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KELOMPOK 8Stephanie d.a.g 12030112130138Yohana ambarita 12030112130199Indika sandra agita 12030112130277Astri dias maharani 12030112140173Arini nur khulil j. 12030112140212
MANAGING KNOWLEDGE
The Knowledge Management Landscape
Important Dimension of Knowledge1. Knowledge is an intangible asset2. Knowledge has different forms3. Knowledge has a location4. Knowledge is situational
The Knowledge Management Landscape
Organizational LearningProcess in which organization learn :- Gain experience through collection of data, measurement, trial and error, and feedback- Adjust behavior to reflect experience
The Knowledge Management Landscape
Knowledge ManagementSet of business processes developed in an organization to create, store, transfer, and apply knowledge
The Knowledge Management Landscape
Knowledge Management Value Chain1. Knowledge acquisition - Documenting tacit and explicit knowledge - Creating knowledge - Tracking data from TPS and external source
The Knowledge Management Landscape
Knowledge Management Value Chain2. Knowledge Storage - Databases - Document management systems - Role of management
The Knowledge Management Landscape
Knowledge Management Value Chain3. Knowledge Dissemination Training programs, informal networks, and shared management experience communi- cated through a supportive culture help managers focus their attention on the important knowledge and information
The Knowledge Management Landscape
Knowledge Management Value Chain4. Knowledge Application Knowledge that is not shared and applied to the practical problems facing firms does not add business value
The Knowledge Management Landscape
Types of Knowledge Management Systems1. Enterprise-wide knowledge management systems2. Knowledge work systems3. Intelligent technique
Enterprise-Wide Knowledge Management Systems
Three major types of knowledge in enterprise1. Structured documents Reports, presentations Formal rules
2. Semi structured documents E-mails, videos
3. Unstructured, tacit knowledge
80% of an organization’s business content is semistructured or unstructured
Enterprise content management systemsHelp capture, store, retrieve, distribute,
preserve Documents, reports, best practices Semistructured knowledge (e-mails)
Bring in external sources News feeds, research
Tools for communication and collaboration
Enterprise-Wide Knowledge Management Systems
AN ENTERPRISE CONTENT MANAGEMENT SYSTEM
Enterprise content management systems
Key problem – Developing taxonomy Knowledge objects must be tagged with categories
for retrieval Digital asset management systems
Specialized content management systems for classifying, storing, managing unstructured digital data
Photographs, graphics, video, audio
Enterprise-Wide Knowledge Management Systems
Knowledge network systems Provide online directory of corporate experts in well-defined
knowledge domains Use communication technologies to make it easy for employees to
find appropriate expert in a company May systematize solutions developed by experts and store them in
knowledge database Best-practices Frequently asked questions (FAQ) repository
Enterprise-Wide Knowledge Management Systems
Enterprise-Wide Knowledge Management Systems
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A knowledge network maintains a database of firm experts, as well as accepted solutions to known problems, and then facilitates the communication between employees looking for knowledge and experts who have that knowledge. Solutions created in this communication are then added to a database of solutions in the form of FAQs, best practices, or other documents
Enterprise-Wide Knowledge Management Systems
Portal and collaboration technologies Enterprise knowledge portals: Access to external and internal
information News feeds, research Capabilities for e-mail, chat, videoconferencing, discussion
Use of consumer Web technologies Blogs Wikis Social bookmarking
Enterprise-Wide Knowledge Management Systems
Learning management systems Provide tools for management, delivery, tracking, and assessment
of various types of employee learning and training Support multiple modes of learning
CD-ROM, Web-based classes, online forums, live instruction, etc. Automates selection and administration of courses Assembles and delivers learning content Measures learning effectiveness
Enterprise-Wide Knowledge Management Systems
Knowladge worker perfom three key roles that are critical to the organization and to the managers who work within organozation :
keeping the organization current in knowladge as it develops in the external world-in technology ,science, soscial thought and the arts
Serving as internal consultants regarding the areas of their knowladge, the changes taking place, and opportunities
Acting as change agents, evaluating, intuiting, and promoting change projects
Knowledge Work Systems
Requirement of Knowladge Work Systems
Knowledge Work Systems
Examples of knowledge work systemsCAD
VIRTUAL REALITY SYSTEMS
AUGMENTED REALITY
VIRTUAL REALITY MODELING LANGUAGE(VRML)
Intelligent Technique
Intelligent techniques: Used to capture individual and collective knowledge and to extend knowledge base To capture tacit knowledge: Expert systems, case-based
reasoning, fuzzy logic Knowledge discovery: Neural networks and data mining Generating solutions to complex problems: Genetic
algorithms Automating tasks: Intelligent agents
Artificial intelligence (AI) technology: Computer-based systems that emulate human behavior
Intelligent Technique
Expert systems: Capture tacit knowledge in very specific and limited
domain of human expertise Capture knowledge of skilled employees as set of
rules in software system that can be used by others in organization
Typically perform limited tasks that may take a few minutes or hours, e.g.: Diagnosing malfunctioning machine Determining whether to grant credit for loan
Rules In A Expert System
Intelligent Technique
How expert systems work Knowledge base: Set of hundreds or thousands of
rules Inference engine: Strategy used to search
knowledge base Forward chaining: Inference engine begins with
information entered by user and searches knowledge base to arrive at conclusion
Backward chaining: Begins with hypothesis and asks user questions until hypothesis is confirmed or disproved
Intelligent Technique
Interference Engines In Expert System
Intelligent Technique
Case-based reasoning (CBR) Descriptions of past experiences of human specialists (cases),
stored in knowledge base System searches for cases with problem characteristics similar to
new one, finds closest fit, and applies solutions of old case to new case
Successful and unsuccessful applications are grouped with case Stores organizational intelligence: Knowledge base is
continuously expanded and refined by users CBR found in
Medical diagnostic systems Customer support
Intelligent Technique
How Case-based Reasoning Works
Intelligent Technique
Intelligent Technique
Fuzzy logic systems Rule-based technology that represents
imprecision used in linguistic categories (e.g., “cold,” “cool”) that represent range of values
Describe a particular phenomenon or process linguistically and then represent that description in a small number of flexible rules
Provides solutions to problems requiring expertise that is difficult to represent with IF-THEN rules Autofocus in cameras
How A Neural Network Works
Intelligent Technique
GENETIC ALGORITHMSUseful for finding the optimal solution for a specific problem by examining a very large
number of possible solution for that problem
Intelligent Technique
HYBRID AI SYSTEMSGenetic algorithms, fuzzy logic, neural
networks and expert systems can be integrated into a single application to take
advantage of the best features of these technologies
Intelligent Technique
Intelligent Agents in P&G’s Supply Chain Network
Intelligent Technique
Case Study
1. The conditions preceding the flash crash involved fearful and uncertain investors is stock market that was “already down and trending even lower”.
2. The benefits of electronic trading are “speed, reduced cost, and more liquid markets” and the use of algorithms that learn what will work in the market and “adjust their stock trading strategies” based on what the market is doing.
3. The features of electronic trading and automated trading programs that contributed to the crash were the “computer selling algorithm that dumped 75,000 contracts” within twenty minutes triggering other HFTs that used algorithms to begin selling as well.
4. The only way that the flash crash could have been prevented would be if computer were never allowed to have control over buying and selling. When you give computers this type of control, no matter what the limitations are, there is always the potential for something to go very wrong. Unless humans are given back the power.