1. INTELLIGENT DECISION SUPPORT FOR EVALUATING
AND SELECTING INFORMATION SYSTEMS PROJECTS
By. Hepu Deng And Santoso Wibowo (2008)
Anita Carollin
TIBS 122121805/RBS 0874078

2. 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

3. IDSS LIMITATIONS AND SOLUTIONS
LIMITATIONS 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

4. 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

5. 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.

6. 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

7. 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

8. 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

9. DSS FRAMEWORK FOR SELECTING IS PROJECTS

10. 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.

11. 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.

12. PROBLEM REQUIREMENTS AND CHARACTERISTIC OF
DIFFERENT METHODS
SAW TOPSIS ELECTRE AHP
FUZZY
METHOD
FUZZY MA
METHOD
Criteria
Weight
Crisp Crisp Crisp Fuzzy Fuzzy Fuzzy
Alternative
Rating
Crisp
Crisp Crisp Fuzzy Fuzzy Fuzzy
Criteria
Information
Procesing
Compensatory Compensatory Compensatory
Non-
Compensatory Compensatory Compensatory
Feature Scoring Ideal Solution Outranking
Pairwise
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

13. 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 5
IF Mode of guidance = “Intermediate” AND Criteria weight = “1” AND Alternative
rating = “3”
SAW, TOPSIS, and
ELECTRE
methods for selection
RULE 6
IF Mode of guidance = “Intermediate” AND Criteria weight = “High” AND
Alternative rating = “High”
AHP, Fuzzy, and Fuzzy
MA methods for
selection
RULE 7
IF Mode of guidance = “Advanced” all MA methods for
selection

14. 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:

15. 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.

16. EXAMPLE OF IMPLEMENTATION
Performance Assesments of
Alternatives SCM Project :
Criteria Weights for SCM Project
Selection:

17. 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.

18. THANK YOU!