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Knowledge Management
ابراهيم مطاوع./ د.أ
E-mail: [email protected]
قسم االنشاءات
كلية الهندسة
جامعة المنصورة
Cairo University – 6th December 2016
Learning Outcomes
• Recognise the meaning, nature and
importance of knowledge management
in construction organisations
• Examine the role of management
information systems (MIS) in the
management of knowledge
Term Description
Data Values obtained from any type of sensors, including
humans. Geographical analogy: heights, distances, etc.
Information Synthesised from data, to produce a map of the
territory (information is the potential for knowledge)
Knowledge What the map means, how it can be used for different
purposes (importance of ‘purpose’), when it should not be used,
when it needs change, if it is insufficient, or when it needs link
with other information.
What is Knowledge?
Properties of Knowledge
• Includes information and data, but these, on their own, may not
include knowledge
• Should have ‘purpose’ (e.g. adding ‘value’ through its application)
• It is both time and context sensitive
• Formal aspects of this knowledge (not tacit aspects) can be taught
• Tacit aspects tend to reside in human heads and can be difficult to
capture or formalise
• It can be costly to generate/replace, either tacit or explicit
Types of Knowledge
• Explicit (Formal) & Tacit Knowledge
• Knowledge for business relevance and
functional role within an organisation
Explicit (Formal) Knowledge
• Easier to identify
• Reusable in a consistent and repeatable
manner
• May be stored as: written procedure,
process in computer system, etc.
Tacit Knowledge (Expertise)
• What is held in human heads (understanding,
implied, existing without being stated)
• Difficult to transfer
• The interface between formal knowledge and
its application to real problems – ‘knowledge
that oils the wheels of formal procedures’
Knowledge for Business Relevance
and Functional Roles
With respect to the role of knowledge within an organisation:
• Knowledge of people (the behaviour of
stakeholders/clients/suppliers, relationships and purposes)
• Business environment insights
• Knowledge embedded in processes, products, services, etc.
(Organisation memory)
Why manage knowledge?
• The importance of knowledge to
organisational success
– Emergence of information economy
– Importance for competitive advantage
– knowledge & core competencies (the few things an
organisation does best) are key organisational assets
What is Knowledge Management?
• Aim of KM is the recognition of the strategic value of its intellectual assets and the careful management and distribution of these assets across the enterprise to create value, increase productivity and gain and sustain competitive advantage
• A set of processes to capture, preserve and disseminate the knowledge of key individuals or groups in the organisation to assure the availability of that knowledge later when the individual has retired or the groups have disperse
• KM involves:
– Generation (creation of new knowledge)
– Capture of existing knowledge
– Storage (humans, database, tools)
– Accessibility (Registry and search mechanisms)
– Application
– Dissemination
– Retirement of knowledge
• In general KM seeks for:
– Explicit knowledge: consolidation and making available of artefacts
– Tacit knowledge: creation of communities to hold, share and grow tacit knowledge
IT infrastructure for KM
• Create Knowledge (Knowledge Work Systems)
CAD
Virtual Reality
• Distribute Knowledge (Office Automation Systems)
Desktop Publishing
Imaging
Electronic calendars
Desktop Databases
• Share Knowledge (Group Collaboration Systems)
Groupware
Intranets
• Capture & Codify Knowledge (Artificial Intelligence Systems)
Expert Systems
Neural Nets
Fuzzy Logic
Genetic Algorithms
Intelligent Agents
Knowledge Work Systems (KWS)
• An information system that aids knowledge workers in the creation and integration of new knowledge in the organisation
• They must give knowledge workers the specialised tools they need (e.g. powerful graphics, analytic tools, communications and document management tools – great computing power with quick and easy access to external databases)
• Computer-aided design (CAD)
– automate the creation and revisions of designs using computers
and sophisticated graphics software
• Virtual Reality Systems
– have visualisation, rendering and simulation capabilities that
surpass CAD systems
– use of interactive graphics to create computer-generated
simulations
Knowledge Distribution
• Connecting the work of the local knowledge workers with all levels and functions of the whole organisation and the external world , including customers, suppliers, government regulators, and external auditors
• Management of documents (creation, storage, retrieval and
dissemination)
• Communicating including initiating, receiving, and managing voice. Digital and document-based communication for both individuals and groups.
Office Automation Systems (OAS)
• Any application of information technology that intends to increase productivity of knowledge workers in the office
– e.g. word processing, voice mail, video conferencing
– digital image processing (imaging systems) at the core of current OAS
– group assistance tools (e.g. networked digital calendars) to assist group work among office workers
Knowledge Sharing
• Necessary to facilitate group work: co-
ordination and collaboration
• Tools include: email, teleconferencing, data
conferencing, groupware, Internet, etc.
• Note:
– group collaboration technologies alone cannot
promote sharing if team members do not want to
share knowledge
Knowledge Capture and Storage
• Use of artificial intelligence (AI) to: – capture individual and collective knowledge
– codify and extend organisational knowledge base
• AI systems: – are efforts to develop computer-based systems that behave as humans
– preserve expertise that might be lost through the absence of an expert
– store knowledge in a active form that can be accessed by others
– create mechanism not subjected to human feelings
– eliminate routine and unsatisfying jobs held by people
– Successful AI systems are based on human expertise, knowledge, and selected reasoning patterns but they do not exhibit the intelligence of human beings
– They do not invent new and novel solutions to problems
• AI applications include: – Robotics, Expert systems, intelligent machines, etc.
Expert Systems
• Knowledge intensive systems that capture human
expertise in limited domains of knowledge
• Assist in decision making by asking relevant
questions and providing explanation for action taken
• Can help organisations make higher level decisions
with fewer people
• Quite narrow, shallow and brittle
How Expert Systems work
Rules
Human Knowledge
Knowledge Frames
Income Salary
Consultancy
Savings interest
Expenses Housing
Tax
Holidays
Budget Income
Expenses
If-Then-Else If condition is true, then
an action is taken, else
another action is taken
Simple example
to select the best
contractor
A
If turnover > 500,000
Ask about “Years in Business” Else EXIT
B
If “Years in Business” > 10 years,
Ask about “No. of employees”
Else EXIT
C
If “No. of employees” is between 75 and
100, Grant one project Else EXIT
D
For on going projects
Ask about performance
E
If missing project deadline > 5% of project
duration, Do F
(grant another one project) Else Do G
F
Only one more
project
G
If missing project budget < 3%
Ask about other debt Else EXIT
H
If other debt >5% of turnover,
Do F Else Do I I
Agree a long
term
partnership
Development of Expert Systems
• Developed within a ‘shell’ (a programming
environment) e.g. Kappa PC, Programming
Language “Prolog”, etc
• The main contributors:
– Expert: have thorough command over knowledge base
– Knowledge Engineer:
• Special expert in eliciting information/knowledge from other
professionals
• Translates information/knowledge into set of rules and/or frames for an expert system
The process involves:
• Selecting/ensuring that the problem is appropriate for an expert system
• Determine balance between potential savings from system against the cost of developing it
• Develop prototype system to test assumptions about how to encode the knowledge of experts
• Develop a full-scale system, with specific focus on the addition of a very large number of rules
• Check the comprehensibility of system and prune, if necessary to achieve simplicity and power
• Test system by a range of actual experts within the organisation against any performance established earlier (e.g. output of the system should agree with that of experts for 90% of the time, etc.)
• After successful testing, integrate system into data flow and work patterns of the organisation
Development of Expert Systems
Problems with Expert Systems
• Lacks the robust and general intelligence of humans
• Suitable for only certain classes of problems and
represent limited forms of knowledge
• Development efforts can be very long
• Knowledge base is fragile and brittle - cannot learn to
change over time
• Based on prior knowledge of a few known alternatives
Case-Based Reasoning
• Unlike expert systems which work by applying a set of IF-THEN-ELSE
rules, CBR represents knowledge as a series of cases and this
knowledge base is continuously expanded and refined by users
• Represents knowledge as a database of cases for later retrieval when
a similar case is encountered
• System searches for stored cases similar to the new one, finds the
closest fit, and applies the solutions of the old case to the new case
• Successful solutions are tagged to the new case and both are stored
together
• Unsuccessful solutions are also appended to case database with
explanations on why they did not work
How Case-Based Reasoning works
Successful?
User describes the problem
System searches database for
similar cases
System asks user additional
questions to narrow the search
System finds closest fit and
retrieves solution
System modifies the solution
to better fit the problem
Case
database
System stores problem
and successful solution in
the database
1
2
3
4
5 6
NO YES
Successful?
Artificial Neural Networks (ANN)
• Attempt to emulate the processing patterns of the brain - learning by example
• Consist of an input, output and a hidden processing layer
• It learns from the input/output patterns of data to construct a hidden layer of logic as follows:
– network is fed training data for which inputs produce known outputs. This helps the computer to learn the correct solution by example
– as more data is fed, each case is compared with known outcome: if it differs, correction is calculated and applied to nodes in hidden processing layer
– Steps are repeated until a satisfactory condition, such as corrections being less than a certain amount, is reached
Neural Network structure –
an example
Turnover
Debt
Years in
Business
Performance
Grant one
project
Agree
partnership
Input Layer Hidden Layer Output Layer
Neural Network
Problems of ANN
• Cannot always explain their outputs
• Cannot guarantee certainty
• Is sensitive to the training data (Too little or
too much data)
Class Activity 1
• Given a work programme of a construction
project that shows when each task starts and
finishes. It also shows the resources required.
There are potential interruptions to the
programme (e.g. delay, resource shortage,
unforeseen events, etc.). Give an example of
solution that a project manager can consider.
Your example should show relevance to the
tacit knowledge of this manager.