ICS321 IBM205 Management Information Systems

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ICS321 / IBM205 Management Information Systems

• Dr. Ken Cosh
• Lecture 15

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Todays Topic

• Knowledge Management and Artificial Intelligence

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From Data to Wisdom

• Data symbols
• Information data that are processed to be
  useful provides answers to "who", "what",
  "where", and "when" questions
• Knowledge application of data and information
  answers "how" questions
• Understanding appreciation of "why"
• Wisdom evaluated understanding.
• (Ackoff)

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Challenge 1

• Designing KMS that enhance organisation
  performance
• How can IS/IT assist with high level management
  tasks? Such as motivation / decision making?
• Organisation knowledge is constantly changing,
  and so is constantly outdated as the environment
  changes.
• How can the benefits of KMS be linked to
  corporate strategy?

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Challenge 2

• Identifying and implementing appropriate
  organisational applications for A.I.
• A.I. works best with complex, repetitive
  information based activities.
• Many work by trial and error, so are completely
  inappropriate for mission critical tasks
• Expert systems are expensive to maintain as they
  need frequent changes to rules.

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Organisational Knowledge Management

• Producing unique products or services, or
  producing them at a lower cost than competitors
  is based on superior knowledge of the production
  process of superior design (Laudon Laudon)
• This knowledge asset is just as important as
  any other business asset (physical / financial)
• Managing this asset effectively can turn it into
  a strategic asset
• The challenge here is to produce, gather, store
  and disseminate this asset.

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Organisational Learning

• Organisations generate knowledge about themselves
  through
• Trial and error
• Careful measurement of planned activities
• Feedback from customers and the environment,
• This Organisational Learning is used to develop
  processes and procedures.
• With rapid response to changes, organisations can
  learn to sense and respond to its environment.

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And M.I.S.?

• All the major ISs that we have discussed already
  facilitate knowledge management.
• Data -gt information -gt knowledge!
• Such as
• SCM, CRM, ERP
• Internal and External Networks
• Databases and Datamining
• Communication Technologies

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Knowledge Management Systems

• Concerns the gathering use of knowledge
• External Knowledge
• From Suppliers, Customers
• Structured Internal Knowledge
• Guides, Manuals etc.
• Informal Internal Knowledge
• Or Tacit Knowledge
• The knowledge of the workforce

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Definition of IS (again)

• An information system can be any organised
  combination of people, hardware, software,
  communications networks, and data resources that
  collects, transforms and disseminates information
  in an organisation. (OBrien).

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Knowledge Management

• Distribute Knowledge
• Office Systems
• Word Processing, Desktop Publishing.
• Create Knowledge
• Knowledge Work Systems
• CAD, VR
• Share Knowledge
• Group Collaboration Systems
• Groupware, Intranets
• Capture and Codify Knowledge
• A.I. Systems
• Expert Systems, Neural Networks, Fuzzy Logic

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Distributing Knowledge

• IS/IT now plays a huge role in distributing
  knowledge.
• Office systems are used to turn knowledge into a
  presentable format.
• Email is used to distribute digital knowledge
• Scanners and OCR are used to make physical
  knowledge into digital knowledge
• Client Server networks are used to store and
  distribute knowledge

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Creating Knowledge

• The internet
• Keeping Managers up to date with the external
  world knowledge, such as the latest technology,
  science, social thought, arts etc.
• For internal knowledge, CAD is a good example of
  IS generated knowledge

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CAD

• Automating the creation and revision of designs
  through graphical software.
• Much more effective than physical design, but can
  generate many revisions through testing, hence
  internal knowledge.
• CAD goes VR
• One of the more recent advances in CAD is VR for
  CAD.
• Better processing power and graphical software
  enables designers to use VR for design.

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Sharing Knowledge

• Key Technologies
• Email, Teleconferencing, Dataconferencing,
  Videoconferencing, groupware
• Basically enabling physically distant people to
  communicate, collaborate and co-ordinate, forming
  a community of practice

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A.I. (to capture and codify knowledge)

• Developing computer based systems that behave as
  humans
• Natural Language
• Linguistical Tasks
• Robotics
• Physical Tasks
• Perceptive Systems
• Providing information about their environments
• Expert Systems
• Mimic human expertise / decision making
• Intelligent Machines
• Logic, reasoning, intuiting, common sense

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Why A.I. for business?

• To store information in an active form as
  organisational memory, creating an organisaitonal
  knowledge base that many employees can examine
  and preserving expertise that might be lost when
  an acknowledged expert leaves the firm.
• To eliminate routine and unsatisfying jobs held
  by people.

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Why A.I. for business?

• To create a mechanism that is not subject to
  human feelings, such as fatigue and worry. This
  may be especially useful when jobs may be
  environmentally, physically or mentally dangerous
  to humans. These systems can be used as advisors
  in times of crisis.
• To enhance the organisations knowledge base by
  generating solutions to specific problems that
  are too massive and complex to be analysed by
  humans in a short period of time.

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Approaches to A.I.

• Rule Based A.I.
• Statistical Based A.I.
• Fuzzy Logic

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Rule Based A.I.

• Requires experts in the field to set up a
  selection of rules determining the expected
  action depending on different circumstances.
• IF XXXX is true, THEN do this.
• Fundamentally pseudo code.

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Statistically Based A.I.

• Basing intelligence on statistical grounding
• State A is true 99 of the time under condition
  B, therefore as we are in condition B, we assume
  State A to be true now.

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Fuzzy Logic

• Easier Rules from rule based A.I.
• If X is nearly true AND Y may happen soon, then
  consider doing Z.
• Instructions are now less precise than Rules