2. Objectives
1. What is knowledge management? Why do
businesses today need knowledge management
programs and systems for knowledge management?
2. What types of systems are used for enterprise-wide
knowledge management? How do they provide
value for organizations?
3. How do knowledge work systems provide value for
firms? What are the major types of knowledge work
systems?
3. Objectives
4. What are the business benefits of using
intelligent techniques for knowledge
management?
5. 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. 1. Designing knowledge systems that genuinely
enhance organizational performance
2. Identifying and implementing appropriate
organizational applications for artificial
intelligence
Management Challenges
5. • Knowledge
• Wisdom
• Tacit knowledge
• Explicit knowledge
Important Dimensions of Knowledge
The Knowledge Management Landscape
7. • Knowledge:
• Is a firm asset
• Has different forms
• Has a location
• Is situational
Important Dimensions of Knowledge
The Knowledge Management Landscape
8. • 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
Organizational Learning and Knowledge Management
The Knowledge Management Landscape
10. • Knowledge acquisition
• Knowledge storage
• Knowledge dissemination
• Knowledge application
The Knowledge Management Value Chain
The Knowledge Management Landscape
11. • 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
The Knowledge Management Value Chain
The Knowledge Management Landscape
12. • 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
Types of Knowledge Management Systems
Types of Knowledge Management Systems
13. Major types of knowledge management systems
Types of Knowledge Management Systems
Figure 11-3
14. • Structured knowledge
• Semistructured knowledge
• Knowledge repository
• Knowledge network
Structured Knowledge Systems
Enterprise-Wide Knowledge Management Systems
17. KPMG knowledge system processes
Enterprise-Wide Knowledge Management Systems
Figure 11-6
18. 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?
Window on Technology
Enterprise-Wide Knowledge Management Systems
19. • Taxonomy: Method of classifying things
according to a predetermined system
• Tagging: Once a knowledge taxonomy is
produced, documents are tagged with
proper classification
Organizing Knowledge: Taxonomies and Tagging
Enterprise-Wide Knowledge Management Systems
21. Key Functions of an Enterprise Knowledge Network
• Knowledge exchange services
• Community of practice support
• Auto-Profiling Capabilities
• Knowledge management services
Knowledge Networks
Enterprise-Wide Knowledge Management Systems
22. The problem of distributed knowledge
Enterprise-Wide Knowledge Management Systems
Figure 11-8
24. • Teamware: Group collaboration software
running on intranets that is customized for
teamwork
Portals, Collaboration Tools, and Learning Management Systems
Enterprise-Wide Knowledge Management Systems
25. • Learning Management Systems (LMS):
Tools for the management, delivery,
tracking, and assessment of various types
of employee learning
Portals, Collaboration Tools, and Learning Management Systems
Enterprise-Wide Knowledge Management Systems
26. 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
Window on Management
Enterprise-Wide Knowledge Management Systems
27. 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
Knowledge Workers and Knowledge Work
Knowledge Work Systems
29. • Computer-aided design (CAD)
• Virtual reality systems
• Virtual Reality Modeling Language (VRML)
• Investment workstations
Examples of Knowledge Work Systems
Knowledge Work Systems
30. • Knowledge Base: Model of human
knowledge
• Rule-based Expert System: Collection in
an AI system represented in the the form
of IF-THEN
Capturing Knowledge: Expert Systems
Intelligent Techniques
31. • 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
Capturing Knowledge: Expert Systems
Intelligent Techniques
32. Rules in an AI program
Intelligent Techniques
Figure 11-11
34. • 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
Capturing Knowledge: Expert Systems
Intelligent Techniques
35. • Galeria Kaufhof
• Countrywide Funding Corp.
Examples of Successful Expert Systems
Intelligent Techniques
36. • Case-based Reasoning (CBR): Artificial intelligence
technology that represents knowledge as a
database of cases and solutions
Organizational Intelligence: Case-Based Reasoning
Intelligent Techniques
38. • Rule-based AI
• Tolerates imprecision
• Uses nonspecific terms called membership
functions to solve problems
Fuzzy Logic Systems
Fuzzy Logic Systems
40. • Hardware or software emulating
processing patterns of biological brain
• Put intelligence into hardware in form of a
generalized capability to learn
Neural Networks
Neural Networks
41. How a neural network works
Neural Networks
Figure 11-15
42. • Problem-solving methods
• Promote evolution of solutions to specified
problems
• Use a model of living organisms adapting
to their environment
Genetic Algorithms
Genetic Algorithms
43. The components of a genetic algorithm
Genetic Algorithms
Figure 11-16
44. • Integration of multiple AI technologies into
a single application
• Takes advantage of best features of
technologies
Hybrid AI Systems
Genetic Algorithms
45. • 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
Intelligent Agents
Intelligent Agents
47. • 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
Implementation Challenges
Management Issues for Knowledge Management Systems
48. • 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
Implementation Challenges
Management Issues for Knowledge Management Systems
50. 1. Develop in stages
2. Choose a high-value business process
3. Choose the right audience
4. Measure ROI during initial implementation
5. Use the preliminary ROI to project enterprise-
wide values
Obtaining Value from Knowledge Management Systems
Obtaining Value from Knowledge Management Systems
51. 1. Analyze P&G’s business strategy using the
value chain and competitive forces
models.
2. What business and technology conditions
caused P&G to change its business
strategy? What management,
organization, and technology problems did
P&G face?
Can Knowledge Systems Help Procter & Gamble Stay Ahead of the Pack?
Chapter 11 Case Study
52. 3. 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.
4. 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.
Can Knowledge Systems Help Procter & Gamble Stay Ahead of the Pack?
Chapter 11 Case Study
