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• Module overview
• 1. Introduction to knowledge management
  • 1.1 Knowledge management in organisations: Theory and practice
  • 1.2 Information and knowledge
    • 1.2.1 Distinguishing information, data, knowledge and wisdom
    • 1.2.2 Knowledge defined and managed

• 2. Type and structure of knowledge

  • 2.1 Type and structure of knowledge
    • 2.1.1 The structure of purposeful knowledge in an organisation
    • 2.1.2 Explicit and tacit knowledge
  • 2.2 Acquiring knowledge
    • 2.2.1 Sources of knowledge

• 3. Knowledge and learning

  • 3.1 The ability to learn as knowledge
  • 3.2 Types of knowledge and culture, meaning, and learning
  • 3.3. Knowledge management and balanced learning strategies

• 4. A knowledge management cycle

  • 4.1 A Knowledge Management Framework
    • 4.1.1 Stages in KM Cycle
  • 4.2 Dimensions to KM Strategy
    • 4.2.1 Leadership dimension to KM
    • 4.2.2 Governance and policy dimension
    • 4.2.3 Architecture dimension
    • 4.2.4 Absorption dimension
    • 4.2.5 Measurement dimension
  • 4.3 Knowledge as a basis for organising

• 5. Conduct a knowledge audit

  • 5.1 Knowledge audit
    • 5.1.1 Conducting a knowledge audit
    • 5.1.2 The Audit Process

• 6. Organisational Knowledge Capital

  • 6.1 Building an organisational Knowledge Capital framework
    • 6.1.1 IC and KC
    • 6.1.2 The value of knowledge
  • 6.2 Structural capital
  • 6.3 Social capital
  • 6.4 Human Capital defined
    • 6.4.1 Capital resources for organisations
    • 6.4.2 The individual as a capital resource
    • 6.4.3 Ingredients to HCM

• 7. Valuing forms of knowledge

  • 7.1 Valuing forms of knowledge assets
    • 7.1.1 Valuing Intellectual assets
    • 7.1.2 Valuing knowledge as an intangible asset

• 8. Information management systems

  • 8.1 Information management systems
    • 8.1.1 A basic information management framework
    • 8.1.2 Building systems to meet individual and organisational needs
    • 8.1.3 Information guardians versus stakeholders
  • 8.2 Structure and information transfer
    • 8.2.1 Information relevance and timeliness
    • 8.2.2 Information characteristics versus decision types
    • 8.2.3 Storage and retrieval

• 9. Information capture and intelligence gathering

  • 9.1 Information capture and intelligence gathering
    • 9.1.1 Developing a 'wish list'
    • 9.1.2 Finding answers
    • 9.1.3 Integrating intelligence within a knowledge management framework
  • 9.2 Forecasting methods
    • 9.2.1 Issues to consider when forecasting

• 10. IT and E-commerce

  • 10.1 An overview of e-commerce
  • 10.2 The Internet, computer networks and e-commerce
    • 10.2.1 Internet speed, innovation and change
  • 10.3 E-commerce advantages and disadvantages
    • 10.3.1 E-commerce advantages
    • 10.3.2 E-commerce disadvantages and constraints
  • 10.4 The Internet and business
    • 10.4.1 Drivers of e-commerce and Internet growth
  • 10.5 E-commerce and e-business

• 11. IT and supply chain management

  • 11.1 Electronic supply chain management (eSCM)
  • 11.2 E-logistics
    • 11.2.1 Are e-logistics and e-commerce the same thing?
    • 11.2.2 E-logistics blurs older logistic business categories
  • 11.3 E-procurement
  • 11.4 E-fulfilment

9.2 Forecasting methods

The type of a business and an industry sector will have a major bearing upon what type of forecasting methods are used within the organisation. However, as more computerised data is now available, there is a distinct trend to move towards more quantitative methods for data analysis, trend identification and forecasting.

Judgemental or subjective methods for forecasting include:

• Jury of expert opinion
• Sales force composite
• Customer expectations

Quantitative methods include:

• Moving average
• Straight line projection
• Exponential smoothing
• Regression
• Trend line analysis
• Simulation
• Life cycle analysis

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