Unit 1 DSS

Decision Support Systems

A brief history

• Academic Researchers from many disciplines has been studying DSS for approximately 40 years.

• According to Keen and Scott Morton (1978), the concept of decision support has evolved from two main areas of research: the theoretical studies of organizational decision making done at the Carnegie Institute of Technology during the late 1950s and early 1960s, and the technical work on interactive computer systems, mainly carried out at the Massachusetts Institute of Technology in the 1960s.

• It is considered that the concept of DSS became an area of research of its own in the middle of the 1970s, before gaining in intensity during the 1980s.

A brief history

• In the middle and late 1980s, Executive Information Systems (EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS) evolved from the single user and model-oriented DSS.

• Beginning in about 1990, data warehousing and on-line analytical processing (OLAP) began broadening the realm of DSS.

• As the turn of the millennium approached, new Web-based analytical applications were introduced.

Decision Support System

• A Decision Support System (DSS) is an interactive computer-based system or subsystem intended to help decision makers use communications technologies, data, documents, knowledge and/or models to identify and solve problems, complete decision process tasks, and make decisions.

• Decision Support System is a general term for any computer application that enhances a person or group’s ability to make decisions.

Decision Making as a Component of Problem Solving

Intelligence

Design

Choice

Implementation

Monitoring

Problem

solving

Decision

making

Problem Solving Factors

• Multiple decision objectives

• Increased alternatives

• Increased competition

• The need for creativity

• Social and political actions

• International aspects

• Technology

• Time compression

Decision Making/ Problem Solving Systems

Target problem

Information systems

Decision

makers

external

Info

goals

reports

queries

info

DATA

DECISIONS

Characteristics of a DSS (1)

• Handles large amounts of data from different sources

• Provides report and presentation flexibility

• Offers both textual and graphical orientation


• Supports drill down analysis

• Performs complex, sophisticated analysis and comparisons using advanced software packages

• Supports optimization, satisfying, and heuristic approaches


• Performs different types of analyses

– “What-if” analysis

• Makes hypothetical changes to problem and observes impact on the results

– Simulation

• Duplicates features of a real system

– Goal-seeking analysis

• Determines problem data required for a given result

Decision Support SystemsA decision support system (DSS) is an organized collection of

people, procedures, software, databases, and devices that

support problem-specific decision making.

The focus of a DSS is on making effective decisions.

A DSS helps a manager “do the right thing.”

A DSS can include a collection of models used to support a decision

maker or user (model base), a collection of facts and information to

assist in decision making (database), and systems and procedures

(user interface or dialogue manager) that help decision makers and

other users interact with the DSS

Types of Decision Support Systems

1) File Drawer System – It allows immediate access todata items. They are basically online computerized versionsof manual filing systems such as status enquiries ofinventory, sales etc. These enquiries are made on irregularbasis.

2) Data Analysis Systems – It allows manipulation of databy means of either analysis operations tailored to the taskand setting or general analysis operations. These systemsare used at lower level of the organization to analyze filescontaining current or historical data. For example: budgetanalysis systems or financial systems for analyzingalternative investment opportunities. The data usagepattern is in the form of data manipulation and datadisplay.


3) Analysis Information Systems - It provides access to a series ofdatabases and small models. For example – in a marketingsupport system, internal sales data, promotional data, price dataand external databases are used for pricing decisions. Data areprovided on requests which are made on irregular basis. The datausage pattern is in the form of generation of special reports andsmall decision models.

4) Accounting Models – It calculates the consequences of plannedactions on the basis of accounting definitions. They typicallygenerate estimates of income, projected profit and loss accountand balance sheet etc. based on variation in input values to thedefinitional formulae. Examples of accounting models arebudgeting systems and other short term planning tools.


5) Representational Model- It estimates the consequences ofactions on the basis of models that represent some non-definitional characteristics of the system such as probabilities ofoccurrence of events. They include all simulation models thatcontain elements beyond accounting definitions. For example- riskanalysis model using estimated probability distributions for eachof the key factors.

6) Optimization Models – It provides guidelines for action bygenerating optimal solution consistent with a series ofconstraints. These are used for decisions that can be describedmathematically and where a specific objective such asminimization of cost / maximization of revenue can be used bystaff analysts for planning resource allocation to differentalternatives.


7) Suggestion Models- It computes specificsuggested decision for a comparativelystructured decision. The objective is tobypass other procedures for generatingsuggestion for example – product pricingbased on a standard set of dimensions.

Database Model base

External database

access

Access to the

internet, networks,

and other computer

systems

Dialogue manager

DBMS MMS

External

databases

Components of Decision Support Systems

Model Base

• Model Base

– Provides decision makers with access to a variety of models and assists them in decision making

• Models

– Financial models

– Statistical analysis models

– Graphical models

– Project management models

Advantages and Disadvantagesof Modeling

– Advantages• Less expensive than custom approaches or real systems.

• Faster to construct than real systems

• Less risky than real systems

• Provides learning experience (trial and error)

• Future projections are possible

• Can test assumptions

– Disadvantages• Assumptions about reality may be incorrect

• Accuracy of predications often unreliable

• Requires abstract thinking

Decision Room

• Decision Room

– For decision makers located in the same geographic area or building

– Use of computing devices, special software, networking capabilities, display equipment, and a session leader

– Collect, coordinate, and feed back organized information to help a group make a decision

– Combines face-to-face verbal interaction with technology-aided formalization

Essential steps in the process of making a decision

Step 1 Concept of Project is Identified

Project assessment. Taking

account of all issues involved

Operation Starts

Project Goes to Detail

Specification For Tender

Tender Accepted. Construction

Starts

Step 2

Step 3

Step 4

Step 5

Decision To Proceed Decision To Abandon





Step 1

• The conceptual need for a project arise mainly as a result of an basement of future requirements.

• It may be made by a team of experts.

• Typically a conceptual study will identify the technical solution required, the economic merits, and acceptability of project in socio political terms.

• It may require discussion with financial institutions wither or not they will provide necessary funds.

Step 2

• Assuming the decision has been made to develop the project further then a detailed assessment will have to be made of all technical, economic and socio-political factors.

• The details may be quantitative and based on subjective knowledge.

• A major decision making is about novelty of project.– A project may technically be novel ( making a new airplane ).

– The project may employ an established technology in novel environment ( using electrical train in third world country).

• In this step the degree of uncertainty associated with each factor will begin to emerge.

• An understanding of uncertainty associated with any proposal is essential for a feasible decision making.

Step 3

• If the outcome of step 2 is to proceed the project, then a tender specification has to be prepared.

• It should define, exactly what work the tender is required to do. Ideally it has to define every thing that has to be done.

• The magnitude of uncertainty associated with this stage is a reason for possible variations in cost and duration of projects.

• Before a tender specification is issued it is prudent to confirm that the project is acceptable to regulatory authorities and that the adequate finance is available.

• The financer need to be convinced that the project is viable, that the proposer is sound and has the experience and capability to derive the project to a successful conclusion.

Step 4 ,5

• Step 4– The first action is to decide if one of the tender should be

accepted.

– The tenderer should have the appropriate experience, capability and adequate financial resources.

• Step 5

– Assuming all steps completed satisfactorily, a decision has to be taken to start the project.

– Even if the project starts, it might have to be stopped if the environment it operates is changed.

Types of Problems

• Structured: situations where the procedures to follow when a decision is needed can be specified in advance– Repetitive– Standard solution methods exist– Complete automation may be feasible

• Unstructured: decision situations where it is not possible to specify in advance most of the decision procedures to follow– One-time– No standard solutions– Rely on judgment– Automation is usually infeasible

• Semi-structured: decision procedures that can be pre specified, but not enough to lead to a definite recommended decision– Some elements and/or phases of decision making process have repetitive elements

DSS most useful for repetitive aspects of semi-structured problems

Typical Architecture

• TPS: transaction processing system

• MODEL: representation of a problem

• OLAP: on-line analytical processing

• USER INTERFACE: how user enters problem & receives answers

• DSS DATABASE: current data from applications or groups

• DATA MINING: technology for finding relationships in large data bases for prediction

TPSEXTERNAL

DATADSS DATA

BASE

DSS SOFTWARE SYSTEM

MODELS

OLAP TOOLS

DATA MINING TOOLS

USER

INTERFACE

USER

DSS Model base

• Model base

– A software component that consists of models used in computational and analytical routines that mathematically express relations among variables

• Examples:

– Linear programming models,

– Multiple regression forecasting models

– Capital budgeting present value models

Definitions

• DBMS - System for storing and retrieving data and processing queries• Data warehouse - Consolidated database, usually gathered from

multiple primary sources, organized and optimized for reporting and analysis

• MIS - System to provide managers with summaries of decision-relevant information

• Expert system - computerized system that exhibits expert-like behavior in a given problem domain

• Decision aid - automated support to help users conform to some normative ideal of rational decision making

• DSS - provide automated support for any or all aspects of the decision making process

• EIS (Executive information system) - A kind of DSS specialized to the needs of top executives

Management Information Systems

• MIS

• Produces information products that support many of the day-to-day decision-making needs of managers and business professionals

• Prespecified reports, displays and responses

• Support more structured decisions

MIS Reporting Alternatives

• Periodic Scheduled Reports– Prespecified format on a regular basis

• Exception Reports– Reports about exceptional conditions

– May be produced regularly or when exception occurs

• Demand Reports and Responses– Information available when demanded

• Push Reporting– Information pushed to manager

Online Analytical Processing

• OLAP

– Enables mangers and analysts to examine and manipulate large amounts of detailed and consolidated data from many perspectives

– Done interactively in real time with rapid response

OLAP Analytical Operations

• Consolidation

– Aggregation of data

• Drill-down

– Display detail data that comprise consolidated data

• Slicing and Dicing

– Ability to look at the database from different viewpoints

Geographic Information Systems

• GIS

– DSS that uses geographic databases to construct and display maps and other graphics displays

– That support decisions affecting the geographic distribution of people and other resources

– Often used with Global Position Systems (GPS) devices

Group Decision Support

Systems and Groupware

Technologies

Introduction

• In the early phase of development of DSS, the focus wason supporting individual decision making.

• However DSS software developers and scholars realizedthat most of the time, important decisions were made bya group consisting of many decision makers.

• Therefore they focused their attention to developsystems that would help group decision making. Suchsystems were named Group Decision support System(GDSS).

• GDSS became more popular because of earlier popularityof the term DSS as applicable for individual decisionmaking. The software that was developed for GDSS wastermed as Groupware.

GDSS

• A GDSS can be defined as a computer – basedsystem that supports a group of decisionmakers engaged in a common task and thatprovides interface to a shared environment.

• DeSanctis and Gallupe defined GDSS as :

“A GDSS is an interactive computer-basedsystem top facilitate the solution ofunstructured problems by a set of decisionmakers working together as a group”.

Features of GDSS1) There is a high level of interaction among decision

makers who work collectively on a problem. Thisinteraction is generally through computer system.The interaction is open and takes place incollaborative meeting atmosphere in whichattendees from various organizational levels feelfreedom to contribute positively to solve theproblem.

2) Emphasis is put on criticism-free idea generation,creating an atmosphere where an idea will beevaluated on its merits rather than on the basis ofthe source of idea.

Features of GDSS3) Priorities are set and decisions are made which

require finding ways to encompass the thinkingof all the members in making these decisions.

4) Each member of the decision-making group hasaccess to relevant internal and externalinformation which allows the members toemphasize their own views, appreciate theviews of others and settle their differences inorder to arrive at an acceptable decision withina given time frame.

Features of GDSS5) Information about the problem on which a

group is working is stored so that those whofail to attend meeting can work on theproblem. There are many problems on whichpeople located at different places may workcollectively and they need to understand thecontent of a meeting at only one of theaffected sites.

Components of GDSS

The principle components of GDSS consists of the following:

1) Decision makers

2) Database and model base

3) Groupware

Components of GDSS

Individual Decision Maker

Report Writing

Software

Model Base

Group of Decision

Makers

Groupware

Database

1)Decision MakersA GDSS has a number of decision makers whowork collectively on a specific problem with theobjective that the problem will be solved by thecollective wisdom of all the decision makers in agroup rather than on the basis of theircontributions taken individually. Each of thesedecision makers has access to database includingmodel base from which each o them can extractrelevant data. Each decision makers has to workon his own ideas as well as on ideas suggested byothers so that the final decision is acceptable tovirtually all decision makers collectively.

Components of GDSS

2)Database and Model Base

Like DSS, a GDSS also uses database andmodel base to extract relevant data andmodels for analyzing these data in order toarrive at a decision. While model base forboth DSS and GDSS may remain the same asboth use almost similar models in decisionmaking, there may be difference in theorganization of database in some cases.

Components of GDSS

3)GroupwareSoftware that is used in GDSS is generally calledas Groupware. While DSS software has beendeveloped on the basis of strong theoreticalframework, groupware has no such base as yet.In 1988, Lotus Development Corporationdeveloped a groupware known as Notes, keepingin view the role of communication in GDSS. Notesincluded a number of features, such as electronicmail, FAX, voice messaging, Internet access,bulletin board system, personal calendaring,group calendaring, video conferencing, taskmanagement, workflow routing, and groupdocuments.

Components of GDSS

3)Groupware

Groupware can be classified into four categories:

1)Brainstorming Software – Brainstormingsoftware supports the definition phase of aproblem by identifying its components. Membersof the decision –making group generate theirown ideas, exchange their ideas with others andevaluate those ideas. The output of this exerciseis a structured report containing the pros andcons of various ideas and how they are relevantfor problem solution.

Components of GDSS

3)Groupware


1)Alternative Rating and Ranking Software –Decision makers use a variety of alternatives andrate them on the basis of certain criteria or rankthem on a basis of these criteria. Alternativerating and ranking software undertakes theevaluation work and combines variousalternatives in the form of a table or graph. Thesoftware supports design phase of decisionmaking by providing means of identifying andevaluating alternative solutions.

Components of GDSS

3)Groupware


2)Consensus Building Software – Consensusbuilding is necessary in group decision making sothat deadlock is broken and members arrive at anagreed decision. It informs the decision makersabut the degree of uniformity in their alternativesolutions. When there is no consensus, thedecision makers can engage in further discussion.The software points out the issues on whichdecision makers disagree and support them tohave a common decision.

Components of GDSS

3)Groupware

Groupware can be classified into fourcategories:

3)Group Authoring and Outlining Software –This software enables various decsion makersto create an outline of a written sections ormaking suggestions to sections written byothers.

Components of GDSS

• Individual Decision Making• Decision making without a group's input or a decision made

regardless of the group's opinion is, naturally, an individual decision. This is the more traditional decision making approach and can work effectively for a manager when the group's input is not required or in certain cases, desired.

• Group Decision Making• There are several models of group decision making that you

can put to use. Two examples are consensus and consultation. Consensus decision making involves posing several options to the group and using the most popular option to make a decision. Consultation takes the opinions of the group into consideration when making a decision. Both methods require the group's participation and call for a manager who respects the opinions and input of the group in the decision making process.

Individual vs. Group Decision Making

Analysis of situations for individual & Group

1) Nature of Problem : If the policy guidelinesregarding the decision for the problem at hand areprovided, individual decision making will result ingreatest creativity as well as efficiency. Where theproblem requires variety of expertise, groupdecision making is suitable.

2) Time Availability : Group decision making is timeconsuming process & therefore when time at thedisposal is sufficient group decision making can bepreferred.


3) Quality of Decision : Group decision makinggenerally leads to higher quality solution unless anindividual has expertise in the decision area and hasbeen identified in advance.

4) Climate of Decision Making : Supportive climateencourages group problem solving whereascompetitive climate stimulates individual problemsolving.

5) Legal Requirement : It also determines whetherindividual or group decision have to be made. Suchrequirement may be prescribed by government’slegal framework or by the organizational policy,rules etc.


Positive Aspects of Group Decision Making

1) Pooling of Knowledge and Information

2) Satisfaction and Commitment

3) Personnel Development

4) More Risk Taking : Every decision involvessome kind of risk because a decision affectsthe future events and individuals vary interms of risk taking aptitudes andcapabilities.

Negative Aspects of Group Decision Making

1) Time - consuming and Costly

2) Individual Domination

3) Problem of Responsibility

4) Groupthink : It is a type of thinking thatoccurs when reaching agreement becomesmore important to group members thanarriving at a sound decision.

Techniques of Group Decision Making

1) Brainstorming

2) Nominal Group Technique : It is a structured groupmeeting which restricts verbal communication amongmembers during the decision making process.

3) Delphi Technique : In this members do not have factto face interaction for group decision. The decision isarrived at through written communication in the formof filling up questionnaires often through mails.

4) Consensus Mapping : It tries to pool the ideasgenerated by several task subgroups to arrive at adecision.

Improving Group Decision Making with GDSS

1) Idea Generation

2) Enhanced Participation

3) Improved Idea Evaluation

4) Preservation of Organizational Memory

EXPERT SYSTEM

Introduction• Artificial Intelligence – It is the effort to develop

computer – based systems that can behave like humans,with the ability to learn languages, accomplish physicaltasks, use a perceptual apparatus, and emulate (follow)human experience and decision making.

• Expert Systems have occupied the prime place in artificialintelligence so far though other techniques are catchingup fast. An expert system is a knowledge - based systemthat uses rules to express logic of the problem beingsolved. In doing so, the expert system imitates thehuman knowledge in limited domain.

Laudon and Laudon defined an expert system as follows:

“An Expert System is a knowledge – intensive programthat solves a problem by capturing the expertise of ahuman in limited domains of knowledge andexperience”.

Features of Expert System1) It performs some of the problem solving work of

human by going through logical reasoning whichinvolves drawing inferences about a problem step bystep.

2) It represents knowledge in the form of rules orframes. These rules or frames are used in drawinginferences.

3) It considers multiple hypotheses simultaneously indrawing inferences. A hypotheses is a conjecturalstatement of the relation between two or morevariables. Since a problem may have a number ofvariables operating simultaneously, there are numberof hypotheses involved in a problem.

Features of Expert System

4) It is not a generalized expert or problemsolver. It typically performs very limited tasksquickly that can be performed byprofessional experts taking much more time.

Components of Expert System

An expert System consists of four components:

1) User Interface

2) Knowledge Base

3) Inference Engine

4) Development Engine

User Interface

Inference Engine

Knowledge Programmer

Problem Domain

Knowledge base

Development Engine

Components of Expert SystemUser

Instructions/Information

Solutions/ Explanations

Knowledge


User Interface

User Interface with the expert system enables the user to enter the

instructions / information in the system and to get solutions/

explanations from the system. The instructions specify the parameters

that guide the expert system through its reasoning process. Thus, user

interface is concerned with expert system inputs and expert system

outputs.

Expert System Inputs

The expert system user interface is designed to facilitate a two-way

dialogue between the user and the system. The system displays

information on the computer screen a the user enters instructions

using keyboard, mouse or other pointing devices.


Expert System Outputs

These are designed to recommend solutions supplemented by

explanations. There are two types of explanations – explanation of

question and explanation of problem solutions. Explanation of

question involves explaining why a particular piece of information is

needed to solve the problem.

Explanation of problem solution involves explaining how the solution

has been arrived at by displaying the various steps involved in the

problem solution.

Components of Expert SystemThe Knowledge Base

The knowledge base stores all the facts and rules about a particular

problem domain. It makes these available to the inference engine in a

form that it can use. The facts may be in the form of background

information built into the system or facts that are input by the user

during a consultation. The rules include both the production rules that

apply to the domain of the expert system and the heuristics or rules-

of-thumb that are provided by the domain expert in order to make the

system find solutions more efficiently by taking short cuts.

Components of Expert SystemThe Shell or Inference Engine

The inference engine is the program that locates the appropriate

knowledge in the knowledge base, and infers new knowledge by

applying logical processing and problem-solving strategies.

An expert system can use 2 different methods of inferencing -

Forward Chaining and Backward Chaining.

A Backward Chaining system (a goal driven system) works with the

system assuming a hypothesis of what the likely outcome will be, and

the system then works backwards to collect the evidence that would

support this conclusion. Expert systems used for planning often use

backward chaining.

A Forward Chaining expert system (a data driven system) simply

gathers facts (like a detective at the scene of a crime) until enough

evidence is collected that points to an outcome. Forward chaining is

often used in expert systems for diagnosis, advise and classification,

although the size and complexity of the system can play a part in

deciding which method of inferencing to use.

Components of Expert SystemDevelopment Engine

It is used to create an expert system. Development engine is used

primarily for developing rules and set of rules either using

programming languages.

Developing An Expert System1) Identification of Problem

2) Creation of Development Team

3) Specification of Rule Set

4) Development of Prototype (Sample/ Model) - Herea model is designed which is converted into anoperational prototype after necessary refinements.A working prototype is designed for the expertsystem incorporating various rules in a network.

5) Testing and Refining Prototype

Advantages of Expert System1) Provides more alternatives

2) Provides higher level logic for decision making

3) Consistent decisions are there as the decisions are

based on rules.

4) Save users time

Disadvantages of Expert System1) Limited to certain problems of classification in

which few alternative outcomes are known in

advance

2) Lack of human knowledge replication

3) Unsuitable to complex managerial problems

4) Costly

Unit 1 DSS

Education

concept of decision

concept of dss

realm of dss

modeloriented dss

interactive computer

data warehousing

collection of models

single user