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Managing Knowledge in the Digital Firm

Chapter 11. Managing Knowledge in the Digital Firm. Objectives. What is knowledge management? Why do businesses today need knowledge management programs and systems for knowledge management?

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Managing Knowledge in the Digital Firm

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  1. Chapter 11 Managing Knowledge inthe Digital Firm

  2. Objectives • What is knowledge management? Why do businesses today need knowledge management programs and systems for knowledge management? • What types of systems are used for enterprise-wide knowledge management? How do they provide value for organizations? • How do knowledge work systems provide value for firms? What are the major types of knowledge work systems?

  3. Objectives • What are the business benefits of using intelligent techniques for knowledge management? • What major management issues and problems are raised by knowledge management systems? How can firms obtain value from their investments in knowledge management systems?

  4. Management Challenges • Designing knowledge systems that genuinely enhance organizational performance • Identifying and implementing appropriate organizational applications for artificial intelligence

  5. The Knowledge Management Landscape Important Dimensions of Knowledge • Knowledge • Wisdom • Tacit knowledge • Explicit knowledge

  6. The Knowledge Management Landscape U.S enterprise knowledge management software revenues, 2001-2006 Figure 11-1

  7. The Knowledge Management Landscape Important Dimensions of Knowledge • Knowledge: • Is a firm asset • Has different forms • Has a location • Is situational

  8. The Knowledge Management Landscape Organizational Learning and Knowledge Management • Organizational learning:Creation of new standard operating procedures and business processes reflecting experience • Knowledge management:Set of processes developed in an organization to create, gather, store, disseminate, and apply knowledge

  9. The Knowledge Management Landscape The knowledge management value chain Figure 11-2

  10. The Knowledge Management Landscape The Knowledge Management Value Chain • Knowledge acquisition • Knowledge storage • Knowledge dissemination • Knowledge application

  11. The Knowledge Management Landscape The Knowledge Management Value Chain • Chief Knowledge Officer (CKO): Senior executive in charge of the organization's knowledge management program • Communities of Practice (COP): Informal groups who may live or work in different locations but share a common profession

  12. Types of Knowledge Management Systems Types of Knowledge Management Systems • Enterprise Knowledge Management Systems: General purpose, integrated, and firm-wide systems to collect, store and disseminate digital content and knowledge • Knowledge Work Systems (KWS): Information systems that aid knowledge workers in the creation and integration of new knowledge in the organization • Intelligent Techniques: Datamining and artificial intelligence technologies used for discovering, codifying, storing, and extending knowledge

  13. Types of Knowledge Management Systems Major types of knowledge management systems Figure 11-3

  14. Enterprise-Wide Knowledge Management Systems Structured Knowledge Systems • Structured knowledge • Semistructured knowledge • Knowledge repository • Knowledge network

  15. Enterprise-Wide Knowledge Management Systems Enterprise-wide knowledge management systems Figure 11-4

  16. Enterprise-Wide Knowledge Management Systems KWorld’s knowledge domain Figure 11-5

  17. Enterprise-Wide Knowledge Management Systems KPMG knowledge system processes Figure 11-6

  18. Enterprise-Wide Knowledge Management Systems Window on Technology DaimlerChrysler Learns to Manage Its Digital Assets • What are the management benefits of using a digital asset management system? • How does ADAM provide value for DaimlerChrysler?

  19. Enterprise-Wide Knowledge Management Systems Organizing Knowledge: Taxonomies and Tagging • Taxonomy: Method of classifying things according to a predetermined system • Tagging: Once a knowledge taxonomy is produced, documents are tagged with proper classification

  20. Enterprise-Wide Knowledge Management Systems Hummingbird’s integrated knowledge management system Figure 11-7

  21. Enterprise-Wide Knowledge Management Systems Knowledge Networks Key Functions of an Enterprise Knowledge Network • Knowledge exchange services • Community of practice support • Auto-Profiling Capabilities • Knowledge management services

  22. Enterprise-Wide Knowledge Management Systems The problem of distributed knowledge Figure 11-8

  23. Enterprise-Wide Knowledge Management Systems AskMe Enterprise knowledge network system Figure 11-9

  24. Enterprise-Wide Knowledge Management Systems Portals, Collaboration Tools, and Learning Management Systems • Teamware: Group collaboration software running on intranets that is customized for teamwork

  25. Enterprise-Wide Knowledge Management Systems Portals, Collaboration Tools, and Learning Management Systems • Learning Management Systems (LMS): Tools for the management, delivery, tracking, and assessment of various types of employee learning

  26. Enterprise-Wide Knowledge Management Systems Window on Management Managing Employee Learning: New Tools, New Benefits • What are the management benefits of using learning management systems? • How do they provide value to Alyeska and APL

  27. Knowledge Work Systems Knowledge Workers and Knowledge Work Knowledge workers perform 3 key roles: • Keeping the organization current in knowledge as it develops in the external world • Serving as integral consultants regarding the areas of their knowledge, the changes taking place, and opportunities • Acting as change agents

  28. Knowledge Work Systems Requirements of knowledge work systems Figure 11-10

  29. Knowledge Work Systems Examples of Knowledge Work Systems • Computer-aided design (CAD) • Virtual reality systems • Virtual Reality Modeling Language (VRML) • Investment workstations

  30. Intelligent Techniques Capturing Knowledge: Expert Systems • Knowledge Base: Model of human knowledge • Rule-based Expert System: Collection in an AI system represented in the the form of IF-THEN

  31. Intelligent Techniques Capturing Knowledge: Expert Systems • AI shell: programming environment • Inference Engine: strategy used to search through the rule base • Forward Chaining: strategy for searching the rules base that begins with the information entered by user and searches the rule base to arrive at a conclusion

  32. Intelligent Techniques Rules in an AI program Figure 11-11

  33. Intelligent Techniques Inference engines in expert systems Figure 11-12

  34. Intelligent Techniques Capturing Knowledge: Expert Systems • Backward Chaining:Strategy for searching the rule base in an expert system that acts as a problem solver • Knowledge Engineer:Specialist who elicits information and expertise from other professionals and translates it into set of rules for an expert system

  35. Intelligent Techniques Examples of Successful Expert Systems • Galeria Kaufhof • Countrywide Funding Corp.

  36. Intelligent Techniques Organizational Intelligence: Case-Based Reasoning • Case-based Reasoning (CBR): Artificial intelligence technology that represents knowledge as a database of cases and solutions

  37. Intelligent Techniques How case-based reasoning works Figure 11-13

  38. Fuzzy Logic Systems Fuzzy Logic Systems • Rule-based AI • Tolerates imprecision • Uses nonspecific terms called membership functions to solve problems

  39. Fuzzy Logic Systems Implementing fuzzy logic rules in hardware Figure 11-14

  40. Neural Networks Neural Networks • Hardware or software emulating processing patterns of biological brain • Put intelligence into hardware in form of a generalized capability to learn

  41. Neural Networks How a neural network works Figure 11-15

  42. Genetic Algorithms Genetic Algorithms • Problem-solving methods • Promote evolution of solutions to specified problems • Use a model of living organisms adapting to their environment

  43. Genetic Algorithms The components of a genetic algorithm Figure 11-16

  44. Genetic Algorithms Hybrid AI Systems • Integration of multiple AI technologies into a single application • Takes advantage of best features of technologies

  45. Intelligent Agents Intelligent Agents • Software program that uses built-in or learned knowledge base to carry out specific, repetitive, and predictable tasks for an individual user, business process, or software application

  46. Intelligent Agents Intelligent agent technology at work Figure 11-17

  47. Management Issues for Knowledge Management Systems Implementation Challenges • Insufficient resources available to structure and update the content in repositories • Poor quality and high variability of content quality because of insufficient mechanisms • Content in repositories lacks context, making documents difficult to understand

  48. Management Issues for Knowledge Management Systems Implementation Challenges • Individual employees not rewarded for contributing content, and many fear sharing knowledge with others on the job • Search engines return too much information, reflecting lack of knowledge structure or taxonomy

  49. Management Issues for Knowledge Management Systems Implementing knowledge management projects in stages Figure 11-18

  50. Obtaining Value from Knowledge Management Systems Obtaining Value from Knowledge Management Systems • Develop in stages • Choose a high-value business process • Choose the right audience • Measure ROI during initial implementation • Use the preliminary ROI to project enterprise-wide values

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