19. Essential Components of KM
• People to relay past experience and generate
new ideas (innovation);
• processes for sharing and distributing that
information; and
• Technologies to make it all work in a fast,
efficient manner.

20. Characteristics of Knowledge
Management
• The challenge of Knowledge Management is to
determine what information within an
organization qualifies as "valuable.”
• Knowledge Management is about people.
• Knowledge Management is goal-directed.
• Knowledge Management is ever-changing.
• Knowledge Management is value-added.
• Knowledge Management is visionary.
• Knowledge Management is complementary.

21. Advantages that KM Offer
• Facilitates better, more informed decisions;
• Contributes to the intellectual capital of an
organization;
• Encourages the free flow of ideas which leads to
insight and innovation;
• Eliminates redundant processes, streamlines
operations, and enhances employee retention
rates;
• Improves customer service and efficiency; and
• Leads to greater productivity.

22. Components of Knowledge
Management
• People: People are necessary for brainpower,
innovation, creativity, and the experiential
knowledge to solve technical problems.
• Processes: An organization must also have
effective and efficient business processes in place
to create a sharing, collective atmosphere.
• Technology: To support human innovation and
progress, a basic technological infrastructure
must be in place to help leverage collective
brainpower and corporate knowledge and deliver
new ideas and solutions quickly and practically.

23. Business Intelligence (BI)
• Business Intelligence (BI) is the user-centered
process of exploring data, data relationships
and trends - thereby helping to improve
overall decision making.
• BI empowers enterprises with systems that
promote understanding and action through
facts and opinions; quality information;
meaningful delivery; proliferation of data
analysis; and shared insights.

24. Users’ BI Usage Pattern

25. BI Issues
• Unrealistic Expectations
• Limiting Access to Results
• Poor Data Quality
• Resistance to Change
• Winning It

26. Intelligent Business
• Intelligent business is a fundamental shift in thinking for
the world of data warehousing and business
intelligence.
• Intelligent business is about is taking business
intelligence and putting at the very heart of the
enterprise. This idea here is that so called ‘traditional’
data warehousing and business intelligence continues
as normal but in addition, operational applications and
portals can request trusted business intelligence on
demand.
• Intelligent business is BI integrated into operational
business processes.
• It is also event driven.

27. Intelligent Business offers
• On-demand requests for specific intelligence e.g.
about a specific customer
• On-demand requests for automatic analysis of
data, rule-driven automatic alerts and automatic
recommendations
• Automatic capturing of events in business
operations that trigger the integration of other
data on-demand, to be automatically analysed to
take manual or automatic actions. This is known
as business activity monitoring (BAM).

28. Competitive Intelligence (CI)
• Competitive Intelligence (CI) is the purposeful
and coordinated monitoring of the
organization competitor(s), wherever and
whoever they may be, within a specific
marketplace.
• Strategically, CI helps to gain prior knowledge
of the competitor's plans and help the
organization to plan their business strategy to
countervail their competitor’s plans.

29. Goals of Competitive Intelligence
• Adopt a strategic approach to the use of
competitive intelligence;
• To see the intelligence function as an integral
part of strategy formulation;
• Show how competitive intelligence is used by
firms to achieve competitive advantage; and
• Examine the process, the tools, and the
output of CI

30. Implementing CI
• How clearly the organization has defined its
mission, its strategic intentions, its objectives and
its strategic choices?
• What the organizations need to know to develop
and to select strategies which are not only
successful, but sustainable?
• What new products should the organization build
and which markets should they enter and how?
• How do they implement the competitive
strategy?

31. CI Framework
• Assessment of strategies
• Competitor perceptions
• Effectiveness of current operations
• Competitor capabilities
• Long-term market prospects

32. Summary
• Intelligent systems are one that learns from the environment and its past actions. One can create intelligent
systems by embedding the intelligence artificially into the machine, so that the machine starts behaving
intelligently.
• Intelligent system found its applications in business areas like financial services, customer satisfaction, and
material management. It is also being widely adopted in diagnostics and testing.
• Artificial intelligence is the science of making machines does things that would require intelligence. Artificial
intelligence (AI) is concerned with two basic ideas - first, it involves studying the thought processes of humans;
second, it deals with representing those processes via machines. AI is concerned with the studying of thought
processes of humans and representing those processes via machines.
• Neural Networks are intelligent systems with architecture & processing capabilities that mimic certain processing
capabilities of the human brain. Knowledge representation is based on massive parallel processing, fast retrieval
of large information, and ability to recognize patterns based on experience is called neural computing.
• Artificial Neural Networks (ANN) simulates neural networks found in nature, such as the human brain.
• Fuzzy logic is an AI technique that deals with uncertainties by simulating the process of human reasoning,
allowing the computer to behave less precisely and logically than conventional computers do.
• Expert system are created with objective to transfer expertise from an expert to a computer and then on to other
humans. In developing such systems, designers usually work with experts to determine the information and
decision rules (heuristics) that the experts use to solve particular types of problems. An expert system is a form of
artificial intelligence that uses a knowledge base (KB) and inference engine to make decisions. Input for the
knowledge base is gathered through a user interface.
• Organizations must have a clear idea on how knowledge is discovered, created, dispersed, and put to use.
• Data relates to facts about business transactions; Information relates to description, definition, or perspective
(what, who, when, where); Knowledge comprises strategy, practice, method, or approach (how); and Wisdom
embodies principle, insight, moral, or archetype (why).

33. Summary
• Knowledge management (KM) is the access, retrieval and distribution of human experiences and relevant
information between related individuals or workgroups. The challenge of Knowledge Management is to
determine what information within an organization qualifies as "valuable." All information is not knowledge,
and all knowledge is not valuable.
• People, processes and technology are the new building blocks for corporate success in today's information-rich
markets. People are necessary for brainpower, innovation, creativity, and the experiential knowledge to solve
technical problems. An organization must also have effective and efficient business processes in place to create
a sharing, collective atmosphere. To support human innovation and progress, a basic technological
infrastructure must be in place to help leverage collective brainpower and corporate knowledge and deliver
new ideas and solutions quickly and practically.
• Business Intelligence (BI) is the user-centered process of exploring data, data relationships and trends - thereby
helping to improve overall decision making. BI empowers enterprises with systems that promote understanding
and action through facts and opinions; quality information; meaningful delivery; proliferation of data analysis;
and shared insights.
• Business intelligence applications and their associated data warehouses are not aligned and were viewed until
recently as strategic and tactical decision-making systems separate from the transactional applications that
manage day-to-day business operations.
• Intelligent business is BI integrated into operational business processes. It is also event driven. There is
automatic monitoring of business activity events as well as responding to requests for just-in-time business
intelligence which may or may not need to be integrated with other operational data on the fly before
delivering this data to applications. It also includes on-demand requests for predictive analysis to provide a
recommendation for example.
• Competitive Intelligence (CI) is the purposeful and coordinated monitoring of the organization competitor(s),
wherever and whoever they may be, within a specific marketplace. The major benefits of CI include - Improved
market knowledge, improved cross-functional relationships in the organization, greater confidence in making
strategic plans, and improvements in product quality versus the competition. In short, better business
performance through doing things better.