6. Knowledge Management

7/28/2019 6. Knowledge Management

1/54

Management Information Systems

Knowledge management systems among fastestgrowing areas of software investment

Information economy

55% U.S. labor force: knowledge and information workers

60% U.S. GDP from knowledge and information sectors

Substantial part of a firms stock market value is

related to intangible assets: knowledge, brands,reputations, and unique business processes

Well-executed knowledge-based projects can

produce extraordinary ROI

The Knowledge Management Landscape

1


2/54


Knowledge Management

Artificial Intelligence

Expert system

Prentice Hall 20112


3/54


Important dimensions of knowledge

Knowledge is a firm asset

Intangible

Creation of knowledge from data, information, requiresorganizational resources

As it is shared, experiences network effects

Knowledge has different forms

May be explicit (documented) or tacit (residing in minds)

Know-how, craft, skill

How to follow procedure

Knowing why things happen (causality)


3


4/54


Knowledge management: Set of business processesdeveloped in an organization to create, store,transfer, and apply knowledge

Knowledge management value chain:

Each stage adds value to raw data and information asthey are transformed into usable knowledge

1.Knowledge acquisition2.Knowledge storage

3.Knowledge dissemination

4.Knowledge application


Prentice Hall 20114


5/54


Knowledge management value chain

1. Knowledge acquisition

Documenting tacit and explicit knowledge Storing documents, reports, presentations, best

practices

Unstructured documents (e.g., e-mails)

Developing online expert networks

Creating knowledge

Tracking data from TPS and external sources


Prentice Hall 20115


6/54


Knowledge management value chain (cont.)

2. Knowledge storage

Databases Document management systems

Role of management:

Support development of planned knowledge storage systems

Encourage development of corporate-wide schemas forindexing documents

Reward employees for taking time to update and store

documents properly


Prentice Hall 20116


7/54



3. Knowledge dissemination

Portals Push e-mail reports

Search engines

Collaboration tools

A deluge of information?

Training programs, informal networks, and shared

management experience help managers focus

attention on important information


Prentice Hall 20117


8/54



4. Knowledge application

To provide return on investment, organizationalknowledge must become systematic part ofmanagement decision making and becomesituated in decision-support systems

New business practices

New products and services

New markets


Prentice Hall 20118


9/54



THE KNOWLEDGE MANAGEMENT VALUE CHAIN

Knowledge management today involves both information systems activities and a host of enabling

management and organizational activities.

Prentice Hall 20119


10/54


New organizational roles and responsibilities

Chief knowledge officer executives

Dedicated staff / knowledge managers

Communities of practice (COPs)

Informal social networks of professionals andemployees within and outside firm who have similarwork-related activities and interests

Activities include education, online newsletters, sharingexperiences and techniques

Facilitate reuse of knowledge, discussion

Reduce learning curves of new employees


Prentice Hall 201110


11/54


3 major ways of knowledge management systems:

1. Enterprise-wide knowledge management systems General-purpose firm-wide efforts to collect, store,

distribute, and apply digital content and knowledge

2. Knowledge work systems (KWS) Specialized systems built for engineers, scientists, other

knowledge workers charged with discovering and

creating new knowledge3. Intelligent techniques

Diverse group of techniques such as data mining usedfor various goals: discovering knowledge, distillingknowledge, discovering optimal solutions




12/54



MAJOR TYPES OF KNOWLEDGE MANAGEMENT SYSTEMS

There are three major categories of knowledge management systems, and each can be broken down

further into more specialized types of knowledge management systems.



13/54


Three major types of knowledge in enterprise

1. Structured documents

Reports, presentations

Formal rules

2. Semistructured documents

E-mails, videos

3. Unstructured, tacit knowledge

80% of an organizations business content issemistructured or unstructured

Enterprise-Wide Knowledge Management Syste



14/54


Enterprise content management systems

Help 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



15/54



AN ENTERPRISE CONTENT MANAGEMENT SYSTEM

An enterprise content management system has capabilities for classifying, organizing, and managing

structured and semistructured knowledge and making it available throughout the enterprise.



16/54


Enterprise content management systems

Key problem Developing taxonomy

Knowledge objects must be tagged withcategories for retrieval

Digital asset management systems

Specialized content management systems forclassifying, storing, managing unstructureddigital data

Photographs, graphics, video, audio




17/54


Knowledge network systems

Provide online directory of corporate experts inwell-defined knowledge domains

Use communication technologies to make it easyfor employees to find appropriate expert in acompany

May systematize solutions developed by expertsand store them in knowledge database

Best-practices

Frequently asked questions (FAQ) repository




18/54



AN ENTERPRISE

KNOWLEDGE

NETWORK SYSTEM

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.



19/54


Portal and collaboration technologies

Enterprise knowledge portals: Access to externaland internal information

News feeds, research

Capabilities for e-mail, chat, videoconferencing,discussion

Use of consumer Web technologies

Blogs

Wikis

Social bookmarking




20/54


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, liveinstruction, etc.

Automates selection and administration of courses

Assembles and delivers learning content

Measures learning effectiveness




21/54


Knowledge work systems Systems for knowledge workers to help create new

knowledge and integrate that knowledge into business

Knowledge workers Researchers, designers, architects, scientists, engineers

who create knowledge for the organization

Three key roles:

1. Keeping organization current in knowledge2. Serving as internal consultants regarding their areas of

expertise

3. Acting as change agents, evaluating, initiating, andpromoting change projects

Knowledge Work Systems


f


22/54


Requirements of knowledge work systems

Substantial computing power for graphics,complex calculations

Powerful graphics and analytical tools

Communications and document management

Access to external databases

User-friendly interfaces

Optimized for tasks to be performed (designengineering, financial analysis)



M I f i S


23/54



REQUIREMENTS OF

KNOWLEDGE WORK

SYSTEMS

Knowledge work systems

require strong links to

external knowledge basesin addition to specialized

hardware and software.


M I f i S


24/54


Examples of knowledge work systems

CAD (computer-aided design): Creation of engineering or architectural designs

Virtual reality systems: Simulate real-life environments

3-D medical modeling for surgeons

Augmented reality (AR) systems

VRML

Investment workstations: Streamline investment process and consolidate

internal, external data for brokers, traders, portfoliomanagers




25/54

What is Artificial Intelligence ?

making computers that think?

the automation of activities we associate with human thinking,

like decision making, learning ... ?

the art of creating machines that perform functions that

require intelligence when performed by people ?

the study of mental faculties through the use of computational

models ?


26/54

What is Artificial Intelligence ?

the study of computations that make it possible to

perceive, reason and act ?

a field of study that seeks to explain and emulate

intelligent behaviour in terms of computational processes

?

a branch of computer science that is concerned with the

automation of intelligent behaviour ?

anything in Computing Science that we don't yet knowhow to do properly ? (!)


27/54

Search Search is the fundamental technique of AI.

Possible answers, decisions or courses of action are structured into anabstract space, which we then search.

Search is either "blind" or informed":

blind

we move through the space without worrying about

what is coming next, but recognising the answer if wesee it

informed

we guess what is ahead, and use that information to

decide where to look next. We may want to search for the first answer that satisfies our goal, or we

may want to keep searching until we find the best answer.


28/54

Knowledge Representation &

Reasoning

The second most important concept in AI If we are going to act rationally in our environment, then we must have

some way of describing that environment and drawing inferences from

that representation.

how do we describe what we know about the world ?

how do we describe it concisely?

how do we describe it so that we can get hold of the right piece of

knowledge when we need it ?

how do we generate new pieces of knowledge ?

how do we deal with uncertain knowledge ?

M t I f ti S t


29/54


Expert systems:

Capture tacit knowledge in very specific and limiteddomain of human expertise

Capture knowledge of skilled employees as set ofrules in software system that can be used by othersin 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

Used for discrete, highly structured decision-making

Intelligent Techniques



30/54

What is an Expert system(ES)?CASE: GE Models Human Troubleshooters

Problem: GE wanted an effective & dependable way of disseminating expertise

to its engineers & preventing valuable knowledge from retiring from

the company.

Solution:

GE decided to build an expert system that modeled the way a humantroubleshooter works.

The system builders spend several months interviewing an employee& transfer their knowledge to a computer.

The new diagnostic technology enables a novice engineer to uncovera fault by spending only a few minutes at the computer terminal.

Results:

The system is currently installed at every railroad repair shop servedby GE.

30



31/54



RULES IN AN

EXPERT SYSTEM

An expert system contains

a number of rules to be

followed. The rules are

interconnected; the

number of outcomes is

known in advance and is

limited; there are multiple

paths to the same

outcome; and the system

can consider multiple rules

at a single time. The rules

illustrated are for simple

credit-grantingexpert systems.




32/54


How expert systems work

Knowledge base: Set of hundreds or thousands ofrules

Inference engine: Strategy used to search knowledgebase

Forward chaining: Inference engine begins withinformation entered by user and searches knowledge

base to arrive at conclusion Backward chaining: Begins with hypothesis and asks

user questions until hypothesis is confirmed ordisproved


CHAPTER 11: MANAGING KNOWLEDGE



33/54

The Structure of ES

Human Expert Problem solving

33

LongTerm Memory

Domain Knowledge

Reasoning

Advisee

Case Facts

Conclusion

Short-Term MemoryCase/Inferred Facts

Conclusion


34/54

The Structure of ES

Expert System problem solving

34

Knowledge BaseDomain Knowledge

Inference Engine

User

Case Facts

ConclusionWorking Memory

Case/Inferred Facts

Conclusion


35/54

The Structure of ES

THE KNOWLEDGE BASE (LONG TERM MEMORY)

The key bottleneck in developing an expert system.

Contain everything necessary for understanding, formulatingand solving a problem.

It contains facts and heuristics.

The most popular approach to representing domainknowledge is using production rules.

Rule 1

IF car won't startTHEN problem in electrical system

Rule 2IF problem in electrical system

AND battery voltage is below 10 volts

THEN bad battery

35


36/54

The Structure of ES

WORKING MEMORY (SHORT TERM MEMORY)

Contains facts about a problem that are discovered duringconsultation with the expert system.

System matches this information with knowledge contained inthe knowledge base to infer new facts.

The conclusion reach will enter the working memory.

Thus, working memory contain information either supplied byuser or infer by the system.

36


37/54

The Structure of ES

INFERENCE ENGINE

Knowledge processor which is modeled after the expertreasoning power.

Processor in an expert system that matches the factscontained in the working memory with the domainknowledge contained in the knowledge base, to drawconclusion about the problems.

It taps the knowledge base and working memory to derivenew information and solve problems

37


38/54

The Structure of ES

THE USER INTERFACE

The user communicates with the expert system through theuser interface.

It allows the user to query the system, supply information andreceive advice.

The aims are to provide the same form of communication

facilities provided by the expert.

But normally has less capability of understanding naturallanguage and general knowledge.

38


39/54

The Structure of ES

THE EXPLANATION FACILITY

A trademark of expert systems: ability to explain their

reasoning.

An additional component of expert system. ES can provide explanation on:

WHY it is asking the question

HOW it reached some conclusion.

39


40/54

The Structure of ES

Explaining WHY Example:

MM : Will the car not start?

Person : WHY

MM : If I know that the car won't start then I usually

assume the problem is the electrical system.

Expert responds with what they might conclude from the

answer.

ES respond to a WHY query by displaying the rule it is

currently pursuing.

40


41/54

The Structure of ES

Explaining HOW

Besides giving final results, expert system can explain how itarrived at a result.

Example:

MM : The battery is bad

Person : HOW

MM : Since your car won't start, I assumed there was

a problem with the electrical system. I found the

battery voltage was below 10, I knew the battery

was bad. ES respond by tracing back through the rules that fire the

conclusion.

This tracing is a map of the system line of reasoning.

41



42/54



INFERENCE ENGINES IN EXPERT SYSTEMS

An inference engine works by searching through the rules and firing those rules that are triggered by facts gathered and entered

by the user. Basically, a collection of rules is similar to a series of nested IF statements in a traditional software program; however,

the magnitude of the statements and degree of nesting are much greater in an expert system.




43/54


Successful expert systems Con-Way Transportation built expert system to automate

and optimize planning of overnight shipment routes fornationwide freight-trucking business

Most expert systems deal with problems ofclassification Have relatively few alternative outcomes

Possible outcomes are known in advance

Many expert systems require large, lengthy, andexpensive development and maintenance efforts Hiring or training more experts may be less expensive





44/54


Case-based reasoning (CBR)

Descriptions of past experiences of human specialists (cases),stored in knowledge base

System searches for cases with problem characteristics similarto new one, finds closest fit, and applies solutions of old caseto new case

Successful and unsuccessful applications are grouped with case

Stores organizational intelligence: Knowledge base iscontinuously expanded and refined by users

CBR found in

Medical diagnostic systems

Customer support





45/54



HOW CASE-BASED

REASONING WORKS

Case-based reasoning represents

knowledge as a database of past

cases and their solutions. The

system uses a six-step process to

generate solutions to new problemsencountered by the user.

FIGURE 11-8




46/54


Fuzzy logic systems

Rule-based technology that represents imprecision usedin linguistic categories (e.g., cold, cool) that representrange of values

Describe a particular phenomenon or processlinguistically and then represent that description in asmall number of flexible rules

Provides solutions to problems requiring expertise that isdifficult to represent with IF-THEN rules

Autofocus in cameras

Detecting possible medical fraud

Sendais subway system acceleration controls





47/54

g y


FUZZY LOGIC FOR TEMPERATURE CONTROL

The membership functions for the input called temperature are in the logic of the thermostat to control the

room temperature. Membership functions help translate linguistic expressions such as warm into numbers

that the computer can manipulate.

FIGURE 11-9




48/54

g y

Neural networks Find patterns and relationships in massive amounts of data too

complicated for humans to analyze

Learn patterns by searching for relationships, building

models, and correcting over and over again

Humans train network by feeding it data inputs for whichoutputs are known, to help neural network learn solution byexample

Used in medicine, science, and business for problems inpattern classification, prediction, financial analysis, and controland optimization

Machine learning: Related AI technology allowing computers tolearn by extracting information using computation and statisticalmethods





49/54

g y


HOW A NEURAL NETWORK WORKS

A neural network uses rules it learns from patterns in data to construct a hidden layer of logic. The hidden

layer then processes inputs, classifying them based on the experience of the model. In this example, the

neural network has been trained to distinguish between valid and fraudulent credit card purchases




50/54

g y

Genetic algorithms

Useful for finding optimal solution for specific problem byexamining very large number of possible solutions for

that problem Conceptually based on process of evolution

Search among solution variables by changing andreorganizing component parts using processes such asinheritance, mutation, and selection

Used in optimization problems (minimization of costs,efficient scheduling, optimal jet engine design) in whichhundreds or thousands of variables exist

Able to evaluate many solution alternatives quickly





51/54

g y


THE COMPONENTS OF A GENETIC ALGORITHM

This example illustrates an initial population of chromosomes, each representing a different solution. The

genetic algorithm uses an iterative process to refine the initial solutions so that the better ones, those with

the higher fitness, are more likely to emerge as the best solution.

FIGURE 11-11




52/54

g y

Hybrid AI systems

Genetic algorithms, fuzzy logic, neural

networks, and expert systems integratedinto single application to take advantageof best features of each

E.g., Matsushita neurofuzzy washingmachine that combines fuzzy logic withneural networks





53/54

g y

Intelligent agents

Work in background to carry out specific, repetitive,and predictable tasks for user, process, or application

Use limited built-in or learned knowledge base toaccomplish tasks or make decisions on users behalf

Deleting junk e-mail

Finding cheapest airfare

Agent-based modeling applications:

Systems of autonomous agents

Model behavior of consumers, stock markets, andsupply chains; used to predict spread of epidemics





54/54

g


INTELLIGENT AGENTS IN P&GS SUPPLY CHAIN NETWORK

Intelligent agents are helping P&G shorten the replenishment cycles for products such as a box of TideFIGURE 11 12