Chapter 10KnowledgeManagementandIntelligentSystems
Learning Objectives•   Intelligent Systems•   Artificial Intelligence (AI)•   Applications of AI•   Knowledge Management• ...
Intelligent System• An intelligent system learns how to act so it  can reach its objectives. An intelligent system  learns...
Artificial Intelligence (AI)• Artificial intelligence is the science of making  machines does things that would require  i...
Applications of Artificial Intelligence  • Expert systems     – Human knowledge stored on machine for use in problem-     ...
Applications of AI•   Artificial Neural Network•   Fuzzy Logic•   Genetic Algorithm•   Expert Systems
Artificial Neural Networks (ANN)• ANN attempts to emulate the processing  patterns of the biological bra ins.• Artificial ...
Fuzzy Logic• Fuzzy logic is an AI technique that deals with  uncertainties by simulating the process of human  reasoning, ...
Genetic Algorithms• A genetic algorithm is a type of search  algorithm that takes input and computes an  output, where mul...
Expert Systems• Expert systems (ES) are computer-based  systems that transfer expertise from an expert  to a computer and ...
Components of Expert Systems•   Every expert system consists of:•   Knowledge Base•   Inference Engine•   Blackboard•   Us...
Business Applications of AI•   Finance     – Insurance evaluation, credit analysis, tax planning, financial planning and  ...
Businesses Challenges• Growing emphasis on creating customer value and  improving customer service;• An increasingly compe...
Some Facts  – A collection of data is not information.  – A collection of information is not knowledge.  – A collection of...
Data-Information-Knowledge-Wisdom            Relationship
Definition Summary• Information relates to description, definition,  or perspective (what, who, when, where).• Knowledge c...
Knowledge Management• Knowledge management (KM) is the access,  retrieval and distribution of human experiences  and relev...
Knowledge Management• The key to knowledge management is to get the necessary  knowledge to the necessary person(s) within...
Essential Components of KM• People to relay past experience and generate  new ideas (innovation);• processes for sharing a...
Characteristics of Knowledge            Management• The challenge of Knowledge Management is to  determine what informatio...
Advantages that KM Offer• Facilitates better, more informed decisions;• Contributes to the intellectual capital of an  org...
Components of Knowledge             Management• People: People are necessary for brainpower,  innovation, creativity, and ...
Business Intelligence (BI)• Business Intelligence (BI) is the user-centered  process of exploring data, data relationships...
Users’ BI Usage Pattern
BI Issues•   Unrealistic Expectations•   Limiting Access to Results•   Poor Data Quality•   Resistance to Change•   Winnin...
Intelligent Business• Intelligent business is a fundamental shift in thinking for  the world of data warehousing and busin...
Intelligent Business offers• On-demand requests for specific intelligence e.g.  about a specific customer• On-demand reque...
Competitive Intelligence (CI)• Competitive Intelligence (CI) is the purposeful  and coordinated monitoring of the  organiz...
Goals of Competitive Intelligence• Adopt a strategic approach to the use of  competitive intelligence;• To see the intelli...
Implementing CI• How clearly the organization has defined its  mission, its strategic intentions, its objectives and  its ...
CI Framework•   Assessment of strategies•   Competitor perceptions•   Effectiveness of current operations•   Competitor ca...
Summary•   Intelligent systems are one that learns from the environment and its past actions. One can create intelligent  ...
Summary•   Knowledge management (KM) is the access, retrieval and distribution of human experiences and relevant    inform...
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Chapter10

  1. 1. Chapter 10KnowledgeManagementandIntelligentSystems
  2. 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. 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. 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. 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. 6. Applications of AI• Artificial Neural Network• Fuzzy Logic• Genetic Algorithm• Expert Systems
  7. 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. 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 operators experience rather than their technical understanding of the system.• It uses imprecise and yet very descriptive terms of what must actually happen.
  9. 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. 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. 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. 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 12
  13. 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. 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. 15. Data-Information-Knowledge-Wisdom Relationship
  16. 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. 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. 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 doesnt 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. 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. 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. 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. 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. 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. 24. Users’ BI Usage Pattern
  25. 25. BI Issues• Unrealistic Expectations• Limiting Access to Results• Poor Data Quality• Resistance to Change• Winning It
  26. 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. 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. 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 competitors plans and help the organization to plan their business strategy to countervail their competitor’s plans.
  29. 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. 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. 31. CI Framework• Assessment of strategies• Competitor perceptions• Effectiveness of current operations• Competitor capabilities• Long-term market prospects
  32. 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. 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 todays 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.

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