1. Managing Knowledge in the Digital Firm
Chapter 11
Managing Knowledge in the 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. Important Dimensions of Knowledge
The Knowledge Management Landscape
Important Dimensions of Knowledge
Knowledge
Wisdom
Tacit knowledge
Explicit knowledge

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

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

8. Organizational Learning and Knowledge Management
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 value chain
The Knowledge Management Landscape
The knowledge management value chain
Figure 11-2

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

11. The Knowledge Management Value Chain
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
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. Major types of knowledge management systems
Figure 11-3

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

15. Enterprise-wide knowledge management systems
Figure 11-4

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

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

18. DaimlerChrysler Learns to Manage Its Digital Assets
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. Organizing Knowledge: Taxonomies and Tagging
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. Hummingbird’s integrated knowledge management system
Enterprise-Wide Knowledge Management Systems
Hummingbird’s integrated knowledge management system
Figure 11-7

21. Key Functions of an Enterprise Knowledge Network
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. The problem of distributed knowledge
Enterprise-Wide Knowledge Management Systems
The problem of distributed knowledge
Figure 11-8

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

24. Portals, Collaboration Tools, and Learning Management Systems
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. Portals, Collaboration Tools, and Learning Management Systems
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. Managing Employee Learning: New Tools, New Benefits
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 Workers and Knowledge Work
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. Requirements of knowledge work systems
Figure 11-10

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

30. Capturing Knowledge: Expert Systems
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. Capturing Knowledge: Expert Systems
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. Inference engines in expert systems
Intelligent Techniques
Inference engines in expert systems
Figure 11-12

34. Capturing Knowledge: Expert Systems
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. Examples of Successful Expert Systems
Intelligent Techniques
Examples of Successful Expert Systems
Galeria Kaufhof
Countrywide Funding Corp.

36. Organizational Intelligence: Case-Based Reasoning
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. How case-based reasoning works
Intelligent Techniques
How case-based reasoning works
Figure 11-13

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

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

40. Hardware or software emulating processing patterns of biological brain
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. How a neural network works
Neural Networks
How a neural network works
Figure 11-15

42. Problem-solving methods
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. The components of a genetic algorithm
Genetic Algorithms
The components of a genetic algorithm
Figure 11-16

44. Integration of multiple AI technologies into a single application
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 agent technology at work
Intelligent Agents
Intelligent agent technology at work
Figure 11-17

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

50. 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

51. Can Knowledge Systems Help Procter & Gamble Stay Ahead of the Pack?
Chapter 11 Case Study
Can Knowledge Systems Help Procter & Gamble Stay Ahead of the Pack?
Analyze P&G’s business strategy using the value chain and competitive forces models.
What business and technology conditions caused P&G to change its business strategy? What management, organization, and technology problems did P&G face?

52. Can Knowledge Systems Help Procter & Gamble Stay Ahead of the Pack?
Chapter 11 Case Study
Can Knowledge Systems Help Procter & Gamble Stay Ahead of the Pack?
What is the role of knowledge management in supporting P&G’s business strategy? Explain how knowledge management systems help P&G execute its business strategy.
How successful has P&G been in pursuing its business strategy and using knowledge management? How successful do you think that strategy will be in the future? Explain your answer.