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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 conversion

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.

Class Exercise 2

• Consider the problem of programme

interruption for the construction process,

use expert systems rules to develop IF-

THEN-ELSE statements for its solution