Transcript 15. MANAGING KNOWLEDGE

c h a p t e r
10
MANAGING
KNOWLEDGE:
KNOWLEDGE WORK
AND ARTIFICIAL
INTELLIGENCE
10.1
LEARNING OBJECTIVES
• EXPLAIN ORGANIZATIONAL KNOWLEDGE
MANAGEMENT
• DESCRIBE USEFUL APPLICATIONS FOR
DISTRIBUTING, CREATING, SHARING
KNOWLEDGE
• EVALUATE ROLE OF ARTIFICIAL
INTELLIGENCE IN KNOWLEDGE
MANAGEMENT
10.2
LEARNING OBJECTIVES
• DEMONSTRATE HOW ORGANIZATIONS
USE EXPERT SYSTEMS, CASE-BASED
REASONING TO CAPTURE KNOWLEDGE
10.3
QUAKER CHEMICAL
• Quaker Chemical, based, Pennsylvania, is a
worldwide producer of custom-formulated
chemical specialty products and fluid
management services.
• Clients:
– Makers of automotive
– Aerospace products
– Firms in the environment
– Can industries
– Pulp industries and paper industries.
10.4
QUAKER CHEMICAL
Recently underwent a global
reorganization so that its
sales staff now sells by
business line rather than
by geographic region.
10.5
QUAKER CHEMICAL
• Quaker needed a system that could
rise above regional barriers and
help its sales force:
– Track, manage, and collaborate on
client services and accounts
10.6
QUAKER CHEMICAL
• Developing new chemical formulas for
automakers
• Determining why sales at certain North
American mills are down.
• These problems could be more easily
solved if the company could gather
information stored on workers' computers
and in their heads and make it more
widely available.
10.7
QUAKER CHEMICAL
• Quaker doesn't have off-the-shelf
products.
• Quaker formulates unique products
for every customer.
capturing what Quaker's laboratory
workers know about Quaker
formulas is especially critical.
10.8
QUAKER CHEMICAL
• In mid-2000, Quaker launched a
KNOWLEDGE MANAGEMENT SYSTEM
called Quaker Business Intelligence, or
QBI.
• The system is a global intranet with
collaborative software from Intraspect
Software of Brisbane, California,
working in conjunction with Quaker's
Novell GroupWise collaboration
system
10.9
QUAKER CHEMICAL
• Employees can DROP word processing
documents, e-mail, Web pages,
presentations, and spreadsheets in
CENTRAL FILES into the system.
• They can SUBSCRIBE to certain folders
relating to their jobs in the same way they
would subscribe to an e-mail mailing list.
• The system ALERTS them automatically
when changes are made to the individual
files RELATING TO THEIR JOB tasks.
10.10
QUAKER CHEMICAL
• When an employee LOGS on to his or her
computer, the Intraspect software POPS UP
with the look and feel of a simple Web page.
Employees can CONTROL what is added to
their "HOME PAGE" or to the files belonging
to their group and they can customize their
home page to fit their job needs.
• The Intraspect system can be INTEGRATED
with a data warehouse that Quaker uses to
track its FINANCIAL INFORMATION.
10.11
QUAKER CHEMICAL
• About 800 of Quaker's 1,100
WORKERS use the Intraspect
system.
• According to Thomas Baker,
Quaker's manager of business
intelligence development, the
system stores nearly 27,000
documents in over 7,000 folders.
10.12
QUAKER CHEMICAL
• PREVIOUSLY, if a manager had trouble finding out
why a customer experienced SURFACE
IMPERFECTIONS while rolling steel in a specific
type of mill, he or she would E-MAIL OTHER
QUAKER COLLEAGUES.
• NOW that manager can INITIATE A DISCUSSION
within QBI, sending an alert to all managers in the
company's steel division.
• The system archives the message threads from
that discussion and these threads can be
ACCESSED LATER by workers trying to answer a
SIMILAR QUESTION.
10.13
QUAKER CHEMICAL
• Many of these benefits are DIFFICULT
TO QUANTIFY.
• However, Quaker claims that QBI has
already saved the company FOUR
MONTHS of labor worth $300,000 by
enabling THREE DIFFERENT LAB
SITES to access formulas stored in the
system rather than DUPLICATE the
research.
10.14
MANAGEMENT CHALLENGES
• Designing knowledge systems that
GENUINELY ENHANCE organizational
performance.
• Information systems that truly enhance
the productivity of knowledge workers
may be difficult to build because the
manner in which information technology
can enhance higher-level tasks, such as
those performed by MANAGERS and
professionals, is NOT ALWAYS CLEARLY
10.15
UNDERSTOOD.
MANAGEMENT CHALLENGES
• Some aspects of organizational knowledge
cannot be CAPTURED EASILY OR
CODIFIED.
• The information that organizations finally
manage to capture may become OUTDATED
as environments change.
• It is VERY DIFFICULT to INTEGRATE
knowledge management programs with
business strategy.
10.16
MANAGEMENT CHALLENGES
– IDENTIFYING and implementing
APPROPRIATE organizational
applications for ARTIFICIAL
INTELLIGENCE.
– Only CERTAIN KINDS of information
problems are APPROPRIATE for artificial
intelligence (AI) applications.
– Many AI applications IMPROVE
performance through TRIAL AND ERROR
and may not be reliable enough for
10.17MISSION-CRITICAL PROBLEMS.
MANAGEMENT CHALLENGES
– EXPERT SYSTEMS are EXPENSIVE and
TIME-CONSUMING to maintain because
their rules must be reprogrammed every
time there is a change in the
organizational environment.
– MANY THOUSANDS of businesses have
undertaken experimental projects in
expert systems, but ONLY A SMALL
percentage have created expert systems
that ACTUALLY CAN BE USED ON A
PRODUCTION BASIS.
10.18
10.19
KNOWLEDGE MANAGEMENT IN
THE ORGANIZATION
• As knowledge becomes a CENTRAL
PRODUCTIVE and STRATEGIC ASSET.
ORGANIZATIONAL SUCCESS increasingly
DEPENDS on the firm's ability to:
– PRODUCE KNOWLEDGE
– GATHER KNOWLEDGE
– STORE KNOWLEDGE
– DISSEMINATE KNOWLEDGE.
– With KNOWLEDGE, firms become more
EFFICIENT and EFFECTIVE in their use of
10.20scarce resources.
KNOWLEDGE MANAGEMENT IN
THE ORGANIZATION
• 55 percent of the U.S. labor force consists of
knowledge and information workers.
• Producing unique products or services or
producing them at a lower cost than competitors
is based on SUPERIOR KNOWLEDGE of the
production process and superior design.
• Knowing how to do things effectively and
efficiently in ways that other organizations cannot
duplicate is a PRIMARY SOURCE OF VALUE and a
factor in production that cannot be purchased in
external markets.
10.21
KNOWLEDGE MANAGEMENT IN
THE ORGANIZATION
• Some management theorists
believe that these KNOWLEDGE
ASSETS are as important as the
PHYSICAL and FINANCIAL
assets.
10.22
KNOWLEDGE MANAGEMENT IN
THE ORGANIZATION
• KNOWLEDGE MANAGEMENT refers to the
SET OF PROCESSES developed in an
organization to CREATE, GATHER, STORE,
TRANSFER, and APPLY knowledge.
• INFORMATION TECHNOLOGY plays an
important role in knowledge management by
SUPPORTING THESE BUSINESS
PROCESSES for creating, identifying, and
leveraging knowledge throughout the
organization.
10.23
KNOWLEDGE MANAGEMENT IN
THE ORGANIZATION
• Organizations create and gather knowledge
through a variety of ORGANIZATIONAL
LEARNING mechanisms.
• Through:
• TRIAL and ERROR
• CAREFUL MEASUREMENT of planned
activities.
• FEEDBACK from customers and the
environment in general.
Organizations create new SOPs and business
processes that reflect their experience
10.24
KNOWLEDGE MANAGEMENT IN
THE ORGANIZATION
CHIEF KNOWLEDGE OFFICER (CKO)
DIGITAL FIRM:
• The chief knowledge officer is a
SENIOR EXECUTIVE who is
responsible for the FIRM'S
KNOWLEDGE MANAGEMENT
PROGRAM.
10.25
Systems and Infrastructure for
Knowledge Management
• This knowledge base may include:
1. Structured, INTERNAL KNOWLEDGE (explicit
knowledge), such as product manuals or
research reports
2. EXTERNAL KNOWLEDGE of competitors,
products, and markets, including competitive
intelligence
3. INFORMAL, internal knowledge, often called
TACIT KNOWLEDGE, which resides in the minds
of individual employees but has not been
documented in structured form
10.26
Systems and Infrastructure for
Knowledge Management
Companies can use information systems to
codify their BEST PRACTICES and make
knowledge of these practices widely
available to employees
BEST PRACTICES
Successful solutions or problem-solving
methods developed by specific
organization or industry
10.27
Systems and Infrastructure for
Knowledge Management
• The knowledge can be PRESERVED as
ORGANIZATIONAL MEMORY to TRAIN
FUTURE employees or to help them with
decision making
ORGANIZATIONAL MEMORY
• Stored learning from organization’s history
• Used for decision making and other purposes
10.28
KNOWLEDGE MANAGEMENT IN THE
ORGANIZATION
KNOWLEDGE MANAGEMENT:
Office Automation Systems (OAS)
Knowledge Work Systems (KWS)
Group Collaboration Systems (GCS)
Artificial Intelligence Applications (AI)
*
10.29
10.30
INFORMATION AND
KNOWLEDGE WORK SYSTEMS
• Information Work is work that consists primarily
of CREATING OR PROCESSING INFORMATION.
• It is carried out by Information workers who
usually are divided into two subcategories:
1. DATA WORKERS, who primarily process and
disseminate information
2. KNOWLEDGE WORKERS, who primarily create
knowledge and information.
10.31
Distributing Knowledge: Office
and Document Management
Systems
• Most data work and a great
deal of knowledge work
takes place in offices,
using office systems
including most of the work
done by managers
10.32
Distributing Knowledge: Office
and Document Management
Systems
Office systems
• Manage and coordinate work of data and
knowledge workers
• Connect work of local information workers
with all levels and functions of organization
• Connect organization to external world
• Example: Word processing, voice mail, and
imaging
10.33
INFORMATION AND KNOWLEDGE WORK SYSTEMS
The Three Major Roles of Offices
Figure 10-2
10.34
OFFICE WORKERS
Office workers span a very
broad range: professionals,
managers, sales, and clerical
workers working alone or in
groups. Their major activities
include the following:
10.35
OFFICE AUTOMATION
SYSTEMS
MANAGING DOCUMENTS:
•
•
•
•
•
CREATION
STORAGE
RETRIEVAL
DISSEMINATION
TECHNOLOGY: Word processing,
desktop publishing, document
imaging, Web publishing, work flow
managers
10.36
OFFICE AUTOMATION
SYSTEMS
SCHEDULING:
FOR INDIVIDUALS & GROUPS:
• ELECTRONIC CALENDARS
• GROUPWARE
• INTRANETS
10.37
OFFICE AUTOMATION
SYSTEMS
COMMUNICATING:
INITIATING, RECEIVING, MANAGING:
• VOICE
• DIGITAL
• DOCUMENTS
• TECHNOLOGY: E-mail, voice mail,
digital answering systems, GroupWare,
intranets
*
10.38
OFFICE AUTOMATION SYSTEMS
MANAGING DATA:
EMPLOYEES, CUSTOMERS, VENDORS:
1. DESKTOP DATABASES
2. SPREADSHEETS
3. USER-FRIENDLY INTERFACES TO
MAINFRAME DATABASES
10.39
OFFICE AUTOMATION SYSTEMS
Document Management :
• DOCUMENT IMAGING SYSTEMS: Systems
convert documents, images into digital
form (e.g.: optical character recognition;
microfiche)
• JUKEBOX: Storage & retrieving device for
CD-ROMs & other optical disks
• INDEX SERVER: Imaging system to store /
retrieve document
*
10.40
INFORMATION AND KNOWLEDGE WORK SYSTEMS
Components of an Imaging System
10.41
Figure 10-3
INFORMATION AND KNOWLEDGE WORK SYSTEMS
Web Publishing and Document Management
Figure 10-4
10.42
CREATE KNOWLEDGE
KNOWLEDGE WORK SYSTEMS:
INFORMATION SYSTEMS THAT AID
KNOWLEDGE WORKERS TO CREATE,
INTEGRATE NEW KNOWLEDGE IN
ORGANIZATION
10.43
REQUIREMENTS OF
KNOWLEDGE WORK SYSTEMS
10.44
CREATE KNOWLEDGE
KNOWLEDGE WORKERS:
1. KEEP ORGANIZATION UP-TO-DATE IN
KNOWLEDGE: Technology; science;
thought; the arts
2. INTERNAL CONSULTANTS IN THEIR
AREAS.
3. CHANGE AGENTS: Evaluating; initiating;
promoting change projects
10.45
CREATE KNOWLEDGE
EXAMPLES KNOWLEDGE
SYSTEMS
•
•
CAD/CAM
Computer Aided Design/Computer Aided
Manufacturing: Provides precise control
over industrial design, manufacturing
VIRTUAL REALITY
Interactive software creates photorealistic
simulations of real world objects (Virtual
Reality Modeling Language: VRML)
10.46
CREATE KNOWLEDGE
EXAMPLES KNOWLEDGE
SYSTEMS
INVESTMENT WORKSTATIONS
• investment workstations is High-end PCs used in finance
to analyze trading situations, that integrate a wide range of
data from both internal and external sources, including
contact management data, real-time and historical market
data, and research reports.
• Previously, financial professionals had to spend
considerable time accessing data from separate systems
and piecing together the information they needed.
By providing one-stop information faster and with fewer
errors.
10.47
SHARE KNOWLEDGE
GROUP COLLABORATION SYSTEMS:
• GROUPWARE: Allows interactive
collaboration.
• Communication, Collaboration, and
coordination.
• It allows groups to work together on:
• Documents
• Schedule meetings,
• Access shared folders,
• Participate in electronic discussions
• Develop shared databases
• Send e-mail.
10.48
SHARE KNOWLEDGE
GROUP COLLABORATION SYSTEMS:
• INTRANETS: Good for relatively stable
information in central repository.
• Ford Motor Company
• Intranet delivers information about news,
people, processes, products, and
competition to 95,000 professional
employees.
• Employees can access online libraries and
a Web Center of Excellence with
information on best practices, standards,
and recommendations.
10.49
ENTERPRISE INFORMATION PORTALS
10.50
Figure 10-7
SHARE KNOWLEDGE
GROUP COLLABORATION SYSTEMS:
• TEAMWARE: Consists of intranet-based
applications for:
• Building a work team
• Sharing ideas and documents
• Brainstorming
• Scheduling, tracking the status of tasks and
projects
• Teamware is similar to groupware, although its
application development capabilities are not as
powerful as those provided by sophisticated
groupware products.
10.51
CAPABILITIES OF
GROUPWARE
•
•
•
•
•
•
10.52
PUBLISHING, REPLICATION
DISCUSSION TRACKING
DOCUMENT MANAGEMENT
WORK-FLOW MANAGEMENT
PORTABILITY
APPLICATION DEVELOPMENT
AI
ARTIFICIAL INTELLIGENCE
(AI) SYSTEMS:
AI: COMPUTER-BASED SYSTEMS
WITH ABILITIES TO LEARN
LANGUAGE, ACCOMPLISH TASKS,
USE PERCEPTUAL APPARATUS,
EMULATE HUMAN EXPERTISE &
DECISION MAKING
*
10.53
AI
AI FAMILY
ARTIFICIAL
INTELLIGENCE
NATURAL
LANGUAGE
10.54
ROBOTICS
PERCEPTIVE
SYSTEMS
EXPERT
SYSTEMS
INTELLIGENT
MACHINES
AI
BUSINESS INTERESTS
IN AI
1. PRESERVE EXPERTISE
2. CREATE KNOWLEDGE BASE
3. MECHANISM NOT SUBJECT TO
FEELINGS, FATIGUE, WORRY, CRISIS
4. ELIMINATE ROUTINE / UNSATISFYING
JOBS
5. ENHANCE KNOWLEDGE BASE
(generating solutions to specific
problems that are too massive and
complex to be analyzed by human beings
10.55
in a short period of time)
ARTIFICIAL INTELLIGENCE
Rules in an AI Program
10.56
Figure 10-9
AI
EXPERT SYSTEMS
KNOWLEDGE - INTENSIVE
CAPTURES HUMAN EXPERTISE
IN LIMITED DOMAINS OF
KNOWLEDGE
*
10.57
AI
EXPERT SYSTEMS
• KNOWLEDGE BASE: Model of Human
Knowledge
• RULE - BASED EXPERT SYSTEM : AI
system based on IF - THEN statements
(Bifurcation); Rule Base: Collection of IF THEN knowledge
• KNOWLEDGE FRAMES: Knowledge
organizes in chunks based on shared
relationships
10.58
*
AI
EXPERT SYSTEMS
• AI SHELL: Programming environment of
expert system
• INFERENCE ENGINE: Search through rule
base
– FORWARD CHAINING: Uses input;
searches rules for answer
– BACKWARD CHAINING: Begins with
hypothesis, seeks information until
hypothesis accepted or rejected
(Should we add this person to the
10.59
prospect database?)
ARTIFICIAL INTELLIGENCE
Figure 10-10
10.60
Building an Expert System
• The team members must select a problem
appropriate for an expert system.
• The project will balance potential savings from the
proposed system against the cost.
• The team members will develop a prototype
system to test assumptions about how to encode
the knowledge of experts.
• Next, they will develop a full-scale system,
focusing mainly on the addition of a very large
number of rules.
10.61
Building an Expert System
• The system will be pruned to achieve
simplicity and power.
• The system is tested by a range of experts
within the organization against the
performance criteria established earlier.
• Once tested, the system will be integrated
into the data flow and work patterns of the
organization.
10.62
Building an Expert System
Knowledge engineer
• Specialist extract information and
expertise from other professionals
• Translates information into set of rules for
an expert system
10.63
Examples of Expert Systems
• Countrywide Funding Corp. is a loan-underwriting
firm with about 400 underwriters in 150 offices
around the country.
• The company developed a PC-based expert
system in 1992 to make preliminary
creditworthiness decisions on loan requests.
• The company had experienced rapid, continuing
growth and wanted the system to help ensure
consistent, high-quality loan decisions.
• CLUES (Countrywide's Loan Underwriting Expert
System) has about 400 rules.
10.64
Examples of Expert Systems
• Countrywide tested the system by sending every loan
application handled by a human underwriter to CLUES as
well.
• The system was refined until it agreed with the
underwriters in 95 percent of the cases.
• Countrywide will not rely on CLUES to reject loans,
because the expert system cannot be programmed to
handle exceptional situations such as those involving a
self-employed person or complex financial schemes.
• An underwriter will review all rejected loans and will make
the final decision. CLUES has other benefits. Traditionally,
an underwriter could handle six or seven applications a
day.
• Using CLUES, the same underwriter can evaluate at least
16 per day.
10.65
AI
EXPERT SYSTEMS
LIMITATIONS:
• Often reduced to problems of
classification
• Can be large, lengthy, expensive
• Maintaining knowledge base critical
• Many managers unwilling to trust
such systems
*
10.66
AI
CASE - BASED
REASON (CBR)
AI USES DATABASE OF CASES:
• USER DESCRIBES PROBLEM
• SYSTEM SEARCHES DATABASE
FOR SIMILAR CASES
• SYSTEM ASKS MORE QUESTIONS
• FINDS CLOSEST FIT
• MODIFIED AS REQUIRED
*
10.67
10.68
Examples of CBR SYSTEMS
• Compaq Computer of Houston, Texas,
gave purchasers of its Pagemarq printer
case-based reasoning software to help
reduce customer service costs.
10.69
c h a p t e r
12
MANAGING
KNOWLEDGE:
KNOWLEDGE WORK
AND ARTIFICIAL
INTELLIGENCE
10.70