Transcript Slide 1

UPRAVLJANJE ZNANJEM
[KNOWLEDGE MANAGEMENT]
Dr. sc. Mirko Maleković
redoviti profesor
[email protected]
[email protected]
SADRŽAJ:
• UVODNI PROBLEMI
• KARAKTERIZACIJA ZNANJA
• PRINCIPI UPRAVLJANJA ZNANJEM
• IT i KM
• SEMANTIČKO MODELIRANJE
• REZONIRANJE O ZNANJU
LITERATURA
[FGS2004]
I. Fernandez, A. Gonzales, R. Sabherwal.
Knowledge Management: Challenges, Solutions, and Technologies
Prentice Hall, 2004.
[SK2000] T. K. Srikantaiah and M. Koening. Knowledge
Management. Information Today, Inc., 2000.
[Mal2001] Y. Malhotra. Knowledge Management and
Business Model Innovation. Idea Group Publishing, 2001.
[Mal2000] Y. Malhotra. Knowledge Management and
Virtual Organizations. Idea Group Publishing, 2000.
TEME ZA PREZENTACIJU
T1. A Way to KM solutions. Microsoft
T2. From Information Management to Knowledge Management.
[SK2000], Chapter 4
T3. Ethics for Knowledge Management. [SK2000], Chapter 8
T4. Organizing to Know and to Learn. [SK2000], Chapter 9
T5. KM and Building the Learning Organization.
[SK2000], Chapter 10
T6. Knowledge Markets. [SK2000], Chapter 11
T7. Tacit Knowledge and Quality Assurance.[SK2000], Chapter12
T8. Components of a Knowledge Strategy. [SK2000], Chapter 21
T9. KM and New Organization Forms. [Mal2000], Chapter I
T10. Interorganizational KM. [Mal2000], Chapter IV
T11. Using Patterns to Capture Tacit Knowledge and Enhance
Knowledge Transfer in Virtual Teams.
[Mal2000], ChapterVII
T12. Managing Knowledge for Strategic Advantages in the
Virtual Organization. [Mal2000], ChapterVIII
T13. KM Assessment of an Organization. [FGS2004],
Chapter6
T14. Technologies to Manage Knowledge: Atrificial
Intelligence. [FGS2004], Chapter7
T15. Preserving and Applying Human Expertise: KnowledgeBased Systems. [FGS2004], Chapter8
T16. Using Past History Explicitly as Knowledge: Case-Based
Systems. [FGS2004], Chapter9
T17. Knowledge Elicitation: Converting Tacit Knowledge to
Explicit Knowledge. [FGS2004], Chapter10
T18. Knowledge Discovery Systems. [FGS2004], Chapter13
T19. Knowledge Capture Systems. [FGS2004], Chapter14
T20. Knowledge Sharing Systems. [FGS2004], Chapter115
T21. Knowledge Application Systems. [FGS2004], Chapter16
Problem1
Osoba A nudi osobi B nagrade n1 i n2 uz sljedeći uvjet
U: Osoba B treba reći izjavu F. Ako F vrijedi, onda B
dobiva n1 ili n2; Ako F ne vrijedi, onda B ne dobiva niti
jednu nagradu.
Koju izjavu F treba reći B da bi dobio n1?
n1= 1000 Kn
n2 = 5 Kn
Rješenje: Uvodimo propozicije
N1: Dobit ću n1;
N2: Dobit ću n2.
F = N2 , tj. neću dobiti n2.
Problem2
Svaki stanovnik države X je sretan (uvijek govori istinu) ili
nesretan (uvijek govori neistinu), ali ne oboje.
Psiholog PS testira bračni par (B1, B2) tako da pita osobu
B1 o stanju para.
Osoba B1 je odgovorila:
Ako sam ja sretna osoba, onda je B2 sretna osoba.
Ps je na osnovi odgovora osobe B1 točno zaključio o stanju
osoba B1 i B2
Objasnite rješenje rješenje psihologa PS.
Problem3
Imamo dvije ponude
Ponuda A:
Reći P;
Ako P = 1, onda dobivate 100Kn
Ako P = 0, onda dobivate S Kn, S  100
Ponuda B:
Reći P;
Bez obzira na istinitost od P, dobivate S Kn, S 100
Koja je ponuda bolja?
Rješenje:
Ponuda A osigurava proizvoljan dobitak od
Zato je A bolja ponuda
P = Neću dobiti 100 Kn i neću dobiti D Kn.
Objasnite rješenje
D Kn.
Problem4
Osoba A daje osobi B dvije kuverte K1 i K2:
K1 = 1000 Kn K2 = 10 Kn
Zatim osoba precizira uvjete:
U: U1: Ako osoba A kaže neistinu, onda osoba B vraća K1 i
zadržava K2
U2: Ako A kaže istinu, onda B zadržava obe kuverte
Pitanje:
Da li su uvjeti igre prihvatljivi za osobu B?
Rješenje:
Uvjeti su vrlo loši za osobu B.
Osoba A kaže rečenicu R: Vratit ćeš mi jednu od kuverti ili
dat ćeš mi 100 milijuna Kn
Objašnjenje: R = 0 vodi na kontradikciju. Dakle, R = 1. Zato,
osoba B mora dati osobi A 100 milijuna Kn.
Fuzzy Logic in Our Emotional World
Concern for mother
0.7
The parks project
0.3
Stay with the ‘old school’
boss
Fuzzy
average
0.2
Rule: If fuzzy average  0.6, then leave (resign)
0.4
K. Popper
Possible worlds
Physical world
C1
C2
C0
Emotional world
Conceptual world
C3
KNOWLEDGE
Knowledge is experience. Everything else is just
information.
...Albert Einstein
Knowledge becomes wisdom only after it has been put to
practical use.
...Mark Twain
ARGUMENT
(Agents A and B)
A: That is the happiness of my dog .
B: You are not a dog yourself, hos can you know
the happiness of the dog ?
A: And you not being I, how can you know that I
do not know ?
Suspectable knowledge
The animal knows, of course. But it certainly does
not know that it knows.
A wise Man
He knows and he knows that he knows
Knowledge and Action
You act, and you know why you act, but you don’t
know
why you know that you know what you do.
"Knowledge exists to be imparted."
...Ralph Waldo Emerson
Personal ROK (Return on
Knowledge)
One chalk mark .. ..... ..... $1
Knowing where to put it ..... $49,999
Knowledge and Information Requirements
Deterministic at the bottom
Highly unsructured
information (knowledge)
environment
Fuzzy (possibilistic) at the top
Fuzzy engineering
Top
Management
DW
Managers & Professionals
Highly structured
information
knowledge
environment
Clerks
DB
Macroscopic Knowledge
Microscopic Knowledge
What is this?
Large-Scale and
Complex Systems
You can’t see anything
without a macroscope
What is this?
Microscopic knowledge
(a) Focused on one domain in which there is little or no
contradiction
(b) Often considered to be ‘obvious’ to persons with
expirience in a given domain
(c) Crisp
Attention to analitical detail is the domain of microscopic
knowledge under the condition of appropriate skill
availability
Macroscopic knowledge
1. Fuzzy and so are the models which it uses
2. Fuzziness characterizes the macroscopic knowledge itself,
its goals and constraints
3. Essentially philosophical and interdisciplinary:
It is qualitative and logical, often suggestive, allowing for
contradiction in concepts and referencies, even vagueness
4. Flexible and adaptive to the changing environment and
its evolution rules
Metarules for assuring that at a higher level of referencce
our base of knowledge is indeed consistent
KNOWLEDGE MANAGEMENT
[ KM ]
Knowledge and Innovation
There is a cycle to the creation and use of knowledge.
Current knowledge is explicitly represented. It is processed
by human brains (together with information about the
specific issue at hand) leading to understanding, decisions
and action.
This is sometimes accompanied by new ideas or hypotheses. If
supported, the new ideas lead to innovative actions, and also
to new knowledge that is added to the explicit representation
of current knowledge.
KM is a discipline that promotes
an integrated approach to identifying,
capturing, evaluating, retrieving, and sharing
all of an enterprise’s information assets.
These assets may include databases, document, policies and
procedures, and previously uncaptured tacit expertise and
experience in individual workers.
Knowledge Management encompasses
management strategies, methods, and
technology for
leveraging intellectual capital and knowhow to achieve gains in human
performance and competitiveness.
KM MODEL
Externally and Internally
available Knowledge
People (internal staff and
outside experts) Knowledge
Explicit Knowledge
Tacit Knowledge
Knowledge Infrastructure
•Commercial
Publications
•Email
•Web
•Intranet
•Databases
•Best Practicies
•Self Study
Material
•Other
•IT
•Customer
Knowledge
•Top
Management
Support
•Social capital
(culture,trust,
knowledge
behavior)
•Apprenticeships
•Mentoring
•Training
•Study tours
•Measurement
•Individual
Knowledge
•Face-to-Face
Conversation
•Other
EVOLUTION of KM
SYSTEMS
•Database
Management Systems
•Management
+ Explicit, Tacit + Cultural =
Information systems
Knowledge
Knowledge
•Decision Support Systems
•Total Quality Management
•Data Warehousing
•Data Mining
•Electronic
Data Interchange
•Selective Dissemination
Information
•Information
Resource Management
•..
KM
PRACTICING KNOWLEDGE MANAGEMENT
Turning Experience & Information into Results
Knowledge
ELEMENTS of KM - SOLUTIONS
PROCESS: Ensuring that KM is aligned with specific
bisiness processes
ORGANIZATIONAL DYNAMICS: Overcoming barriers
to sharing knowledge and fostering a spirit of innovation
TECHNOLOGY: Enabling people’s knowledge-sharing
activities within familiar tools
BUSINESS STRATEGY
ORGANIZATION
PROCESS
TECHNOLOGY
PROCESS TARGETS for KM
A: Product and Service Design and Development
B: Customer and Issue Management
C: Business Planning
D: Employee Management and Development
ORGANIZATIONAL DYNAMICS
Knowledge Sharing
Fear of Inovation
Implicit Strategies
Explicit Strategies
Does KM = IT ?
CKO M. J. Turillo: KM cannot be done without IT
Maybe not! ... But
Yogesh Malhotra ( www.brint.com ) identiyied some myths
regarding the murky confluence of IT and KM
Myth1: KM technologies deliver the right information to the
right person at the right time
Outdated business model
Businesses will change incrementally in an inherently stable
market, and executive can forsee change by examining the past.
You can predict.. how and what you’ll need to do and IT (IS) can
simplify this and do it efficiently
•The new business model of the Information (Knowledge) Age
is marked by fundamental, not incremental, change.
•Business can’t plan long-term
•They must shift to a more flexible ‘anticipation-of-suprise’
model
Myth2: ITs can store human intelligence and experience
•IT can’t store rich schemas that people posses for making
sense of data
• IT can’t store human experience (unless you can scan a
person’s mind
Semantic Web
Intelligent Systems
Myth3: ITs can distribute human intelligence
• Assumed is that companies can predict the right information
(knowledge) to distribute and the right people to distribute it to
• Most of our KM technologies concentrates on efficiency and
creating a consensus-oriented view
• Such systems do not account for renewal of existing knowledge
and creation of new knowledge
Barriers to KM Implementation
Immaturity of Technology
19%
Immaturity of Industry
16%
Cost
12%
Lack of Need
5%
Cultural Resistance
48%