Decision Support System

357
5/12/2010 Data & Information The Business School University of Kashmir

Transcript of Decision Support System

Page 1: Decision Support System

5/12/2010

Data & Information

The Business School

University of Kashmir

Page 2: Decision Support System

25/12/2010 10:21:02 PM Rafi A Khan

Definitions

Fact – statement of some element of truth about a subject matter or a domain.

Example: milk is white, sun rises in east

Intelligence – capacity to acquire, store, improve and apply knowledge

Experience – what we have done and what has happened in past in a specific

area of work

Common sense – natural ability to sense, judge or perceive situations ; grows

stronger over time

Memory – ability to store and retrieve relevant experience at will, is part of

intelligence

Learning – is knowledge or skill that is acquired by instruction or study

Page 3: Decision Support System

3

Definitions

Knowledge. Information once analyzed, understood, and explained is

knowledge or foreknowledge (predictions or forecasts).

Rafi A Khan

Page 4: Decision Support System

4Rafi A Khan

Data, Information and Systems

Data vs. Information

– Data

• A ―given,‖ or fact; a number, a statement, or a picture

• Represents something in the real world

• The raw materials in the production of information

– Information

• Data that have meaning within a context

• Data in relationships

• Data after manipulation

Page 5: Decision Support System

5Rafi A Khan

Data, Information and Systems

Data Manipulation

– Example: customer survey

• Reading through data collected from a customer survey with

questions in various categories would be time-consuming and

not very helpful.

• When manipulated, the surveys may provide useful

information.

Page 6: Decision Support System

6Rafi A Khan

Why Information Systems

Page 7: Decision Support System

7Rafi A Khan

What Is an Information System?

Page 8: Decision Support System

8Rafi A Khan

Systems

Generating Information

– Computer-based ISs take data as raw material, process it,

and produce information as output.

Figure 1.1 Input-process-output

Page 9: Decision Support System

9Rafi A KhanFigure 1.2 Characteristics of useful information

Characteristics of Information

Page 10: Decision Support System

10Rafi A Khan

INPUT OUTPUTPROCESS

FEEDBACK

Activities in an Information System

Why Information Systems

Page 11: Decision Support System

11Rafi A Khan

Why Information Systems

Page 12: Decision Support System

12Rafi A Khan

Information Needs of a Shopkeeper

Daily sales account

List of low stock items to be re-ordered

List of overstock items

Long overdue payments

Profit and loss account

Used to streamline day to day operations called Operational

information

Page 13: Decision Support System

13Rafi A Khan

Slow or fast moving items

Reliable supplier of items

Sales trends

Used to improve profitability of shop called Tactical information

Information Needs of a Shopkeeper

Page 14: Decision Support System

14Rafi A Khan

Whether to stock different varieties of items

Whether to diversify

Whether to start a new branch in a different locality

Whether to start an e-shop

Information to expand business and explore new opportunities

Known as Strategic Information

Information Needs of a Shopkeeper

Page 15: Decision Support System

15Rafi A Khan

Types of Information

Strategic : Needed for long range planning and directions.

• This is less/un- structured.

Tactical : Needed to take short range decisions to improve

• Profitability and Performance.

Operational : Needed for day to day operations of the

organization.

• Eg: Daily Sales, Billing.

Page 16: Decision Support System

16Rafi A Khan

SYSTEM

System as a group of interrelated components working together toward a

common goal by accepting inputs and producing outputs in an organized

transformation process.

Such a system has three basic interacting components or functions:

Input: Involves capturing and assembling elements that enter the system to be

processed. For example, raw materials, energy, data, and human effort must be

secured and organized for processing.

Page 17: Decision Support System

17Rafi A Khan

SYSTEM

Processing: Involves transformation process that converts input into output.

Examples to these are manufacturing process, the human breathing process,

etc.

Output: Involves transferring elements that have been produced by

transformation process to their ultimate destination, Examples to these are

finished products, human services and management information that must be

transmitted to their human users.

Page 18: Decision Support System

18Rafi A Khan

System

Input

Feedback and Control

OutputProcess

System

Environment

Fig. Showing Elements of a System

Page 19: Decision Support System

19Rafi A Khan

System

A system with feedback and control components is sometimes called a

cybernetic system, that is, a self-monitoring, self-regulating system.

Feedback: It is data about the performance of a system. It is actually

measured in terms of the outcome to that of the predefined objectives

set out at the beginning of the process.

Control: It involves monitoring and evaluating feedback to determine

whether a system is moving toward the achievement of its goal.

Page 20: Decision Support System

20Rafi A Khan

System Characteristics

A system does not exist in a Vacuum; rather, it exists and function in an

environment containing other systems

If a system is one of the components of a larger system, it is then

referred to as a subsystem, and the larger system is its environment.

The system that has the ability to change itself or its environment in

order to survive is an adaptive system

Page 21: Decision Support System

21Rafi A Khan

Types of System

A large system can be split or decomposed into smaller subsystems

up to a certain level

The decomposition of a system into subsystems can be in a serial

form or it could be in a matrix form

In a serial system processing, the entire output of a subsystem is the

input to the next sub­system and so on.

In the matrix arrangement the different outputs go to different sub-

systems. A subsystem receives more than one input from other

subsystems.

Page 22: Decision Support System

22Rafi A Khan

Types of System

If the process of input transformation is not visible and

understandable then we say that the system is a black box and

the process is not transparent

Most of the systems can be viewed in a hierarchical structure.

Breaking the system in a hierarchical manner provides a way to

structured systems analysis. It gives a clear understanding of the

contribution of each subsystem in terms of data flow and

decisions, and its interface to the other subsystems.

Page 23: Decision Support System

23Rafi A Khan

Types of System

The systems can be classified in different categories based on the

predictability of its output and the degree of information exchange

with the environment.

Deterministic- when the inputs, the process and the outputs of a

system are known with certainty. In a deterministic system, you can

predict the output with certainty.

Probabilistic- when the output can only be predicted in

probabilistic terms. The accounting system is deterministic while

the demand forecasting system is a probabilistic one.

Page 24: Decision Support System

24Rafi A Khan

Types of System

If a system is functioning in isolation from the environment, then the

system does not have any exchange with the environment nor is it

influenced by the environmental changes. Such a system is called a

closed system.

If the system has exchange with the environment and is influenced by

the environment then it is called an open system.

All kinds of accounting systems, viz., cash, stocks, attendance of

employees are closed systems. Most of the systems based on rules

and principles are closed systems.

The systems which are required to respond to changes in the

environment, such as marketing, communication and forecasting are

open systems

Page 25: Decision Support System

25Rafi A Khan

Types of System

Specify in the inputs, processes, and outputs of the following

systems. Determine what is required for each system to be

efficient and effective.

Post Office

Elementary school

Grocery store

Farm

Page 26: Decision Support System

26Rafi A Khan

Types of System

Organization Inputs Processes Outputs

Post Office Letters mailed Delivery of mail Mail delivered

School Students Teaching Graduating

students

Grocery StoreFood products Stocking, selling

Food sold to

customers

Farm Feedstock, seeds,

fertilizer

Animals and

plants

growing

Food delivered to

market

Page 27: Decision Support System

27Rafi A Khan

System

List possible kinds of feedback for the systems in the previous question.

Post Office: Customers' complaints, average days for a delivery, cost,

percent of lost mail

School: Students' complaints, achievement on national tests, success

in job placement

Grocery store: Customer feedback on quality, quantity, percent of

theft and waste, etc.

Farm: Quality of output sold to market

Page 28: Decision Support System

28Rafi A Khan

•Information system consists of physical and non-physical components

working together

•A computer combines with a software program may constitute an

information system, but only if the program is designed to produce

information that helps an organization or person to achieve a specific

goal.

Information Systems

Page 29: Decision Support System

29Rafi A Khan

Management Information System (MIS) Computer-based or manual system

- transforms data into information to support the decision making.

MIS can be classified as performing three functions:

(1) To generate reports - for example, financial statements, inventory status reports, or performance reports needed for routine or non-routine purposes.

(2) To answer what-if questions asked by management. For example, questions such as "What would happen to deposits if the bank increases interest rates?" can be answered by MIS.

(3) To support decision making. This type of MIS is appropriately called Decision Support System (DSS).

-DSS attempts to integrate the decision maker, the data base, and the quantitative models being used.

Information Systems

Page 30: Decision Support System

30Rafi A Khan

Information Systems?

Page 31: Decision Support System

31Rafi A Khan

Sales and marketing

Manufacturing

Finance

Accounting

Human resources

Major Business Functions

WHY INFORMATION SYSTEMS?

Page 32: Decision Support System

32Rafi A Khan

Marketing Management Information Systems:

It supports managerial activity in the area of product

development, distribution, pricing decisions, promotional

effectiveness, and sales forecasting.

It mainly relies on external sources of data like competitors

and customers.

MIS in Marketing

Page 33: Decision Support System

33Rafi A Khan

Major functions of systems:

Sales management, market research, promotion, pricing, new

products

Major application systems:

Sales order info system, market research system, pricing

system

Sales and Marketing Systems

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 34: Decision Support System

34Rafi A Khan

Sales and Marketing Systems

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 35: Decision Support System

35Rafi A Khan

Manufacturing Management Information Systems:

Inventories are provided just in time to reduce costs of

warehousing huge inventories .

MIS in Manufacturing

Page 36: Decision Support System

36Rafi A Khan

Major functions of systems:

Scheduling, purchasing, shipping, receiving, engineering, operations

Major application systems:

Materials resource planning systems, purchase order control systems, engineering systems, quality control systems

Manufacturing and Production Systems

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 37: Decision Support System

37Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 38: Decision Support System

38Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 39: Decision Support System

39Rafi A Khan

Financial Management Information Systems:

It provides financial information to all financial managers

within an organization including the chief financial officer.

The chief financial officer analyzes historical and current

financial activity, future financial needs, and monitors and

controls the use of funds over time using the MIS

MIS in Finance

Page 40: Decision Support System

40Rafi A Khan

Major functions of systems:

Budgeting, general ledger, billing, cost accounting

Major application systems:

General ledger, accounts receivable, accounts payable,

budgeting, funds management systems

Financing and Accounting Systems

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 41: Decision Support System

41Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 42: Decision Support System

42Rafi A Khan

Human Resources Management Information Systems:

These systems are concerned with activities related to

workers, managers, and other individuals employed by the

organization.

It includes, work-force analysis and planning, hiring,

training, and job assignments.

MIS in HR

Page 43: Decision Support System

43Rafi A Khan

Major functions of systems:

Personnel records, benefits, compensation, labor relations,

training

Major application systems:

Payroll, employee records, benefit systems, career path

systems, personnel training systems

Human Resource Systems

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 44: Decision Support System

44Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 45: Decision Support System

45Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 46: Decision Support System

46Rafi A Khan

People: Managers, knowledge workers, data workers,

production or service workers

Structure: Organization chart, products, geography

Operating procedures: Standard operating procedures (SOP,

rules for action)

Key Elements of An Organization

Page 47: Decision Support System

47Rafi A Khan

Hardware: Physical equipment

Software: Detailed preprogrammed instructions

Storage: Physical media for storing data and the software

Communications Technology: transfers data from one

physical location to another

Networks: link computers to share data or resources

IT/Tools for Managers

Page 48: Decision Support System

48Rafi A Khan

IS & Organizations

Page 49: Decision Support System

49Rafi A Khan

TOWARD THE DIGITAL FIRM

Page 50: Decision Support System

50Rafi A Khan

Page 51: Decision Support System

51Rafi A Khan

Information Sytems

Page 52: Decision Support System

52Rafi A Khan

Major Types of Systems

• Executive Support Systems (ESS)

• Decision Support Systems (DSS)

• Management Information Systems (MIS)

• Knowledge Work Systems (KWS)

• Office Systems

• Transaction Processing Systems (TPS)

Page 53: Decision Support System

53Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 54: Decision Support System

54Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Transaction Processing Systems (TPS)

Operational Level :

• Basic business systems that serve the operational level

• A computerized system that performs and records the daily

routine transactions necessary to the conduct of the business

Page 55: Decision Support System

55Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 56: Decision Support System

56Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 57: Decision Support System

57Rafi A Khan

Knowledge Work Systems (KWS)

Knowledge level

Inputs : Design specs

Processing : Modeling

Outputs : Designs, graphics

Users : Technical staff and professionals

Example: Engineering work station

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 58: Decision Support System

58Rafi A Khan

Management Information System (MIS)

Management level

Inputs : High-volume data

Processing : Simple models

Outputs : Summary reports

Users : Middle managers

Example: Annual budgeting

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 59: Decision Support System

59Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 60: Decision Support System

60Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 61: Decision Support System

61Rafi A Khan

Decision Support System (DSS)

Management level

Inputs : Low/High volume data

Processing : Interactive

Outputs : Decision analysis

Users : Professionals, Staff

Example: Forecasting

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 62: Decision Support System

62Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 63: Decision Support System

63Rafi A Khan

Executive Support System (ESS)

Strategic level

Inputs : Aggregate data

Processing : Interactive

Outputs : Projections

Users : Senior managers

Example: 5-year operating plan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 64: Decision Support System

64Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 65: Decision Support System

65Rafi A Khan

MAJOR TYPES OF SYSTEMS IN ORGANIZATIONS

Page 66: Decision Support System

66Rafi A Khan

Business processes

Manner in which work is organized, coordinated, and focused to produce a valuable product or service

Concrete work flows of material, information, and knowledge—sets of activities

Business Processes and Information Systems

ENTERPRISE APPLICATIONS

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

Page 67: Decision Support System

67Rafi A Khan

Unique ways to coordinate work,

information, and knowledge

Ways in which management chooses

to coordinate work

Business Processes and Information Systems

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 68: Decision Support System

68Rafi A Khan

Business Processes and Information Systems

Information systems help organizations

Achieve great efficiencies by automating parts of processes

Rethink and streamline processes

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 69: Decision Support System

69Rafi A Khan

Manufacturing and production: Assembling product, checking

quality, producing bills of materials

Sales and marketing: Identifying customers, creating customer

awareness, selling

Examples of Business Processes

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 70: Decision Support System

70Rafi A Khan

Finance and accounting: Paying creditors, creating financial

statements, managing cash accounts

Human Resources: Hiring employees, evaluating performance,

enrolling employees in benefits plans

Examples of Business Processes

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 71: Decision Support System

71Rafi A Khan

Cross-Functional Business Processes

Transcend boundary between sales, marketing, manufacturing, and research and development

Group employees from different functional specialties to a complete piece of work

Example: Order Fulfillment Process

Business Processes and Information Systems

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 72: Decision Support System

72Rafi A Khan

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 73: Decision Support System

73Rafi A Khan

Enterprise Applications

Enterprise systems

Supply chain management systems

Customer relationship management systems

Knowledge management systems

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 74: Decision Support System

74Rafi A Khan

Within the business: There are functions, each having its uses of

information systems

Outside the organization‘s boundaries: There are customers and

vendors

Functions tend to work in isolation

Traditional View of the Systems

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 75: Decision Support System

75Rafi A Khan

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Figure 2-13

Page 76: Decision Support System

76Rafi A Khan

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 77: Decision Support System

77Rafi A Khan

Firm structure and organization: One organization

Management: Firm-wide knowledge-based management processes

Technology: Unified platform

Business: More efficient operations and customer-driven business processes

Benefits of Enterprise Systems

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 78: Decision Support System

78Rafi A Khan

Difficult to build: Require fundamental changes in the way the business operates

Technology: Require complex pieces of software and large investments of time, money, and expertise

Centralized organizational coordination and decision making: Not the best way for the firms to operate

Challenges of Enterprise Systems

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 79: Decision Support System

79Rafi A Khan

Supply Chain Management (SCM)

Close linkage and coordination of activities involved in buying, making, and moving a product

Integrates supplier, manufacturer, distributor, and customer logistics time

Reduces time, redundant effort, and inventory costs

Supply Chain Management (SCM)

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 80: Decision Support System

80Rafi A Khan

Supply Chain

Network of organizations and business processes

Helps in procurement of materials, transformation of raw materials

into intermediate and finished products

Supply Chain Management (SCM)

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 81: Decision Support System

81Rafi A Khan

Limitations:

Inefficiencies can waste as much as 25% of company‘s operating costs

Bullwhip Effect: Information about the demand for the product gets

distorted as it passes from one entity to next

Supply Chain Management (SCM)

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 82: Decision Support System

82Rafi A Khan

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 83: Decision Support System

83Rafi A Khan

Helps in distribution of the finished products to customers

Includes reverse logistics - returned items flow in the reverse direction

from the buyer back to the seller

Supply Chain Management (SCM)

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 84: Decision Support System

84Rafi A Khan

Decide when, what to produce, store, move

Rapidly communicate orders

Communicate orders, track order status

Check inventory availability, monitor levels

Track shipments

Plan production based on actual demand

Rapidly communicate product design change

Provide product specifications

Share information about defect rates, returns

How Information Systems Facilitate Supply Chain Management

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 85: Decision Support System

85Rafi A Khan

Supply chain planning system: Enables firm to generate forecasts for

a product and to develop sourcing and a manufacturing plan for the

product

Supply chain execution system: Manages flow of products through

distribution centers and warehouses

Supply Chain Management (SCM)

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 86: Decision Support System

86Rafi A Khan

Uses digital technologies to enable multiple organizations to

collaboratively design, develop, build, move, and manage products

Increases efficiencies in reducing product design life cycles, minimizing

excess inventory, forecasting demand, and keeping partners and

customers informed

Collaborative Commerce

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 87: Decision Support System

87Rafi A Khan

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 88: Decision Support System

88Rafi A Khan

Private Industrial Networks

Web-enabled networks

Link systems of multiple firms in an industry

Coordinate transorganizational business processes

Industrial Networks

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 89: Decision Support System

89Rafi A Khan

Customer Relationship Management (CRM)

Manages all ways used by firms to deal with existing and potential new customers

Business and Technology discipline

Uses information system to coordinate entire business processes of a firm

Customer Relationship Management (CRM)

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 90: Decision Support System

90Rafi A Khan

Provides end-to-end customer care

Provides a unified view of customer across the company

Consolidates customer data from multiple sources and provides

analytical tools for answering questions

Customer Relationship Management (CRM)

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 91: Decision Support System

91Rafi A Khan

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

ENTERPRISE APPLICATIONS

Page 92: Decision Support System

92Rafi A Khan

Knowledge Management Systems

Creating knowledge

Discovering and codifying knowledge

Sharing knowledge

Distributing knowledge

Management Information Systems 8/eChapter 2 Information Systems in the Enterprise

Page 93: Decision Support System

93Rafi A Khan

The End

Page 94: Decision Support System

5/12/2010 94

Management Information Systems

Page 95: Decision Support System

95Rafi A Khan

Feasibility Study

Three types of feasibility :

Technical Feasibility

Economical Feasibility

Operational Feasibility

Page 96: Decision Support System

96Rafi A Khan

Technical Feasibility

H/W - I/P, O/P, Communication, Storage

S/W - Database, OS, Languages

Application - System Packages, Management Science Models

Page 97: Decision Support System

97Rafi A Khan

Economical Feasibility

Costs - Sytems/Programmes, Operations, H/W, S/W

Savings - Operating Expenses, Clerical Personnel,

Equipment

Benefits - Tangible ---- Reduction in Production Cost

Intangible ---- Customer Satisfaction

Page 98: Decision Support System

98Rafi A Khan

Operational Feasibility

Management - Operating Management

Middle Management

Top Management

Page 99: Decision Support System

99

Reports of MIS

Periodic Scheduled Reports.

Exception Reports.

Demand Reports and Responses.

Push Reporting.

Page 100: Decision Support System

10

0

Information Technology and MIS

Information Technology is defined as that branch of computer science

that includes:

Hardware.

Software.

Communication Technology.

Storage systems and

Other Information processing technologies.

Page 101: Decision Support System

10

1

Computer Hardware-------The physical equipment

Communication

Devices

Secondary storage

•Magnetic disk

•Optical disk

•Magnetic tape

Primary StorageCentral

Processing Unit

Output Devices

•Printers

•VDT

•Plotters

•Audio output

Input Devices

•Keyboard

•Computer mouse

•Touch screen

•Source data automation

Buses

Page 102: Decision Support System

10

2

Computer Software

Computer

Software

Application

Software

System

Software

General purpose

Application

Programs

Application-Specific

Programs

System Management

Programs

System Development

Programs

Page 103: Decision Support System

10

3

Communication Technology

Communications technology allows systems to transfer data from one location to

another for the transmission of voice, data, images, sound and even video. It can

take the form of :-

• Wired transmission: The transmission media can be

• Twisted pair cable.

• Coaxial cable.

• Fiber-optic cable.

• Wireless transmission: This includes:-

• Microwave Transmission.

• Satellite Transmission.

Page 104: Decision Support System

10

4

Using Communication Technology for Business Solutions

The Internet is revolutionizing communications by providing a

worldwide network linking business, government, and scientific and

educational organizations to individuals. Internet use falls into several

major areas, including:

• Electronic mail/ Voice mail.

• World Wide Web.

• Chat.

• Electronic Data Interchange.

• Electronic Commerce.

• Mobile Commerce.

Page 105: Decision Support System

10

5

Using Communication Technology for Business Solutions

Intranets

These help organizations in creating richer, more responsive information

environments in which members of an organization can exchange

ideas, share information and work together on common projects and

assignments regardless of their physical location.

Page 106: Decision Support System

10

6

Using Communication Technology for Business Solutions

Extranets

These are privately owned networks that are extended to authorized users outside the company e.g. authorized buyers, retailers, distributors, customers. They are often used for collaborating with other companies for:

1. Supply Chain Management.

2. Customer Relationship Management.

3. Product design and development.

4. Training efforts.

Page 107: Decision Support System

10

7

Suppliers

Customer Relationship

Management.

Marketing. Sales. Service

Supply Chain Management.

Sourcing. Procurement

Enterprise Resource Planning.

Internal Business Processes.

Kn

ow

ledge M

anag

emen

t.

Co

llabo

ration

. Decisio

n S

up

po

rt.

Partn

er Relatio

nsh

ip M

anag

emen

t.

Sellin

g. D

istribu

tion

.

Partn

ers

Customers

Em

plo

yee

sEnterprise application architecture presenting an overview of the major

cross-functional enterprise applications and their interrelationships.

Page 108: Decision Support System

10

8

Supply Chain Management

It is a cross-functional inter-enterprise system that uses information

technology to help support and manage the links between some of

company‘s key business processes and those of its suppliers, customers

and business partners.

The goal of SCM is to create a fast, efficient and low-cost network of

business relationships, or supply chain, to get a company‘s products

from concept to market.

Page 109: Decision Support System

10

9

Enterprise Resource Planning

Integrated cross-functional software that re-engineers manufacturing,

distribution finance, human resources and other basic business process

of a company to improve its efficiency, agility, and profitability.

It focuses on the company‘s internal aspects giving them an integrated

real-time view of its core business processes.

Simply the technological backbone of e-business.

Page 110: Decision Support System

11

0

Rafi A Khan

Customer Relationship Management

A cross-functional e-business application that integrates and automates

many customer serving processes in sales, direct marketing, account

and order management, and customer service and support.

CRM systems create an IT framework of web-enabled software and

databases that integrates these processes with the rest of a company‘s

business operations.

Page 111: Decision Support System

11

1

Rafi A Khan

Knowledge Management

Organizing and sharing the diverse forms of business information

created within an organization. Includes managing project and

enterprise document libraries, discussion databases, intranet website

databases, and other types of knowledge bases.

Different phases of a knowledge management system (KMS).

• Capturing/Acquisition of data/information

• Transformation of Info. into Knowledge

• Knowledge Storage

• Disseminating/Sharing of Knowledge

Figure

Page 112: Decision Support System

11

2

Rafi A Khan

Capturing/Acquisition of Data/Information

Various technologies that can help in capturing of information

are:

• Document Management System:

Document management system keeps track of masses

of data and information, which is stored in a secure file

vault where its integrity is guaranteed and all changes to it,

is monitored, controlled, and recorded providing far easy

and faster access to all the documents. It takes care of

creating, storing, editing, and distributing documents.

Page 113: Decision Support System

11

3

Rafi A Khan

Capturing/Acquisition of Data/Information

Database

Database is a collection of data organized to serve many

applications efficiently by centralizing the data and minimizing

redundant data. It is a computerized record keeping system that stores,

maintains, and provides access to information.

Database Management System (DBMS) is simply the software

that permits an organization to centralize data, manage them

efficiently, and provide access to the stored data by applications

programs. The DBMS acts as an interface between application

programs and the physical data files.

Page 114: Decision Support System

11

4

Rafi A Khan

Capturing/Acquisition of Data/Information

Data Warehouse

An integrated collection of data extracted from operational,

historical and external databases and cleaned, transformed and

cataloged for retrieval and analysis to provide business intelligence for

business decision making.

Search Engines

These are huge databases of web page files that have been

assembled automatically by machine.

Page 115: Decision Support System

11

5

Rafi A Khan

Transformation of Info. into Knowledge

Useful technologies for this phase of the knowledge management process

include:

Multidimensional Data Analysis: Another term for multidimensional

data analysis is Online Analytical Processing (OLAP), which is a function

of business intelligence software that enables a user to easily and

selectively extract and view data from different points of view.

OLAP tools structure data hierarchically – the way managers think of

their enterprises, and also allows business analysts to rotate that data,

changing the relationships to get more detailed insight into corporate

information.

Page 116: Decision Support System

11

6

Rafi A Khan

Transformation of Info. into Knowledge

Data mining or Knowledge Discovery in Databases (KDD) provides an

organization with highly tangible benefits in the area of analysis. Data

mining is the nontrivial extraction of implicit, previously unknown,

and potentially useful information from data. This encompasses a

number of different technical approaches such as clustering, data

summarization, learning classification rules, finding dependency net

works, analyzing changes, and detecting anomalies.

Data mining software tools find hidden patterns and relationships in

large pools of data and infer rules from them that can be used to

predict future behavior and guide decision-making.

Page 117: Decision Support System

11

7

Rafi A Khan

Transformation of Info. into Knowledge contd.

Decision Support Systems (DSS)

These are a specific class of computerized information system that

supports business and organizational decision-making activities.

Artificial Intelligence (AI)

Page 118: Decision Support System

11

8

Rafi A Khan

Info/Knowledge Storage

Knowledge repositories are widely recognized as key components of

most knowledge management systems. Once knowledge is captured, it

must be stored in a knowledge repository. A knowledge repository is a

collection of both internal and external knowledge.

Page 119: Decision Support System

11

9

Rafi A Khan

Info/Knowledge Dissemination

The final phase is effectively communicating the captured "knowledge."

In fact, knowledge is not truly captured. Instead, what is captured is

information that is more easily transformed into knowledge by the

recipient. The key technologies that can be used for dissemination are:

• E-mail

• Teleconferencing, Data-conferencing

• Videoconferencing

• Groupware, and

• Intranets.

Page 120: Decision Support System

12

0

Rafi A Khan

5/12/2010 120

Decision Making2009

Page 121: Decision Support System

12

1

Rafi A Khan5/12/2010 121

DECISION MAKING

System is a collection of objects such as people, resources,

concepts, and procedures intended to perform a function or

to serve a goal.

• Closed systems are totally independent.

• Open systems dependent on their environment.

• System effectiveness is the degree to which goals are achieved.

• System efficiency is a measure of the use of inputs (or resources)

to achieve outputs.

Page 122: Decision Support System

12

2

Rafi A Khan5/12/2010 122

Decision making is a process of choosing among alternativecourses of action for the purpose of attaining a goal or goals.

(1) intelligence

(2) design

(3) choice

(4) implementation

problem solving

decision making

decision making

problem solving

Simon’s 4 Phases of Decision Making

Page 123: Decision Support System

12

3

Rafi A Khan5/12/2010 123

INTELLIGENCE PHASE

Organizational objectives

Search and scanning

Data collection

Problem identification

Problem ownership

Problem classification

Problem statement

DESIGN PHASE

Formulate a model

Set criteria for choice

Search for alternatives

Predict and measure outcomes

Reality

Implementation

of solution

Failure

Solution

Alternatives

Problem statement

Validation of the model

Verification, testing of proposed solution SuccessCHOICE PHASE

Solution to the model

Sensitivity analysis

Selection of best alternative (s)

Plan for implementation

Simplification/Assumption

Page 124: Decision Support System

12

4

Rafi A Khan5/12/2010 124

1. Intelligence phase

Scan the environment

Analyze organizational goals (e.g. Inventory Management, Job Selection, lack or an incorrect web presence)

Collect data (Monitoring & analyzing)

Identify problem

Categorize problem

– Programmed (repetitive & routine) ---Scheduling of employees, inventory level etc

– Non-programmed (Unstructured) --- Merger & Acquisitions

– Decomposed into smaller parts

Assess ownership and responsibility for problem resolution

Page 125: Decision Support System

12

5

Rafi A Khan5/12/2010 125

2. Design phase

• Formulate a model

• Set criteria for choice (Are we willing to take High risk or we prefer low risk approach)

• Search for alternatives

• Predict and measure outcomes (E.g. Profit Maximization)

Page 126: Decision Support System

12

6

Rafi A Khan5/12/2010 126

3. Choice phase

•Each alternative must be evaluated

•Sensitivity analysis (determines robustness of any given alternative)

•Selection of best alternative (s)

•Plan for implementation

solution - set of values for the decision variables in a selected alternative

Page 127: Decision Support System

12

7

Rafi A Khan5/12/2010 127

4. Implementation phase

•Putting a recommended solution to work

• Vague boundaries which include:

–Dealing with resistance to change

–User training

–Upper management support

•The problem is considered solved after the recommended solution to the model is successfully implemented.

Page 128: Decision Support System

12

8

Rafi A Khan5/12/2010 128

Source: Based on Sprague, R.H., Jr., “A Framework for the Development of DSS.” MIS Quarterly, Dec. 1980, Fig. 5, p. 13.

Page 129: Decision Support System

12

9

Rafi A Khan5/12/2010 129

Decision Support Systems

Intelligence Phase

– Automatic

• Data Mining

– Expert systems, CRM, neural networks

– Manual

• OLAP

• KMS

– Reporting

• Routine and ad hoc

Page 130: Decision Support System

13

0

Rafi A Khan5/12/2010 130

Decision Support Systems

Design Phase

– Financial and forecasting models

– Generation of alternatives by expert system

– Relationship identification through OLAP and data mining

– Use of KMS

– Business process models from CRM, RMS, ERP, and SCM

Page 131: Decision Support System

13

1

Rafi A Khan5/12/2010 131

Decision Support Systems

Choice Phase

– Identification of best alternative

– Identification of good enough alternative

– What-if analysis

– Goal-seeking analysis

– May use KMS, GSS, CRM, ERP, and SCM systems

Page 132: Decision Support System

13

2

Rafi A Khan5/12/2010 132

Decision Support Systems

Implementation Phase

– Improved communications

– Collaboration

– Training

– Supported by KMS, expert systems, GSS

Page 133: Decision Support System

13

3

Rafi A Khan

5/12/2010 133

TYPES OF DECISIONS

Page 134: Decision Support System

13

4

Rafi A Khan5/12/2010 134

TYPES OF DECISIONS

Decisions are categorized along two dimensions:-

The nature of the decision to be made

The scope of the decision itself

Page 135: Decision Support System

13

5

Rafi A Khan5/12/2010 135

TYPES OF DECISION

On the basis of the nature of the decision:-

1)Structured decision:-It‘s the one for which a well defined decision making

procedure exists.

2)Unstructured decision:- it is the one for which all the three decision phases

are unstructured.

3)Semi structured decision:- In this type one or two phases are structured and

the others are not.

Page 136: Decision Support System

13

6

Rafi A Khan5/12/2010 136

On the basis of scope of the decision itself.

1. Strategic Decision:- It is the one which effects the entire organization or a

major part of it for a long period of time

2. Tactical Decision:- It effects how a part of the organization does business

for a limited time in the future.

3. Operational Decision:- It is the one which effects a particular activity

currently taking place in an organization but either has a little impact on

the future.

Page 137: Decision Support System

13

7

Rafi A Khan5/12/2010 137

Combination of various types of Decisions

Structured /operational

Structured / tactical

Structured/ strategic

Semi-structured/ operational.

Semi-structured/ tactical

Semi-structured / strategic

Unstructured/ operational

Unstructured/ tactical

Unstructured/ strategic

Page 138: Decision Support System

13

8

Rafi A Khan5/12/2010 138

Structured/Operational: Decide how to cut a log into boards in order

to minimize wastage.

The intelligence phase is trivial; if a log arrives at mill, it must be cut .

The design phases likewise fixed; the products that the mill produces

and hence the acceptable types of cuts.

The choice phase can be optimized mathematically because the value of

each potential board is known from business consideration and the

number of boards that can be operated via each communication of cuts

is a problem.

Page 139: Decision Support System

13

9

Rafi A Khan5/12/2010 139

Structured /Tactical: Choosing the way in which to depreciate

corporate assets.

Resource allocation problems that can be solved by linear

programming methods are also in this category.

Structured /Strategic: Deciding weather or not to proceed with an

R&D project on the bases of projected ROI

A plant location decision could be in this category if the only factors in

decisions are quantifiable, such as transportation costs of known raw

materials from known locations and of known products to known

markets.

Page 140: Decision Support System

14

0

Rafi A Khan5/12/2010 140

Semistructured/Operational: Deciding to accept or reject an applicant

to a selective collage.

Semitructured /Tactical: Choosing an insurance company for an

employee health program. Cost per employee is an important and

objective factor in this decision. Intangible factors include acceptability

of a company to the employee population and the relative importance

of different benefits: is 100 percent hospitalization coverage with Rs.

500 deductible amount better or worse than 80 percent coverage with

no deductible?

Page 141: Decision Support System

14

1

Rafi A Khan5/12/2010 141

Semitructured /Strategic: Deciding whether or not to enter a new

market. Sales projections, marketplace growth data, development cost

estimates and marketing expenses forecasts can combine to provide a

profit-and-loss forecast. However there are countless factors that could

make it totally worthless. Judgment of experienced managers is

needed for the final step.

Unstructured/Operational: Dealing with a machine breakdown. There

is no set procedure what to do while awaiting repairs. The decision is

operational because the way a company deals with one machine failure

need not set a precedent for the next.

Page 142: Decision Support System

14

2

Rafi A Khan5/12/2010 142

Unstructured /Tactical: Hiring decisions typically fall into this area,

especially if the job to be filled is above level where aptitude and

ability tests can be relied on as performance indicators.

Unstructured/Strategic: Deciding how to respond to an unfriendly

takeover proposal made by a competitor. The action can have a long

term impact on the entire firm.

Page 143: Decision Support System

14

3

Rafi A Khan5/12/2010 143

Decision Support Frameworks

Type of Control

Type of Decision: Operational Control Managerial Control Strategic Planning

Structured

(Programmed)

Accounts receivable,

accounts payable, order

entry

Budget analysis, short-

term forecasting,

personnel reports

Investments, warehouse

locations, distribution

centers

Semistructured Production scheduling,

inventory control

Credit evaluation,

budget preparation,

project scheduling,

rewards systems

Mergers and

acquisitions, new

product planning,

compensation, QA, HR

policy planning

Unstructured

(Unprogrammed)

Buying software,

approving loans, help

desk

Negotiations,

recruitment, hardware

purchasing

R&D planning,

technology

development, social

responsibility plans

Page 144: Decision Support System

14

4

Rafi A Khan5/12/2010 144

The components of the quantitative model– result variable indicate how well the system performs– decision variables describe the alternative course of action– uncontrollable variables or parameters are not under the control of the decision maker

Uncontrollablevariables

Mathematicalrelationships

Result variablesDecision variables

– intermediate result variables reflect intermediate outcomes

Page 145: Decision Support System

14

5

Rafi A Khan5/12/2010 145

Examples of the Components of Models.

Area

Decision

Variables

Result

Variables

Uncontrollable

Variables and Parameters

Financial investment Investment alternatives and

amounts

How long to invest

When to invest

Total profit

Rate of return (ROI)

Earnings per share

Liquidity level

Inflation rate

Prime rate

Competition

Marketing Advertising budget

Where to advertise

Market share

Customer satisfaction

Customers' income

Competitors' actions

Manufacturing What and how much to

produce

Inventory levels

Compensation programs

Total cost

Quality level

Employee satisfaction

Machine capacity

Technology

Materials prices

Accounting Use of computers

Audit schedule

Data processing cost

Error rate

Computer technology

Tax rates

Legal requirements

Transportation Shipments schedule Total transport cost Delivery distance

Regulations

Services Staffing levels Customer satisfaction Demand for services

Page 146: Decision Support System

14

6

Rafi A Khan5/12/2010 146

Example

Company makes special purpose computers.Decision to be made: how many computers should be produced next month?Two types of computers are considered: T1, T2.They require different days of labour, different costs for material.

Uncontrollable

variables

constraints on labour

and budget

Mathematical

relationships

Maximise profit

subject to constraints

Result variables

Total profit

Decision variables

X1 = NofT1

X2 = NofT2

Page 147: Decision Support System

14

7

Rafi A Khan5/12/2010 147

Principle of choice is a decision regarding the acceptability of a solution approach.

• Normative models– chosen alternative is the best of all possible alternatives– suboptimisation– optimisation models

• Descriptive models describe things as they are, or as they arebelieved to be.

– no guarantee a solution is optimal– simulation

Generating alternatives– automatically by the model– by using heuristics

Page 148: Decision Support System

14

8

Rafi A Khan5/12/2010 148

Predicting the outcomes of alternatives

1. Decision making under certainty

Decision maker knows exactly what the outcome of

each course of action will be - deterministic environment.

2. Decision making under risk

Each alternative has several possible outcomes,

each with a given probability of occurrence

- probabilistic or stochastic decision situation.

3. Decision making under uncertainty

Several outcomes are possible for each course of action,

their probabilities are not known.

Page 149: Decision Support System

14

9

Rafi A Khan5/12/2010 149

Measuring outcomes

The value of the an alternative is judged in terms of

goal attainment.

Scenario describes the decision and uncontrollable variables

and parameters for a specific modelling situation.

Of special interest are:

– the worst possible scenario

– the best possible scenario

– the most likely scenario

Page 150: Decision Support System

15

0

Rafi A Khan5/12/2010 150

Search

• Analytical techniques

– mathematical formulae

– algorithm: step-by-step search process

• Blind search

– complete enumeration

– incomplete search

• Heuristic search (derived from the Greek word for discovery)

rules guide the search process

Normative models:

– analytical techniques

– complete, exhaustive enumeration

Descriptive models:

– blind search

– using heuristics

Page 151: Decision Support System

15

1

Rafi A Khan5/12/2010 151

Evaluation

• Multiple goals

– Today's management systems want to achieve

multiple goals simultaneously.

– Goals are usually partially or totally conflicting.

• Sensitivity analysis

Checks the impact of a change in the input data or parameters

on the proposed solution (the result variable)

1. Automatic sensitivity analysis

tells the range within which an input variable or parameter

can vary without impact on the proposed solution

one change at a time

2. Trial and error

some input data are changed

Page 152: Decision Support System

15

2

Rafi A Khan5/12/2010 152

• What-if-analysis

What will happen to the solution if an input variable or

a parameter is changed?

e.g. what will happen to the total inventory cost if the cost of

carrying inventories increases by 10%?

• Goal seeking analysis

Computes the amount of inputs necessary to achieve a desired level

of an input (goal).

e.g. How many nurses are needed to reduce the average waiting time

of a patient in the emergency room to less than 10 minutes.

Page 153: Decision Support System

15

3

Rafi A Khan5/12/2010 153

Page 154: Decision Support System

15

4

Rafi A Khan5/12/2010 154

Literature:

1. (a) Decision Support Systems and Intelligent Systems, Fifth Edition

E.Turban, Jay Aronson,

Prentice Hall, 1998.

(b) Decision Support Systems and Expert Systems,

Management Support Systems, E.Turban, Fourth Edition,

Prentice Hall, 1995.

2. Knowledge-based Decision Support Systems, With Applications

in Business, 2nd Edition, M. Klein, L. Methlie,

Wiley, 1995.

Page 155: Decision Support System

15

5

Rafi A Khan5/12/2010 155

Systems are composed of inputs, outputs, processes, and

decision makers.

A model is simplified representation or abstraction of reality.

They can be iconic, analog, or mathematical.

Decision making involves four major phases: intelligence, design,

choice, and implementation.

SUMMARY

Page 156: Decision Support System

15

6

Rafi A Khan5/12/2010 156

Models

A model is a simplified representation or abstraction of reality.

1. Iconic model is a physical replica of a system.

2. Analog model gives a symbolic representation of reality, behaves like the real system but does not look like it.

3. Mathematical (quantitative) models use mathematical relationshipsBenefits:– compression of time– easy model manipulation– low cost of the analysis– cost of making mistakes is less than mistakes on real system– can model risk and uncertainty– a very large number of solutions can be analysed– enhance learning and training

Page 157: Decision Support System

15

7

Rafi A Khan5/12/2010 157

3. Optimisation

model generates an optimal solution

Limitations:

– works if the problem is structured and deterministic

4. Heuristics

Informal knowledge of how to solve problems efficiently and

effectively, how to plan steps in solving a complex problem,

how to improve performance, and so forth.

Page 158: Decision Support System

15

8

Rafi A Khan5/12/2010 158

Modelling Process

Example: How much to order for the grocery?

The Question: How much bread to stock each day?

1. Trial-and-error

experimentation on the real system

Not appropriate if:

– too many alternatives to explore

– the cost of making errors is very high

– the environment keeps changing

2. Simulation

assume the appearance of the characteristics of reality

Problems:

– no guarantee that the solution is optimal one

– professional development

Page 159: Decision Support System

15

9

Rafi A Khan5/12/2010 159

Definition of DSS

DSS is an interactive computer-based systems, which help decision makers

utilize data and models to solve unstructured problems.

DSS is an interactive computer-based systems, which help decision makers

utilize data and models to solve unstructured problems.

Page 160: Decision Support System

16

0

Rafi A Khan5/12/2010 160

Definition of DSS

Decision Support Systems (DSS) are a class of computerized information

systems that support decision-making activities. DSS are interactive

computer-based systems and subsystems intended to help decision makers

use communications technologies, data, documents, knowledge and/or

models to successfully complete decision process tasks.

Page 161: Decision Support System

16

1

Rafi A Khan5/12/2010 161

Components of DSS

Other computer based systems

Internet, intranet, extranet.

Data management Model management External models

Knowledge-based subsystems

User interface

Manager (user)Organizational KB

Page 162: Decision Support System

16

2

Rafi A Khan5/12/2010 162

Components of DSS

Data management subsystem

The data management subsystem includes a data base, which contains

relevant data for the situation and is managed by software call the database

management system (DBMS) .the data management subsystem can be

interconnected with the corporate data warehouse, a repository for

corporate relevant decision making data.

Model management subsystem

This is software package that includes financial, statistical, management

science, or other quantitative models that provide the system analytical

capabilities and appropriate software management. Modeling languages in

building custom models are also included, this software is often called a

model base management system (MBMS). This component can be

connected to corporate or external storage of models.

Page 163: Decision Support System

16

3

Rafi A Khan5/12/2010 163

Components of DSS

Knowledge based management subsystem

This subsystem can support any of the other subsystems or act as an

independent component. It provides intelligence to augment the decision

maker‘s own. It can be interconnected with the organization‘s knowledge

depository, which is called the organizational knowledge base.

User interface subsystem

The user communicates with and commands the DSS through this

subsystem. The user is considered part of the system. Researchers assert that

some of unique contributions of DSS are derived from the intensive

interaction between the computer and the decision maker.

Page 164: Decision Support System

16

4

Rafi A Khan5/12/2010 164

TH

E D

AT

A M

AN

AG

EM

EN

T S

UB

SY

ST

EM

External data Source

Internal data sources

Finance Marketing Production Personal Other

Extraction

Organizationalknowledge base

Private personal data

Decision support

database

QueryFacility

Corporate datawarehouse

Database management System

oRetrievaloInquiryoUpdateoReport generationoDelete

Data directory

Interfacemanagement

Model management

Knowledge-based subsystem

Page 165: Decision Support System

16

5

Rafi A Khan5/12/2010 165

THE DATA MANAGEMENT SUBSYSTEM

The data management subsystem is composed of the following elements:

DSS database

Database management system

Data directory.

Query facility.

Page 166: Decision Support System

16

6

Rafi A Khan5/12/2010 166

THE DATABASE

A database is a collection of interrelated data organized to meet the needs

and struc­ture of an organization and can be used by more than one person

for more than one ap­plication

The data in the DSS database are extracted from internal and external data

sources, as well as from personal data belonging to one or more Users.

Page 167: Decision Support System

16

7

Rafi A Khan5/12/2010 167

DATA ORGANISATION

In small ad hoc DSS, data can be entered directly into models some times

extracted directly from larger databases.

In large organizations that use extensive data ,such as Wal-Mart, AT&T,

and United Air Lines data are organized in a data warehouse and used

when needed .

Page 168: Decision Support System

16

8

Rafi A Khan5/12/2010 168

EXTRACTION

To create a DSS database or a data warehouse it is often necessary to capture

data from several sources. This operation is called extraction.

It basically consists of importing of files, summarization, standardization,

filtration, and condensation of data.

The data for the warehouse are extracted from internal and external sources.

The extraction process is frequently managed by a DBMS.

Page 169: Decision Support System

16

9

Rafi A Khan5/12/2010 169

DATABASE MANAGEMENT SYSTEM

A database is created, accessed, and updated by a DBMS.

Most DSS are built with a standard commercial relational DBMS that

provides capabilities such as it captures or extracts data for inclusion in a

DSS database ,it updates (adds, deletes, edits, changes) data records and

files, retrieves data ,provides data security etc.

Page 170: Decision Support System

17

0

Rafi A Khan5/12/2010 170

THE QUERY FACILITY

Query facility is necessary to access, manipulate, and query data.

The query facility includes a special query language.

Important functions of DSS query system are selection and manipulation

operation (e.g., the ability to follow a computer instruction such as "Search

for a sales in zone B during June 2000 and summarize sales by salesperson").

Page 171: Decision Support System

17

1

Rafi A Khan5/12/2010 171

THE DIRECTORY

The data directory is a catalog of all the data in the database.

It contains data definitions and its main function is to answer questions

about the availability of data items, their source, and their exact meaning.

The directory is especially appropriate for supporting the intelligence phase

of the decision-making process by helping to scan data and identify problem

areas or opportunities.

It supports the addition of new entries, deletion of entries, and retrieval of

information on specific objects.

Page 172: Decision Support System

17

2

Rafi A Khan5/12/2010 172

General Functions of the DBMS

Data Definition

Provides a data definition language (DDL) that allows users to describe

the data entities and their associated attributes and relationships

Allows for the interrelation of data from multiple sources

Data Manipulation

Provides the user with a query language to interact with the database

Allows for capture and extraction of data

Provides rapid retrieval of data for ad hoc queries and reports

Allows for the construction of complex queries for retrieval and data

manipulation

Page 173: Decision Support System

17

3

Rafi A Khan5/12/2010 173

Data Integrity

Allows the user to describe rules (integrity constraints) to maintain the

integrity of the database

Assists in the control of erroneous data entry based on the defined integrity

constraints

Access Control

Allows identification of authorized users

Controls access to data various elements and data manipulation activities

within the database

Tracks usage and access to data by authorized users

Concurrency Control

Provides procedures for controlling simultaneous access to the same data

by more than one user

Page 174: Decision Support System

17

4

Rafi A Khan

5/12/2010 174

Improved data sharing.

The DBMS helps create an environment in which end users have

better access to more and better-managed data. Such access takes it

possible for end users to respond quickly to changes in their

environment.

Transaction Recovery

Provides a mechanism for restart and reconciliation of the database in

the event of hardware failure

Records information on all transactions at certain points to enable

satisfactory database restart

Minimized data inconsistency.

Data inconsistency exists when different versions of the same data

appear in different places.

Page 175: Decision Support System

17

5

Rafi A Khan5/12/2010 175

Improved decision making.

Better-managed data and improved data access make it possible to

generate better quality information, on which better decisions are based.

Increased end-user productivity.

The availability of data, combined with the tools that transform data

into usable information, empowers end users to make quick, informed

decisions that can make the difference between success and failure in the

global economy.

Page 176: Decision Support System

17

6

Rafi A Khan5/12/2010 176

Models (model base)•Strategic, tactical, operational•Statistical, financial, marketing,

mgt. science, accounting etc•Model building blocks

Model directory

Model base management•Modeling commands : creation•Maintenance: update•Database interface•Modeling language

Model execution, integration, and command processor

Data management Interface management Knowledge –based subsystem

Structure of Model Management System

Page 177: Decision Support System

17

7

Rafi A Khan5/12/2010 177

Functions of the MBMS

Creates models easily and quickly, either from scratch or from the building

blocks

Allows users to manipulate models so that they can conduct experiments

and sensitivity analyses ranging from what-if to goal seeking

Stores, retrieves and manages a wide variety of different types of models

in a logical and integrated manner

Accesses and integrates the model building blocks

Catalogs and displays the directory of models for use by several

individuals in the organization

Page 178: Decision Support System

17

8

Rafi A Khan5/12/2010 178

Functions of the MBMS

Tracks model data and application use

Interrelates model with appropriate linkages with the database and

integrates them within the DSS

Manages and maintains the model base with management functions

analogous to database management: store, access, run, update, link, catalog,

and query

Use multiple models to support problem solving

Page 179: Decision Support System

17

9

Rafi A Khan5/12/2010 179

US

ER

INT

ER

FA

CE

MA

NA

GE

ME

NT

SY

ST

EM

Data management

and DBMSKnowledge- based

subsystemModel management

and MBMS

User Interface Management System

(UIMS)

Language Processor

Printers, plotters

Users

Input Output

Action DisplayLanguages Languages

Page 180: Decision Support System

18

0

Rafi A Khan5/12/2010 180

General Functions of the DSS Interface

Allows for interaction with the DSS in a variety of dialog styles

Accommodates the user with a variety of input devices

Presents data with a variety of formats and output devices

Gives user help capabilities, prompting, diagnostic and suggestion routines,

or any other flexible support.

Stores input and output data.

Provides support for communication among and between multiple DSS

users

Page 181: Decision Support System

18

1

Rafi A Khan5/12/2010 181

General Functions of the DSS Interface

variety of formats included menu driven, question/answer, procedural

command language, or natural command language

Provides for the presentation of data in a variety of formats

Allows for detailed report definition and generation by the DSS user

Allows for the creation of forms, tables, and graphics for data output

Can provide multiple ―windows‖ or views of the data to be available

simultaneously

Page 182: Decision Support System

18

2

Rafi A Khan5/12/2010 182

CHARACTERISTICS OF DSS

DSS provides support for decision makers mainly in semi-structured and

unstructured situations by bringing together human judgment and

computerized information.

Support is provided for various managerial levels, ranging from top

executives to line managers.

Support is provided to individuals as well as to groups.

DSS provides support to several interdependent or sequential decisions.

The decisions may be made once, several times or repeatedly.

DSS supports all phases of decision making process; intelligence, design,

choice and implementation.

Page 183: Decision Support System

18

3

Rafi A Khan5/12/2010 183

CHARACTERISTICS OF DSS

DSS are adaptive over time. DSS are flexible and so users can add, delete,

combine, change or rearrange basic elements.

User Interface – Interactive and friendly.

DSS attempt to prove the effectiveness of decision making rather than its

efficiency.

The decision maker has complete control over all steps of the decision

making process in solving a problem. A DSS specifically aims to support

and not to replace the decision maker.

Page 184: Decision Support System

18

4

Rafi A Khan5/12/2010 184

CHARACTERISTICS OF DSS

End users should be able to construct and modify simple systems by

themselves. Larger systems can be built with assistance from information

system (IS) specialists.

A DSS usually utilizes models for analyzing decision making situations. The

modeling capability enables experimenting with different strategies under

different configurations.

Page 185: Decision Support System

18

5

Rafi A Khan5/12/2010 185

Benefits of DSS Use

Extend the decision-maker‘s ability to process information and knowledge

Extend the decision-maker‘s ability to tackle large-scale, time-consuming,

complex problems

Improve the time associated with making a particular decision

Improve the reliability of a particular decision process or outcome

Encourage exploration and discovery on the part of the decision-maker

Reveal new approaches to thinking about a particular problem space or

decision context

Generate new evidence in support of a particular decision or confirmation

of existing assumptions

Create a strategic or competitive advantage over competing organizations

Page 186: Decision Support System

18

6

Rafi A Khan5/12/2010 186

Limitations of DSS Use

DSSs cannot yet be designed to contain distinctly human decision-

making talents such as creativity, imaginativeness, or intuition

The power of a DSS is limited by the computer system upon which it is

running, its design, and the knowledge it possesses at the time of its

use

Language and command interfaces are not yet sophisticated enough to

allow for natural language processing of user directives and inquiries

DSSs are normally designed to be narrow in scope of application thus

limiting their generalizability to multiple decision-making contexts

Page 187: Decision Support System

18

7

Rafi A Khan 187

DSS Classification

1. Alter’s Output Classification (1980)

2. Holsapple and Whinston’s Classification

1. Text-oriented DSS

2. Database-oriented DSS

3. Spreadsheet-oriented DSS

4. Solver-oriented DSS

5. Rule-oriented DSS

6. Compound DSS

Page 188: Decision Support System

18

8

Rafi A Khan5/12/2010 188

Alters' Classification of DSS

Page 189: Decision Support System

18

9

Rafi A Khan5/12/2010 189

Alter’s Classification of DSS

Data-Driven DSS

Data-Driven DSS take the massive amounts of data available through the

company's TPS and MIS systems and cull from it useful information which

executives can use to make more informed decisions.

Data- Driven DSS emphasize access to and manipulation of large databases

of structured data

Page 190: Decision Support System

19

0

Rafi A Khan5/12/2010 190

Alter’s Classification of DSS

Model-Driven DSS

A second category, Model-Driven DSS (accounting and financial models,

representational models, and optimization models).

Model-Driven DSS emphasize access to and manipulation of a model.

Model-Driven DSS use data and parameters provided by decision-makers to

aid them in analyzing a situation, but they are not usually data intensive.

Very large databases are usually not needed for Model-Driven DSS.

Primarily used for the typical "what-if" analysis. That is, "What if we

increase production of our products and decrease the shipment time?"

Page 191: Decision Support System

19

1

Rafi A Khan5/12/2010 191

DSS Classifications

Holsapple and Whinston’s Classification

1. Text-oriented DSS

2. Database-oriented DSS

3. Spreadsheet-oriented DSS

4. Solver-oriented DSS

5. Rule-oriented DSS

6. Compound DSS

Page 192: Decision Support System

19

2

Rafi A Khan5/12/2010 192

Holsapple and Winston Classification

TEXT ORIENTED DSS

Textually represented information that could have a bearing on decision.

Documents to be electronically created, revised and viewed as needed.

Information Technologies such as documents imaging, hypertext etc can be

incorporated into this type.

DMS, KMS, Content Mgt System, Business rule system

DATABASE ORIENTED DSS

In this type of DSS the database plays a major role in the DSS structure.

Strong report generation and query capabilities.

Data are organized in a highly structured format.

Page 193: Decision Support System

19

3

Rafi A Khan5/12/2010 193

Holsapple and Winston Classification

SPREADSHEET ORIENTED DSS

Spreadsheet is a modeling language that allows the user to write models to

execute DSS analysis.

Tools- Statistical packages, linear programming package (Solver), financial

and management science models.

The most popular tools used are Excel and Lotus 1-2-3.

SOLVER ORIENTED DSS

A solver is an algorithmic or procedure written as a computer program for

performing certain computations for solving a particular problem type.

EOQ for calculating optimal ordering quantity or a linear regression routine

for calculating trend.

Excel, Lotus 1-2-3 and quatro pro can be used to develop such a system.

C++, Lingo etc

Page 194: Decision Support System

19

4

Rafi A Khan5/12/2010 194

Holsapple and Winston Classification

RULE ORIENTED DSS

The knowledge component of DSS includes both procedural and inferential

(Reasoning) rules, often in an expert system, format.

Assignment Algorithm for Flight Scheduling

COMPOUND DSS

It is a hybrid system that includes two or more of the fine basic structures

explained above. It can be built by using a set of independent DSS, each

specializing in one area.

Page 195: Decision Support System

19

5

Rafi A Khan5/12/2010 195

Other DSS Classification

Personal

Group

Organizational

Custom VS Readymade

Page 196: Decision Support System

19

6

Rafi A Khan5/12/2010 196

DSS Classification

OTHER CLASSIFICATIONS OF DSS

INSTITUTIONAL DSS

Deal with decisions of a recurring nature. An institutionalized DSS can be

developed and refined as it evolves over a number of years because the DSS

is used repeatedly to solve identical or similar problems.

Portfolio Management

ADHOC DSS

Deals with specific problems that are usually neither anticipated nor

recurring. Adhoc decisions often involve strategic planning issues

sometimes management control problems.

Page 197: Decision Support System

19

7

Rafi A Khan5/12/2010 197

Knowledge-Driven DSS

Knowledge-Driven DSS

It suggest or recommend actions to managers.

These DSS are computer systems with specialized problem-solving

expertise.

The "expertise" consists of knowledge about a particular domain,

understanding of problems within that domain, and "skill" at solving some

of these problems.

A related concept is Data Mining. It refers to a class of analytical

applications that search for hidden patterns in a database.

Data mining is the process of searching through large amounts of data to

produce data content relationships.

Page 198: Decision Support System

19

8

Rafi A Khan5/12/2010 198

Document-Driven DSS

A new type of DSS, a Document-Driven DSS is evolving to help managers

retrieve and manage unstructured documents and Web pages.

The Web provides access to large document databases including databases

of hypertext documents, images, sounds and video.

Examples of documents that would be accessed by a Document-Based DSS

are policies and procedures, product specifications, catalogs, and corporate

historical documents, including minutes of meetings, corporate records, and

important correspondence.

A search engine is a powerful decision aiding tool associated with a

Document-Driven DSS.

Page 199: Decision Support System

19

9

Rafi A Khan5/12/2010 199

Communications-Driven and Group DSS

Group Decision Support Systems (GDSS) came first, but now a broader

category of Communications-Driven DSS or groupware can be identified.

It includes communication, collaboration and decision support technologies

that do not fit within those DSS types identified.

A Group DSS is a hybrid Decision Support System that emphasizes both the

use of communications and decision models.

A Group Decision Support System is an interactive computer-based system

intended to facilitate the solution of problems by decision-makers working

together as a group.

Groupware supports electronic communication, scheduling, document

sharing, two-way interactive video, White Boards, Bulletin Boards, and

Email.

Page 200: Decision Support System

20

0

Rafi A Khan

MIS

1. Impact on Structured Tasks, where standard procedures, decision

rules and information flows can be reliably Predefined.

2. Payoff – Improvement in efficient by reducing costs, turnaround

time , replacing clerical personnel or increasing their productivity.

Mg

t. Sci / O

R

1. Impact mostly on Structured problems (rather than tasks), in which

the objective, data and constraints can be prespecified.

2. Payoff – generation of better solutions for general categories of

problems (e.g. inventory).

DS

S

1. Impact is on decisions in which there is sufficient structure for

computer and analytic aids to be of value but where the managers

judgment is essential.

2. Payoff – extending the range and capability of managers decision

process to help them improve their effectiveness.

Page 201: Decision Support System

20

1

Rafi A Khan

MIS

3. Relevance for managers decision making – indirect (e.g. by

providing reports and access to data.

4. MIS application is routine and done periodically.

Mg

t. Sci / O

R

3. Relevance for managers – provision of detailed recommendation

and new methods handling complex problems.

4. Application are nonroutine, as needed.

DS

S

3. Relevance for managers – creation of supportive tool, under their

own control..

4. Application are nonroutine, as needed.

Page 202: Decision Support System

20

2

Rafi A Khan

5/12/2010

Knowledge Management

The Business School

University of Kashmir

Page 203: Decision Support System

20

3

Rafi A Khan5/12/2010 10:21:04 PM Rafi A Khan

Knowledge Management

Ancient

Collaboration at the organizational level

Could revolutionize collaboration and computing

Page 204: Decision Support System

20

4

Rafi A Khan5/12/2010 10:21:04 PM Rafi A Khan

Knowledge Management

Helps organizations

Identify

Select

Organize

Disseminate

Transfer

Important information and expertise within the organizational

memory in an unstructured manner

Page 205: Decision Support System

20

5

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Knowledge

Understanding gained through experience or study

Know-how or familiarity with how to do something

Information that is contextual, relevant, and actionable

Accumulation of facts, procedural rules or heuristics

Knowledge is INFORMATION IN ACTION

Actionable (relevant) information available in the right format, at

the right time, and at the right place for decision making

(TIWANA2000)

Page 206: Decision Support System

20

6

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Knowledge

Fact – statement of some element of truth about a subject matter or a domain.

Example: milk is white, sun rises in east.

Heuristics – rule of thumb based on years of experience.

Example: strike on independence day in our state

Intelligence – capacity to acquire, improve and apply knowledge.

Experience – what we have done and what has happened in past in a specific

area of work

Common sense – natural ability to sense, judge or perceive situations ; grows

stronger over time.

Memory – ability to store and retrieve relevant experience at will, is part of

intelligence.

Learning – is knowledge or skill that is acquired by instruction or study

Page 207: Decision Support System

20

7

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Knowledge Types

Explicit knowledge

– Objective, rational, technical

– Policies, goals, strategies, papers, reports

– Codified

– Leaky knowledge

Tacit knowledge

– Subjective, experiential learning

– Highly personalized

– Difficult to formalize

Page 208: Decision Support System

20

8

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Knowledge Types

Shallow (surface) knowledge

– Indicates minimal understanding of the problem area

Example – If u don‘t have petrol in your car, the car

wont start

Deep knowledge

– Indicates maximal understanding of the problem area

Example – why don‘t a car starts when it has no petrol

(need to know various components of car)

Page 209: Decision Support System

20

9

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Knowledge Types

– Descriptive – data, information

– Procedural – how to do something

– Reasoning – policies or rules

– Linguistic – vocabulary or grammar

– Presentation – graphing, messaging

Page 210: Decision Support System

21

0

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

DATA

Processed

INFORMATIONRelevant and

actionable

KNOWLEDGE

Relevant and actionable data

Data, Information and Knowledge

Page 211: Decision Support System

21

1

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Knowledge Management (KM)

A process of capturing, transformation, and diffusion of

knowledge throughout an enterprise so that it can be

shared and thus REUSED

Helps organizations find, select, organize, disseminate,

and transfer important information and expertise

Transforms data / information into actionable knowledge

to be used effectively anywhere in the organization by

anyone

Page 212: Decision Support System

21

2

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

KM Objectives

Create knowledge repositories

Improve knowledge access

Enhance the knowledge environment

Manage knowledge as an asset

Page 213: Decision Support System

21

3

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

KMS Manage

Knowledge creation through learning

Knowledge capture

Knowledge sharing and communication through

collaboration

Knowledge access

Knowledge use and reuse

Knowledge archiving

Page 214: Decision Support System

21

4

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

Create knowledge

Capture knowledge

Refine knowledge

Store knowledge

Manage knowledge

Disseminate knowledge

Page 215: Decision Support System

21

5

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

Create Knowledge

Page 216: Decision Support System

21

6

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

Create Knowledge

Page 217: Decision Support System

21

7

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

Create Knowledge

CaptureKnowledge

Page 218: Decision Support System

21

8

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

Create Knowledge

CaptureKnowledge

Page 219: Decision Support System

21

9

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

RefineKnowledge

Create Knowledge

CaptureKnowledge

Page 220: Decision Support System

22

0

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

RefineKnowledge

Create Knowledge

CaptureKnowledge

Page 221: Decision Support System

22

1

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

RefineKnowledge

Create Knowledge

CaptureKnowledge

StoreKnowledge

Page 222: Decision Support System

22

2

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

RefineKnowledge

Create Knowledge

CaptureKnowledge

StoreKnowledge

Page 223: Decision Support System

22

3

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

ManageKnowledge

RefineKnowledge

Create Knowledge

CaptureKnowledge

StoreKnowledge

Page 224: Decision Support System

22

4

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

ManageKnowledge

RefineKnowledge

Create Knowledge

CaptureKnowledge

StoreKnowledge

Page 225: Decision Support System

22

5

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

ManageKnowledge

RefineKnowledge

Create Knowledge

CaptureKnowledge

StoreKnowledge

DisseminateKnowledge

Page 226: Decision Support System

22

6

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Cyclic Model of KM

ManageKnowledge

RefineKnowledge

Create Knowledge

CaptureKnowledge

StoreKnowledge

DisseminateKnowledge

Page 227: Decision Support System

22

7

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Why Adopt KM

Cost savings

Better performance

Demonstrated success

Share Best Practices

Competitive Advantage

Page 228: Decision Support System

22

8

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

KM Methods, Technologies, and Tools

Email or messaging

Document management

Search engines

Enterprise information portal

Data warehouse

Groupware

Workflow management

Web-based training

Others

Page 229: Decision Support System

22

9

Rafi A Khan

5/12/2010

Knowledge Acquisition Techniques

The Business School

University of Kashmir

Page 230: Decision Support System

23

0

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Knowledge Acquisition

The following are main methods of knowledge acquisition :

• Production Rule

• Frames

• Semantic Network

Page 231: Decision Support System

23

1

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Production Rules

IF-THEN

Independent part, combined with other pieces, to produce

better result

Model of human behavior

Examples

– IF condition, THEN conclusion

– Conclusion, IF condition

– If condition, THEN conclusion1 (OR) ELSE conclusion2

Page 232: Decision Support System

23

2

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Frames

Organized structure of knowledge

Put related knowledge in one area called frame

A frame consists of slots representing a part of

knowledge

Each slot has a value in the form of data,

information, process and rules

Frame can be related to other frames

Page 233: Decision Support System

23

3

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Frames(Engine Overheating)

Slot : Symptoms Value

Temp More than 80 deg

Water Boiling

Speed Retardation

Slot : Inspection

Value

Check Water Level

Oil in Engine

Carburetor

Slot : Treatment Value

Stop Engine & Drain

Water

Start Engine & pour cold Water

Increase oil level

Adjust Carburetor

Page 234: Decision Support System

23

4

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Semantic Networks

Graphical

depictions

Nodes and links

connecting nodes

Node represents an

Entity & link

represents

Association

Hierarchical

relationships

between concepts

Page 235: Decision Support System

23

5

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Inferencing

Inferencing means deriving a conclusion based on statements

that only imply that conclusion.

Every rule in knowledge base can be checked to see whether

its premise (principle) or conclusion can be satisfied by

previously made assertions.

This process can be done in two directions :

–Forward

–Backward

Page 236: Decision Support System

23

6

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Inference Techniques

Forward Chaining

Forward chaining is a data-driven approach . We start from available

information as it becomes available or from a basic idea, and then we try to

draw conclusions.

Backward chaining

Backward chaining is a goal-driven approach in which you start from an

expectation of what is going to happen (hypothesis) and then seek evidence

that supports (or contradicts) your expectation.

Page 237: Decision Support System

23

7

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Example

Investment Decision : Whether to invest in IBM Stocks

The following variables are used:

– A= Have Rs.10,000

– B= Younger than 30

– C= Education at college level

– D= Annual income of atleast Rs.40,000

– E= Invest in securities

– F= Invest in growth stocks

– G= Invest in IBM stock (the potential goal)

The facts: we assume that an investor has Rs.10,000(that A is true) and

that she is 25 years old (B is true). She would like advice on

investing in IBM stock(yes or no for the goal).

Page 238: Decision Support System

23

8

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Example

The Rules:Our knowledge base includes the following five rules:

R1: IF a person has Rs10,000 to invest and has a college degree THEN she should invest in securities

R2: IF a persons annual income is atleast Rs40,000 to invest and has a college

degree THEN she should invest in growth stocks

R3: IF a person is < 30 and is investing in securities THEN she should invest in

growth stocks

R4: IF a person is < 30 and >22 THEN she has a college degree

R5:IF a person wants to invest in growth stocks then the stock should be IBM

– R1: IF A and C, THEN E

– R2: IF D and C,THEN F

– R3: IF B and E, THEN F

– R4: IF B, THEN C

– R5: IF F, THEN G

Page 239: Decision Support System

23

9

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Forward Chaining

Page 240: Decision Support System

24

0

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Backward Chaining

Page 241: Decision Support System

24

1

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

The End

Page 242: Decision Support System

24

2

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Experts

Experts

– Have special knowledge, judgment, and experience

– Can apply these to solve problems

• Higher performance level than average person

• Faster Solutions

• Recognize Patterns

Expertise

– Task specific knowledge of experts

• Acquired from reading, training, practice

Page 243: Decision Support System

24

3

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Expert System

Expert Systems: a computer application that employs a set of rules based

on human knowledge to solve problems that require human expertise

Information systems that solve problems by capturing knowledge for a

very specific and limited domain of human expertise are called expert

systems

– For example, diagnosing a cars ignition system, classifying biological

specimen

Page 244: Decision Support System

24

4

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Common Expert System Architecture

Knowledge Base

User

Interface

Inference

Engine

User Environment

User

Page 245: Decision Support System

24

5

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

KBES

Knowledge based expert system (KBES) has three basic

components:

• Knowledge base

• User control mechanism

• Inference Mechanism

Page 246: Decision Support System

24

6

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

User Interface

Design of the UI focuses on human concerns such as ease of use,

reliability and reduction of fatigue

Design should allow for a variety of methods of interaction

(input, control and query)

Mechanisms include touch screen, keypad, light pens, voice

command, hot keys

Page 247: Decision Support System

24

7

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Knowledge Base

Contains the domain-specific knowledge acquired from the

domain experts

Can consist of all the theoretical foundations, facts, judgments,

rules, formulas, intuitions and experience

The success of an ES relies on the completeness and accuracy of

its knowledge base

Page 248: Decision Support System

24

8

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Inference Engine

Here, the knowledge is put to use to produce solutions

Interprets the knowledge available and performs logical

deductions in a given situation.

It is a strategy used to search through rule base

Page 249: Decision Support System

24

9

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Applications of Expert Systems

DENDRAL project

– Applied knowledge or rule-based reasoning commands

– Deduced likely molecular structure of compounds

MYCIN

– Rule-based system for diagnosing bacterial infections

XCON

– Rule-based system to determine optimal systems configuration

Credit analysis

– Ruled-based systems for commercial lenders

Pension fund adviser

– Knowledge-based system analyzing impact of regulation

Page 250: Decision Support System

25

0

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Applications

Finance

– Insurance evaluation, credit analysis, tax planning, financial planning and reporting, performance evaluation

Data processing

– Systems planning, equipment maintenance, vendor evaluation, network management

Marketing

– Customer-relationship management, market analysis, product planning

Human resources

– HR planning, performance evaluation, scheduling, pension management, legal advising

Manufacturing

– Production planning, quality management, product design, plant site selection, equipment maintenance and repair

Page 251: Decision Support System

25

1

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Benefits of Expert Systems

Increased outputs

Increased productivity

Decreased decision-making time

Increased process and product quality

Reduced downtime

Capture of scarce expertise

Flexibility

Ease of complex equipment operation

Elimination of expensive monitoring equipment

Operation in hazardous environments

Access to knowledge and help desks

Page 252: Decision Support System

25

2

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Benefits of Expert Systems

Ability to work with incomplete, imprecise, uncertain data

Provides training

Enhanced problem solving and decision-making

Rapid feedback

Facilitate communications

Reliable decision quality

Ability to solve complex problems

Ease of knowledge transfer to remote locations

Provides intelligent capabilities to other information

Page 253: Decision Support System

25

3

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Limitations

Knowledge not always readily available

Difficult to extract expertise from humans

Lack of end-user trust

Knowledge subject to biases

Systems may not be able to arrive at conclusions

Page 254: Decision Support System

25

4

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

The End

Page 255: Decision Support System

25

5

Rafi A Khan

5/12/2010

The Data Warehouse

Department of Management Studies

University of Kashmir

Page 256: Decision Support System

25

6

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Objective

How operational data and decision support data differ

What a data warehouse is, how data for it are prepared, and how it is

implemented

What data mining is and what role it plays in decision support

What online analytical processing (OLAP) is

Page 257: Decision Support System

25

7

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

The Need for Data Analysis

Managers must be able to track daily transactions to evaluate how the

business is performing

By tapping into operational database, management can develop strategies

to meet organizational goals

Data analysis can provide information about short-term tactical

evaluations and strategies

Page 258: Decision Support System

25

8

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Operational Data

– Mostly stored in relational database

– Optimized to support transactions representing daily operations

DSS Data

– Give tactical and strategic business meaning to operational data

– Differs from operational data in following three main areas:

• Time span

• Granularity

• Dimensionality

Operational Data vs. Decision Support Data

Page 259: Decision Support System

25

9

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Operational Data vs. Decision Support Data

Page 260: Decision Support System

26

0

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Operational Data vs. Decision Support Data

Page 261: Decision Support System

26

1

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

The Data Warehouse

Integrated, subject-oriented, time-variant, nonvolatile collection of data

that provides support for decision making

Usually a read-only database optimized for data analysis and query

processing

Requires time, money, and considerable managerial effort to create

Page 262: Decision Support System

26

2

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

Characteristics of a Data Warehouse

• Subject orientation: data is organized based on how the users refer to it.

• Integrated: all inconsistencies regarding naming convention and value

representations are removed.

• Non-volatile: data is stored in read-only format and do not change over

time.

• Time Variant: data are not current but normally time-series.

• Summarized: operational data are mapped into a decision-usable

format.

• Large Volume: time-series datasets are normally quite large.

• Not Normalized: DW data can, and often is, redundant.

• Metadata: data about data is stored.

• Data Sources: internal and external unintegrated operational systems.

Page 263: Decision Support System

26

3

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

The Data Warehouse (continued)

Page 264: Decision Support System

26

4

Rafi A Khan5/12/2010 10:21:05 PM Rafi A Khan

The Data Warehouse (continued)

Page 265: Decision Support System

26

5

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

OLAP tools

Data Mining Tools

Ad-hoc Queries

Reporting Tools

Monitoring/

Operational

Database(s)

Data

Warehouse

Independent

Data Mart

External

Data

ETL Routine(Extract/Transform/Load)

Dependent

Data Mart

Extract/Summarize Data

Fig. Data warehouse process model

Page 266: Decision Support System

26

6

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan266

Data Warehousing Benefits

Increase in knowledge worker productivity

Supports all decision makers‘ data requirements

Provide ready access to critical data

Insulates operation databases from adhoc processing

Provides high-level summary information

Provides drill down capabilities

Yields

– Improved business knowledge

– Competitive advantage

– Enhances customer service and satisfaction

– Facilitates decision making

– Help streamline business processes

Page 267: Decision Support System

26

7

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

The Data Mart

Data mart

– Small, single-subject data warehouse subset

– Each is more manageable data set than data warehouse

– Provides decision support to small group of people

– Typically lower cost and lower implementation time than data

warehouse

Page 268: Decision Support System

26

8

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Twelve Rules that Define a Data Warehouse

Data warehouse and operational environments are separated

Data warehouse data are integrated

Data warehouse contains historical data over long time horizon

Data warehouse data are snapshot data captured at given point in time

Data warehouse data are subject oriented

Page 269: Decision Support System

26

9

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Twelve Rules that Define a Data Warehouse (continued)

Data warehouse data are mainly read-only with periodic batch updates

from operational data

– No online updates allowed

Data warehouse development life cycle differs from classical systems

development

Data warehouse contains data with several levels of detail: current detail

data, old detail data, lightly summarized data, and highly summarized

data

Data warehouse environment is characterized by read-only transactions to

very large data sets

Page 270: Decision Support System

27

0

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Twelve Rules that Define a Data Warehouse (continued)

Data warehouse environment has system that traces data sources,

transformations, and storage

Data warehouse‘s metadata are critical component of this environment

Data warehouse contains a mechanism for resource usage that enforces

optimal use of data by end users

Page 271: Decision Support System

27

1

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan271

OLAP Activities

Generating queries

Requesting ad hoc reports

Conducting statistical and other analyses

Developing multimedia applications

Page 272: Decision Support System

27

2

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Online Analytical Processing

Advanced data analysis environment that supports decision making,

business modeling, and operations research

OLAP systems share four main characteristics:

– Use multidimensional data analysis techniques

– Provide advanced database support

– Provide easy-to-use end-user interfaces

– Support client/server architecture

Page 273: Decision Support System

27

3

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Multidimensional Data Analysis Techniques

Data are processed and viewed as part of a multidimensional structure

Particularly attractive to business decision makers

Page 274: Decision Support System

27

4

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

OLTP vs OLAP

• Time-critical

• In-place data update

• Current data

• Functional transaction focus

• Store details only

• Only keeps company internal data

• Small delays tolerable

• Append only

• Historical and current data

• Reporting (information

delivery) focus

• Store summary + details

(e.g. counts and aggregates)

• Warehouse also keeps external data

On-Line Transaction Processing On-Line Analytical Processing

Page 275: Decision Support System

27

5

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Multidimensional Data Analysis Techniques

Page 276: Decision Support System

27

6

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Multidimensional Data Analysis Techniques (continued)

Page 277: Decision Support System

27

7

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

OLAP Architecture

Page 278: Decision Support System

27

8

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

OLAP Architecture (continued)

Page 279: Decision Support System

27

9

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

OLAP Architecture (continued)

Designed to use both operational and data warehouse data

Defined as an ―advanced data analysis environment that supports decision making, business modeling, and an operation‘s research activities‖

In most implementations, data warehouse and OLAP are interrelated and complementary environments

Page 280: Decision Support System

28

0

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Multi Dimensional Data

Page 281: Decision Support System

28

1

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Multi Dimensional Data

Page 282: Decision Support System

28

2

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Drill Down & Roll Up

Page 283: Decision Support System

28

3

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Drill Down & Roll Up

Page 284: Decision Support System

28

4

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Fresh Produce

Tinned Food

Toiletries

60

30

50

82

84

15

63

79

46

59

64

73

August

July

Sept

Page 285: Decision Support System

28

5

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Fresh Produce

Tinned Food

Toiletries

60

30

50

82

84

15

63

79

46

59

64

73

August

July

Sept

Page 286: Decision Support System

28

6

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Fruit

Vegetables

Dairy

15

15

August

July

Fresh Produce

Tinned Food

Toiletries

60

30

50

82

84

15

63

79

46

59

64

73

Fresh ProduceSept

30

August

July

Sept

Page 287: Decision Support System

28

7

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Fruit

Vegetables

Dairy

15

15

August

July

Fresh Produce

Tinned Food

Toiletries

60

30

50

82

84

15

63

79

46

59

64

73

Fresh Produce

30

Sept

August

July

Sept

Page 288: Decision Support System

28

8

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Fruit

Vegetables

Dairy

15

15

August

July

Fresh Produce

Tinned Food

Toiletries

60

30

50

82

84

15

63

79

46

59

64

73

Fresh Produce

30

Sept

August

July

Sept

Page 289: Decision Support System

28

9

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Fruit

Vegetables

Dairy

15

15

August

July

Fresh Produce

Tinned Food

Toiletries

60

30

50

82

84

15

63

79

46

59

64

73

Fresh Produce

2

3

2

4

3

1

1

4

3

1

2

4

30

Sept

10-20 sept21-30 sept

1st-10 sept

August

July

Sept

Page 290: Decision Support System

29

0

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Fruit

Vegetables

Dairy

15

15

August

July

Fresh Produce

Tinned Food

Toiletries

60

30

50

82

84

15

63

79

46

59

64

73

Fresh Produce

2

3

2

4

3

1

1

4

3

1

2

4

30

Sept

10-20 sept21-30 sept

1st-10 sept

Apples

Mangoes

Oranges

Fruits

August

July

Sept

Page 291: Decision Support System

29

1

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Some Tools in the Marketplace

• Data Warehousing

• Microsoft SQL Server 2000 Data Transformation Service

• Oracle 9i Warehouse Builder

• IBM Red Brick Warehouse, and DB2

• NCR/Teradata

• SAS Data Warehousing (Warehouse Administrator)

• OLAP

• Cognos PowerPlay

• Business Objects Analytics• Microstrategy 7i

• Microsoft SQL Server 2000 Analysis Service

+ MDX query language for decision support

+ Microsoft Data Analyzer

• Oracle 9i OLAP

Data Warehouse, OLAP, and Data Mining solutions are sometimes listed

under the title ‗Business Intelligence‘ (BI) software.

Page 292: Decision Support System

29

2

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Knowledge Discovery in Databases (KDD)

• Knowledge Discovery in Databases (KDD) is the automated discovery

of patterns and relationships in large databases.

• Knowledge Discovery in Databases (KDD) as it is also known, is the

nontrivial extraction of implicit, previously unknown, and potentially

useful information from data.

• KDD is the search for relationships and global patterns that exist in

large databases but are ̀ hidden' among the vast amount of data, such

as a relationship between patient data and their medical diagnosis.

These relationships represent valuable knowledge about the database

and the objects in the database

Page 293: Decision Support System

29

3

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Knowledge Discovery in Databases (KDD)

Selection:

Extraction of the data from a database that is relevant to the data mining analysis.

Preprocessing:

Ensuring that values have a uniform meaning, eliminating missing values in the data, and inaccurate (inconsistent) data.

Data Transformation:

Converting the data into a two-dimensional table and eliminating unwanted fields so the results are valid.

Data mining:

The extraction of patterns from the data using by an appropriate set of algorithms.

Interpretation:

The transformation of the identified patterns into knowledge

Page 294: Decision Support System

29

4

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

KDD PROCESS

Page 295: Decision Support System

29

5

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Trends leading to Data Flood

More data is generated:

– Bank, telecom, other business

transactions ...

– Scientific data: astronomy,

biology, etc

– Web, text, and e-commerce

Page 296: Decision Support System

29

6

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Big Data Examples

Europe's Very Long Baseline Interferometry (VLBI) has 16 telescopes, each

of which produces 1 Gigabit/second of astronomical data over a 25-day

observation session

– storage and analysis a big problem

AT&T handles billions of calls per day

– so much data, it cannot be all stored -- analysis has to be done ―on the

fly‖, on streaming data

Page 297: Decision Support System

29

7

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Largest databases in 2003

Commercial databases:

– Winter Corp. 2003 Survey: France Telecom has largest decision-support

DB, ~30TB; AT&T ~ 26 TB

Web

– Alexa internet archive: 7 years of data, 500 TB

– Google searches 4+ Billion pages, many hundreds TB

– IBM WebFountain, 160 TB (2003)

– Internet Archive (www.archive.org),~ 300 TB

Page 298: Decision Support System

29

8

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

From terabytes to exabytes to …

UC Berkeley 2003 estimate: 5 exabytes (5 million terabytes) of new data

was created in 2002.

www.sims.berkeley.edu/research/projects/how-much-info-2003/

US produces ~40% of new stored data worldwide

2006 estimate: 161 exabytes (IDC study)

– www.usatoday.com/tech/news/2007-03-05-data_N.htm

2010 projection: 988 exabytes

Page 299: Decision Support System

29

9

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Largest Databases in 2005

Winter Corp. 2005 Commercial Database

Survey:

1. Max Planck Inst. for Meteorology ,

222 TB

2. Yahoo ~ 100 TB (Largest Data

Warehouse)

3. AT&T ~ 94 TB www.wintercorp.com/VLDB/2005_TopTen_Survey/TopTenWinners_2005.asp

Page 300: Decision Support System

30

0

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Growth

In 2 years, the size of the largest database TRIPLED!

Page 301: Decision Support System

30

1

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Growth Rate

Twice as much information was created in 2002 as in 1999 (~30% growth

rate)

Other growth rate estimates even higher

Very little data will ever be looked at by a human

Knowledge Discovery is NEEDED to make sense and use of data.

Page 302: Decision Support System

30

2

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Machine Learning / Data Mining Application areas

Science

– astronomy, bioinformatics, drug discovery, …

Business

– CRM (Customer Relationship management), fraud detection, e-commerce,

manufacturing, sports/entertainment, telecom, targeted marketing, health care,

Web:

– search engines, advertising, web and text mining, …

Government

– surveillance, crime detection, profiling tax cheaters, …

Page 303: Decision Support System

30

3

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Application Areas

What do you think are some of the most important and

widespread business applications of Data Mining?

Page 304: Decision Support System

30

4

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Mining for Customer Modeling

Customer Tasks:

– attrition prediction

– targeted marketing:

• cross-sell, customer acquisition

– credit-risk

– fraud detection

Industries

– banking, telecom, retail sales, …

Page 305: Decision Support System

30

5

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Customer Attrition: Case Study

Situation: Attrition rate at for mobile phone customers is around 25-30% a year!

With this in mind, what is our task?

– Assume we have customer information for the past N months.

Page 306: Decision Support System

30

6

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Customer Attrition: Case Study

Task:

Predict who is likely to attrite next month.

Estimate customer value and what is the cost-effective offer to

be made to this customer.

Page 307: Decision Support System

30

7

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Customer Attrition Results

Verizon Wireless built a customer data warehouse

Identified potential attires

Developed multiple, regional models

Targeted customers with high propensity to accept the offer

Reduced attrition rate from over 2%/month to under 1.5%/month (huge impact, with >30 M subscribers)

(Reported in 2003)

Page 308: Decision Support System

30

8

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Assessing Credit Risk: Case Study

Situation: Person applies for a loan

Task: Should a bank approve the loan?

Note: People who have the best credit don‘t need the loans, and people

with worst credit are not likely to repay. Bank‘s best customers are in the

middle

Page 309: Decision Support System

30

9

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Credit Risk - Results

Banks develop credit models using variety of machine learning methods.

Mortgage and credit card proliferation are the results of being able to

successfully predict if a person is likely to default on a loan

Widely deployed in many countries

Page 310: Decision Support System

31

0

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

e-commerce

A person buys a book (product) at Amazon.com

What is the task?

Page 311: Decision Support System

31

1

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Successful e-commerce – Case Study

Task: Recommend other books (products) this person is likely to buy

Amazon does clustering based on books bought:

– customers who bought ―Advances in Knowledge Discovery and Data

Mining‖, also bought ―Data Mining: Practical Machine Learning

Tools and Techniques with Java Implementations‖

Recommendation program is quite successful

Page 312: Decision Support System

31

2

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Security and Fraud Detection - Case Study

Credit Card Fraud Detection

Detection of Money laundering

– FAIS (US Treasury)

Securities Fraud

– NASDAQ KDD system

Phone fraud

– AT&T, Bell Atlantic, British Telecom/MCI

Bio-terrorism detection at Salt Lake Olympics 2002

Page 313: Decision Support System

31

3

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Mining and Privacy

in 2006, NSA (National Security Agency) was reported to be mining years

of call info, to identify terrorism networks

Social network analysis has a potential to find networks

Invasion of privacy – do you mind if your call information is in a gov

database?

What if NSA program finds one real suspect for 1,000 false leads ?

1,000,000 false leads?

Page 314: Decision Support System

31

5

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Related Fields

Statistics

MachineLearning

Databases

Visualization

Data Mining and

Knowledge Discovery

Page 315: Decision Support System

31

6

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Statistics, Machine Learning andData Mining

Statistics:

– more theory-based

– more focused on testing hypotheses

Machine learning

– more heuristic(experience-based techniques that help in problem solving, learning and discovery)

– focused on improving performance of a learning agent

– also looks at real-time learning and robotics – areas not part of data mining

Data Mining and Knowledge Discovery

– integrates theory and heuristics

– focus on the entire process of knowledge discovery, including data cleaning, learning, and integration and visualization of results

Distinctions are fuzzy

witten&eibe

Page 316: Decision Support System

31

7

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data mining

Many Definitions…

– A short one…

Search for Valuable Information in Large Volumes of Data.

– A long one…

Exploration & Analysis, by Automatic or Semi-Automatic Means,

of Large Quantities of Data in order to Discover Meaningful

Patterns & Rules.

Page 317: Decision Support System

31

8

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Mining

Data Mining is the step in the process of knowledge discovery in

databases, that inputs predominantly cleaned, transformed data, searches

the data using algorithms, and outputs patterns and relationships to the

interpretation/evaluation step of the KDD

Data mining is a process that uses a variety of data analysis tools to

discover patterns and relationships in data that may be used to make valid

predictions.

Page 318: Decision Support System

31

9

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Mining

Data Mining constitutes one step in the KDD process.

The transformed data is used in the data mining step. It is in this step that

the actual search for patterns of interest is performed.

The appropriate data mining algorithm (linear/logistic regression, neural

networks, association rules, etc.) for the data mining task (classification,

database segmentation, rule generation, etc) are applied.

It is necessary to remove redundant and irrelevant patterns from the set of

useful patterns. Once a set of good patterns have been discovered, they

then have to be reported to the end user. This can be done can be done

textually, by way of reports or using visualizations such as graphs,

spreadsheets, diagrams, etc.

Page 319: Decision Support System

32

0

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Mining

Data mining tools do the following:

– Analyze data

– Uncover problems or opportunities hidden in data relationships

– Form computer models based on their findings

– Use models to predict business behavior

Require minimal end-user intervention

Page 320: Decision Support System

32

1

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Mining (continued)

Page 321: Decision Support System

32

2

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Major Data Mining Tasks

Classification: predicting an item class

Clustering: finding clusters in data

Associations: e.g. A & B & C occur frequently

Visualization: to facilitate human discovery

Summarization: describing a group

Deviation Detection: finding changes

Estimation: predicting a continuous value

Link Analysis: finding relationships

Page 322: Decision Support System

32

3

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Mining Tasks: Classification

Learn a method for predicting the instance class from pre-labeled (classified) instances

Many approaches:

Statistics,

Decision Trees,

Neural Networks,

...

Page 323: Decision Support System

32

4

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Mining Tasks: Clustering

Find “natural” grouping of instances given un-labeled data

Page 324: Decision Support System

32

5

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Mining (continued)

Page 325: Decision Support System

32

6

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

We want to know ...

Given a database of 100,000 names, which persons are the least likely to

default on their credit cards?

Which types of transactions are likely to be fraudulent given the

demographics and transactional history of a particular customer?

If I raise the price of my product by Rs. 2, what is the effect on my ROI?

If I offer only 2,500 airline miles as an incentive to purchase rather than

5,000, how many lost responses will result?

If I emphasize ease-of-use of the product as opposed to its technical

capabilities, what will be the net effect on my revenues?

Which of my customers are likely to be the most loyal? Data Mining helps extract such information

Page 326: Decision Support System

32

7

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan327

Major Data Mining Characteristics and Objectives

Data are often buried deep

Client/server architecture

Sophisticated new tools--including advanced visualization tools

End-user miner empowered by data drills and other power query tools with

little or no programming skills

Often involves finding unexpected results

Tools are easily combined with spreadsheets, etc.

Parallel processing for data mining

Page 327: Decision Support System

32

8

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Difference between OLAP & Data Minig

OLAP is part of the spectrum of decision support tools. Traditional query and

report tools describe what is in a database. OLAP goes further; it‘s used to

answer why certain things are true. The user forms a hypothesis about a

relationship and verifies it with a series of queries against the data. For

example, an analyst might want to determine the factors that lead to loan

defaults. He or she might initially hypothesize that people with low

incomes are bad credit risks and analyze the database with OLAP to verify

(or disprove) this assumption. If that hypothesis were not borne out by the

data, the analyst might then look at high debt as the determinant of risk. If

the data did not support this guess either, he or she might then try debt

and income together as the best predictor of bad credit risks.

Page 328: Decision Support System

32

9

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

OLAP analysis is essentially a deductive process.

The OLAP analyst generates a series of hypothetical patterns and

relationships and uses queries against the database to verify them or disprove

them.

It becomes much more difficult and time-consuming to find a good

hypothesis, when the number of variables being analyzed is in the dozens or

even hundreds? and analyze the database with OLAP to verify or disprove it.

Difference between OLAP & Dataminig

Page 329: Decision Support System

33

0

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data mining , rather than verify hypothetical patterns, it uses the data itself to

uncover such patterns. It is essentially an inductive process.

For example, suppose the analyst who wanted to identify the risk factors for

loan default were to use a data mining tool. The data mining tool might

discover that people with high debt and low incomes were bad credit risks (as

above), but it might go further and also discover a pattern the analyst did not

think to try, such as that age is also a determinant of risk.

Difference between OLAP & Dataminig

Page 330: Decision Support System

33

1

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data mining Applications

Many organizations are using data mining to help manage all phases of the

customer life cycle, including acquiring new customers, increasing revenue

from existing customers, and retaining good customers.

Telecommunications and credit card companies are two of the leaders in

applying data mining to detect fraudulent use of their services.

Insurance companies and stock exchanges are also interested in applying

this technology to reduce fraud.

Page 331: Decision Support System

33

2

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Retail/Marketing

Identify buying patterns from customers

Find associations among customer demographic characteristics

Predict response to mailing campaigns

Market basket analysis

Retailers are making more use of data mining to decide which products to

stock in particular stores (and even how to place them within a store), as

well as to assess the effectiveness of promotions and coupons.

Data mining Applications

Page 332: Decision Support System

33

3

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Medicine

Characterise patient behaviour to predict office visits

Identify successful medical therapies for different illnesses

Medical applications are another fruitful area: data mining can be used to

predict the effectiveness of surgical procedures, medical tests or

medications.

Pharmaceutical firms are mining large databases of chemical compounds

and of genetic material to discover substances that might be candidates for

development as agents for the treatments of disease.

Data mining Applications

Page 333: Decision Support System

33

4

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Banking

Detect patterns of fraudulent credit card use

Identify `loyal' customers

Predict customers likely to change their credit card affiliation

Determine credit card spending by customer groups

Find hidden correlations between different financial indicators

Identify stock trading rules from historical market data

Companies active in the financial markets use data mining to determine

market and industry characteristics as well as to predict individual

company and stock performance.

Data mining Applications

Page 334: Decision Support System

33

5

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Insurance and Health Care

Claims analysis - i.e which medical procedures are claimed together

Predict which customers will buy new policies

Identify behaviour patterns of risky customers

Identify fraudulent behaviour

Transportation

Determine the distribution schedules among outlets

Analyse loading patterns

Analyse loading patterns

Data mining Applications

Page 335: Decision Support System

33

6

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Mining (continued)

Page 336: Decision Support System

33

7

Rafi A Khan5/12/2010 10:21:06 PM Rafi A Khan

Data Mining (continued)

Page 337: Decision Support System

33

8

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Page 338: Decision Support System

33

9

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Systems Development Life Cycle

Four phases

– Planning

– Analysis

– Design

– Implementation

Cyclical

Can return to other phases

Waterfall model

Page 339: Decision Support System

34

0

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Tools

Computer-aided software design tools

– CASE tools- Oracle 9i developer suite, Rational rose, Paradigm Plus

RAD design tools- Sybase power Designer. Oracle Internet Development Suite, Rational RequisitePro

Code debugging methods -

Testing and quality assurance tools - Red Views WebLoad, Load Runner, Rational RequisitePro, SilkPerformer

Page 340: Decision Support System

34

1

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Successful Project Management

Define requirements

Manage change

Get support from upper management

Establish timelines, milestones, and budgets based on realistic

goals

Involve users

Document everything

Page 341: Decision Support System

34

2

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Implementation Failures

Lack of stakeholder involvement

Incomplete requirements

Unrealistic expectations

Project champion leaves

Lack of skill or expertise

Inadequate human resources

New technologies

Page 342: Decision Support System

34

3

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Project Management Tools

Project management software can allow:

– Collaboration among disparate teams

– Resource and program management

– Portfolio management

– Web enabled

– Analyses of project data

S/W Examples : Microsoft project, PlanView, ActiveProject

Page 343: Decision Support System

34

4

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Alternative Development Methodologies

Parallel Development

– Multiple development on separate systems (Design & Implementation Phases)

– Database, Model base, UI and Knowledge can be developed in parallel

RAD

– Quick development allowing fast, but limited functionality

– Methods of RAD

• Phased development

– Sequential serial development (Break system into Pieces)

• Prototyping ( Analysis, Design & Implementation repeatedly)

– Rapid development of portions of projects for user input and modification

– Small working model or may become functional part of final system

• Throwaway prototyping

– Pilot test or simple development platforms

Page 344: Decision Support System

34

5

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Page 345: Decision Support System

34

6

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Page 346: Decision Support System

34

7

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Tools

Computer-aided software design tools

– CASE tools - Oracle 9i developer suite, Rational rose, Paradigm Plus

RAD design tools- Sybase power Designer. Oracle Internet Development

Suite, Rational RequisitePro

Code debugging methods -

Testing and quality assurance tools - Red Views WebLoad, Load Runner,

Rational RequisitePro, SilkPerformer

Page 347: Decision Support System

34

8

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

DSS Prototyping

Short steps

– Planning

– Analysis

– Design

– Prototype

Immediate user feedback

Iterative

– In development of prototype

Page 348: Decision Support System

34

9

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Successful Project Management

Define requirements

Manage change

Get support from upper management

Establish timelines, milestones, and budgets based on realistic

goals

Involve users

Document everything

Page 349: Decision Support System

35

0

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Implementation Failures

Lack of stakeholder involvement

Incomplete requirements

Unrealistic expectations

Project champion leaves

Lack of skill or expertise

Inadequate human resources

New technologies

Page 350: Decision Support System

35

1

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Project Management Tools

Project management software can allow:

– Collaboration among disparate teams

– Resource and program management

– Portfolio management

– Web enabled

– Analyses of project data

S/W Examples : Microsoft project, PlanView, ActiveProject

Page 351: Decision Support System

35

2

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Agile Development

Rapid prototyping used for rapidly changing requirements

Used for:

– Unclear or rapidly changing requirements

– Speedy development

Heavy user input

Incremental delivery with short time frames

Tend to have integration problems

Example : Extreme Programming (XP), Scrum, Crystal.

Page 352: Decision Support System

35

3

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

DSS Prototyping

Advantages

– User and management involvement

– Short user-reaction time(Feedback from user)

– Short intervals between iterations

– Low cost & Short development time

– Improved user understanding of system

Disadvantages

– Changing requirements

– May not have thorough understanding of benefits and costs

– Poorly tested

– Dependencies, security, and safety may be ignored

– High uncertainty

– Problem may get lost

– Reduction in quality

– Higher costs due to multiple productions

Page 353: Decision Support System

35

4

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

Change Management

Crucial to DSS

People resistant to change

Examine cause of change

May require organizational culture shift

Lewin-Schein change theory steps

– Unfreeze

• Create awareness of need for change

• People support what they help create

– Move

• Develop new methods and behaviors

• Create and maintain momentum

– Refreeze

• Reinforce desired changes

• Establish stable environment

Page 354: Decision Support System

35

5

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

DSS Technology Levels

DSS primary tools

–Fundamental elements

•Programming languages, graphics, editors, query systems

DSS generator (engine)

–Integrated software package for building specific DSS

•Modeling, report generation, graphics, risk analysis

•These range from spreadsheets such as Excel—perhaps with some add-ins or a more sophisticated generator such as MicroStrategy‘s DSS Architect.

Specific DSS

–For some problem types there may be a commercially available package that can be acquired and customized

DSS primary tools are used to construct integrated tools that are used to construct specific tools

Page 355: Decision Support System

35

6

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

DSS

Hardware

– PCs to multiprocessor mainframes

Software

– Involves multiple criteria

– Develop in house, outsource, or buy off the shelf

– Off the shelf software rapidly updated; many on market

– Prices fluctuate

– Different tools available

Page 356: Decision Support System

35

7

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

DSS Team developed DSS requires substantial effort to build and

manage

End user developed DSS

– Decision-makers and knowledge workers develop to solve problems or enhance productivity

• Advantages

– Short delivery time

– User requirements specifications are eliminated

– Reduced implementation problems

– Low costs

• Risks

– Quality may be low

– May have lack of documentation

– Security risks may increase

Page 357: Decision Support System

35

8

Rafi A KhanFriday, July 18, 2008 Rafi A. Khan

DSS

Microstrategy 8

Hyperion System 9 BI+

Business Object XI

Microsoft Biztalk server2004

IBM Websphere Commerce Suite

Oracle Daily Business Intelligence(DBI)