INTELLIGENT DECISION SUPPORT FOR EVALUATING

AND SELECTING INFORMATION SYSTEMS PROJECTS

By. Hepu Deng And Santoso Wibowo (2008)

Anita Carollin

TIBS 122121805/RBS 0874078

INTRODUCTION

IS

• The availability of numerous IS projects

• The increasing complexities IS projects, and the pressure to make timely decisions in a dynamic environment further complicate the IS project evaluation and selection process.

MA

• MA refers to selecting or ranking alternative(s) from available alternatives with respect to multiple, usually conflicting criteria

• MA methodology is well suited for evaluating the overall suitability of individual IS projects in an organization.

IDSS

• Facilitating the process of selecting the appropriate MultiCriteria Analysis method in a specific IS project evaluation and selection

• As a result, effective decisions can be made for solving the IS project evaluation and selection problem

IDSS LIMITATIONS AND SOLUTIONS

The inadequacy in addressing both the characteristics of the problem and the requirements of the decision maker

The lack of flexibility and interactivity required by the decision maker to address a wide range of decision making situations

The lack of capability to match the most appropriate MA method with the problem involved

Matching the nature of the problem with the requirements of the decision maker

Facilitating the adoption of the most appropriate MA method for a specific IS project selection situation

Giving the control of the method selection process to the DSS

LIMITATIONS SOLUTIONS

IS PROJECTS SELECTION PROBLEM

DECISION MAKER NEEDS TO SELECT THE

MOST APPROPRIATE IS PROJECT

Evaluate the performance of all the available IS projects

Assess the relative importance of the selection criteria

Aggregate the assessments for producing an overall

performance index value for each available IS project

CHARACTERISTICS OF A SPECIFIC IS PROJECT

EVALUATION AND SELECTION PROBLEM

The specific expectation and requirements of the

decision maker involved

The characteristics of the problem under consideration

The characteristics of different MA methods available for

solving the problem

Select the familiar not

the most appropriate method will

result ad hoc decision

A systematic framework

is required for solving

the IS project selection problem

IDSS FRAMEWORK

The DSS is designed to help the decision maker choose the appropriate IS

project in a flexible and user-friendly manner by allowing the decision maker

requirements and to fully explore the relationships between the criteria, the

alternatives, the methods available and the outcome of the selection process.

The problem-oriented approach is vital for effectively and efficiently solving

the IS project evaluation and selection problem in an organization.

IDSS FRAMEWORK

THREE MAJOR SUBSYSTEMS OF DSS:

• Serves to integrate various other subsystems as well as to be responsible for user-friendly communications between the DSS and the decision maker.

The Dialogue Subsystem

• Oganizes and manages all the inputs for solving the IS project evaluation and selection problem.

• This input data can be classified into: Primary (the alternatives, the criteria, the decision matrix, and the pairwise comparison matrices) and Secondary (the criteria weightings)

The Input Management Subsystem

• Consistent with the general architecture of DSS

• Manages all the MA methods available in the DSS

The Knowledge Management Subsystem

THE SIX MA METHOD

THE SIMPLE ADDITIVE WEIGHTING (SAW) METHOD

THE TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION (TOPSIS) METHOD

THE ELIMINATION ET CHOICE TRANSLATION REALITY (ELECTRE) METHOD

THE ANALYTICAL HIERARCHY PROCESS (AHP) METHOD

FUZZY METHOD

FUZZY MA METHOD

One of these MA methods can be invoked directly by the decision maker or selected

automatically by the proposed DSS through the knowledge management subsystem

SIX PHASES OF PROPOSED DSS

1. Identification of The Decision Maker’s Requirements,

2. Determination of Criteria Weights,

3. Determination of Performance Ratings of Alternative IS Projects

With Respect to Each Criterion,

4. Selection of The Most Appropriate MA Method,

5. Evaluation of The IS Project, And

6. Selection of The Appropriate IS Project Alternative

DSS FRAMEWORK FOR SELECTING IS PROJECTS

THREE MODES OF GUIDANCES FOR DECISIONS MAKER

A Novice Mode:

Designed for decision maker who is totally unfamiliar with the MA methodology. The system recommends the most suitable method for application.

An Intermediate Mode:

Used when the decision maker has the knowledge of the various inputs and data and would like to know the available methods that could make use of these inputs.

It is activated after all the available inputs were entered and the knowledge management subsystem will search for the methods that match these inputs.

An Advanced Mode:

Used when the decision maker is highly familiar with various MA methods and he/she is capable of selecting a specific method.

IDSS RULES

Each rule takes the form of:

IF <requirement>

Describes: the requirements of the decision makers and the

characteristics of the IS project evaluation and selection problem.

THEN <outcome>

Represents the most suitable MA method.

With the development of the knowledge base, the DSS becomes

intelligent in the process of selecting the MA method.

PROBLEM REQUIREMENTS AND CHARACTERISTIC OF

DIFFERENT METHODS

SAW TOPSIS ELECTRE AHPFUZZY

METHOD

FUZZY MA

METHOD

Criteria

WeightCrisp Crisp Crisp Fuzzy Fuzzy Fuzzy

Alternative

RatingCrisp

Crisp Crisp Fuzzy Fuzzy Fuzzy

Criteria

Information

Procesing

Compensatory Compensatory Compensatory

Non-

Compensatory Compensatory Compensatory

Feature Scoring Ideal Solution OutrankingPairwise

Comparison Ideal Solution

Pairwise

Comparison

Solution

Aimed to

Evaluate,

Prioritize and

Select

Evaluate,

Prioritize and

Select

Evaluate,

Prioritize and

Select

Evaluate,

Prioritize and

Select

Evaluate,

Prioritize and

Select

Evaluate,

Prioritize and

Select

Transforma

tion of

Values to

Common

Scale

Normalized

Scale

Normalized

Scale

Normalized

Scale

Normalized

Scale

Normalized

Scale

EXAMPLE OF THE RULES

RULES CONDITIONS METHOD

RULE 1

IF Mode of guidance = “Novice” AND Criteria weight = “1” AND Alternative

rating = “3” AND Criteria information processing =

“Compensatory” AND Feature = “Scoring” AND Transformation of values =

“Common scale”

SAW

RULE 2

IF Mode of guidance = “Novice” AND Criteria weight = “3” AND Alternative

rating = “2” AND Criteria information processing =

“Compensatory” AND Feature = “Ideal Solution” AND Transformation of values

= “Normalized scale”

TOPSIS

RULE 3

IF Mode of guidance = “Novice” AND Criteria weight = “Very high” AND

Alternative rating = “Low” AND Criteria information processing =

“Non-compensatory” AND Feature = “Pairwise comparison” AND

Transformation of values = “Normalized scale”

AHP

RULE 4

Mode of guidance = “Novice” AND Criteria weight = “High” AND Alternative

rating = “High” AND Criteria information processing =

“Compensatory” AND Feature = “Ideal solution” AND Transformation of values

= “Normalized scale”

FUZZY

RULE 5IF Mode of guidance = “Intermediate” AND Criteria weight = “1” AND Alternative

rating = “3”

SAW, TOPSIS, and

ELECTRE

methods for selection

RULE 6IF Mode of guidance = “Intermediate” AND Criteria weight = “High” AND

Alternative rating = “High”

AHP, Fuzzy, and Fuzzy

MA methods for

selection

RULE 7IF Mode of guidance = “Advanced” all MA methods for

selection

EXAMPLE OF IMPLEMENTATION

Problem:

Evaluating and Selecting

a SCM IS Project at Steel

Mill in Taiwan

Objective:

To be competitive by

reducing total costs and

maximize its return in

investment

Challenges:

A SCM system should

can improve by:

collaboration different

stages of a supply chain

and providing real time

analytical capabilities in

production planning

Team (Decision

Makers):Formation of project team

involving seven senior

managers (represent each

department)

Defined:

The Problems, industry

characteristic, changes

business environment,

clients demands, for

determining the scope of

project

Criteria Determined:

Strategic Capability (C1),

Project Characteristic

(C2),

IS Project Capability

(C3), and

Vendor Characteristic

(C4)

Hierarchical Structure of SCM

Project selection Problem:

Legend:

EXAMPLE OF IMPLEMENTATION

Assigned Linguistic Variables for the Criteria Variables (by Specific Concern):

Assigned Linguistic Variables for Weights of Criteria (by Specific Concern):

Select one of Mode for Decision

Maker:

1. Novice Mode

2. Intermediate Mode or

3. Advanced Mode

The Reason to Novice Mode:(a) the decision maker’s preference of a specific MA

method,

(b) the time availability of the decision maker,

(c) the decision maker’s desire to interact with the

system, and

(d) the desire to allow the system to select one

satisfactory solution or for the decision maker to

select a solution.

EXAMPLE OF IMPLEMENTATION

Performance Assesments of

Alternatives SCM Project : Criteria Weights for SCM Project

Selection:

EXAMPLE OF IMPLEMENTATION

Based on the information provided by the decision maker, the IF-THEN rules

explicitly match the specific method to the requirements of the decision maker. In

this case, the DSS has selected the fuzzy method.

Based on the information given by the decision maker to handle this specific

SCM project selection problem. Performance Index Result are:

As result:

A2 is the most suitable project alternative.

THANK YOU!