managing knowledge in the digital firm
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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? - PowerPoint PPT PresentationTRANSCRIPT
Essentials of Management Information Systems, 6eEssentials of Management Information Systems, 6eChapter 11 Managing Knowledge in the Digital FirmChapter 11 Managing Knowledge in the Digital Firm
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Managing Knowledge inthe Digital Firm
Chapter 11
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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?
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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?
Objectives
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1. Designing knowledge systems that genuinely enhance organizational performance
2. Identifying and implementing appropriate organizational applications for artificial intelligence
Management Challenges
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• Knowledge
• Wisdom
• Tacit knowledge
• Explicit knowledge
Important Dimensions of Knowledge
The Knowledge Management Landscape
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U.S enterprise knowledge management software revenues, 2001-2006
The Knowledge Management Landscape
Figure 11-1
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• Knowledge:– Is a firm asset– Has different forms– Has a location– Is situational
Important Dimensions of Knowledge
The Knowledge Management Landscape
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• 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
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The knowledge management value chain
The Knowledge Management Landscape
Figure 11-2
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• Knowledge acquisition
• Knowledge storage
• Knowledge dissemination
• Knowledge application
The Knowledge Management Value Chain
The Knowledge Management Landscape
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• 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
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• 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
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Major types of knowledge management systems
Types of Knowledge Management Systems
Figure 11-3
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• Structured knowledge
• Semistructured knowledge
• Knowledge repository
• Knowledge network
Structured Knowledge Systems
Enterprise-Wide Knowledge Management Systems
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Enterprise-wide knowledge management systems
Enterprise-Wide Knowledge Management Systems
Figure 11-4
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KWorld’s knowledge domain
Enterprise-Wide Knowledge Management Systems
Figure 11-5
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KPMG knowledge system processes
Enterprise-Wide Knowledge Management Systems
Figure 11-6
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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
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• 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
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Hummingbird’s integrated knowledge management system
Enterprise-Wide Knowledge Management Systems
Figure 11-7
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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
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The problem of distributed knowledge
Enterprise-Wide Knowledge Management Systems
Figure 11-8
Essentials of Management Information Systems, 6eEssentials of Management Information Systems, 6eChapter 11 Managing Knowledge in the Digital FirmChapter 11 Managing Knowledge in the Digital Firm
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AskMe Enterprise knowledge network system
Enterprise-Wide Knowledge Management Systems
Figure 11-9
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• Teamware: Group collaboration software running on intranets that is customized for teamwork
Portals, Collaboration Tools, and Learning Management Systems
Enterprise-Wide Knowledge Management Systems
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• 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
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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
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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
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Requirements of knowledge work systems
Knowledge Work Systems
Figure 11-10
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• Computer-aided design (CAD)
• Virtual reality systems
• Virtual Reality Modeling Language (VRML)
• Investment workstations
Examples of Knowledge Work Systems
Knowledge Work Systems
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• 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
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• 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
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Rules in an AI program
Intelligent Techniques
Figure 11-11
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Inference engines in expert systems
Intelligent Techniques
Figure 11-12
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• 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
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• Galeria Kaufhof
• Countrywide Funding Corp.
Examples of Successful Expert Systems
Intelligent Techniques
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• Case-based Reasoning (CBR): Artificial intelligence technology that represents knowledge as a database of cases and solutions
Organizational Intelligence: Case-Based Reasoning
Intelligent Techniques
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How case-based reasoning works
Intelligent Techniques
Figure 11-13
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• Rule-based AI
• Tolerates imprecision
• Uses nonspecific terms called membership functions to solve problems
Fuzzy Logic Systems
Fuzzy Logic Systems
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Implementing fuzzy logic rules in hardware
Fuzzy Logic Systems
Figure 11-14
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• 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
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How a neural network works
Neural Networks
Figure 11-15
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• Problem-solving methods
• Promote evolution of solutions to specified problems
• Use a model of living organisms adapting to their environment
Genetic Algorithms
Genetic Algorithms
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The components of a genetic algorithm
Genetic Algorithms
Figure 11-16
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• Integration of multiple AI technologies into a single application
• Takes advantage of best features of technologies
Hybrid AI Systems
Genetic Algorithms
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• 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
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Intelligent agent technology at work
Intelligent Agents
Figure 11-17
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• 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
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• 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
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Implementing knowledge management projects in stages
Management Issues for Knowledge Management Systems
Figure 11-18
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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
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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
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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