Dss

1. Constructing a DSS
Introduction
Identifying the Problem
Analyzing the Data
Testing the Models

2. Introduction
• A decision support system (DSS) is a computer-based information system that supports business or
organizational decision-making activities. DSSs serve the management, operations and planning levels of an
organization and help to make decisions, which may be rapidly changing and not easily specified in advance.
• Constructing a DSS involves a number of steps, including identifying the problem, collecting data, analyzing the
data, developing models, testing the models and implementing the system. In this presentation, we will explore
each of these steps in detail.

3. Identifying the Problem
 The first step in constructing a DSS is to identify the problem that needs to be solved.
 This involves understanding the decision-making process and the factors that
influence it. It is important to involve all stakeholders in this process to ensure that the
problem is well-defined and that everyone has a clear understanding of the objectives.
 Once the problem has been identified, the next step is to collect data. This may involve
gathering information from internal sources, such as databases and spreadsheets, as
well as external sources, such as market research reports and industry publications.

4. Analyzing the Data
After the data has been collected, it needs to be analyzed to identify patterns and trends.
This may involve using statistical analysis tools, such as regression analysis and correlation
analysis, to identify relationships between variables. It may also involve using data mining
techniques to uncover hidden patterns in the data.
Once the data has been analyzed, the next step is to develop models. These models may be
mathematical, such as linear programming models, or they may be rule-based, such as
expert systems. The models are designed to help decision makers evaluate different
scenarios and make informed decisions based on the data.

5. Testing the Models
Before implementing the DSS, it is important to test the models to ensure that they are
accurate and reliable. This may involve running simulations and conducting sensitivity
analyses to evaluate the performance of the models under different conditions. It may
also involve comparing the results of the models to actual outcomes to determine their
effectiveness.
Once the models have been tested and refined, the final step is to implement the DSS.
This may involve integrating the system with existing IT infrastructure, training users on
how to use the system, and monitoring its performance to ensure that it continues to
meet the needs of the organization.

6. Development strategies
 Customised DSS in general-purpose programming language
 Fourth Generation Language
 DSS integrated development tool(generator/engine)
 Specific DSS generator
 CASE methodology

7. The DSS Development Process: life Cycle versus Prototyping
System Development Methods
• System development life cycle (SDLC)
• Prototyping
SDLC
• This approach considers identification of user requirement, analysis of existing system, designing overall system
and its designing , development(with simultaneous implementation) and testing.
• Each of these steps calls a written document, reviewed and approved before starting next step. The basic
advantage of this approach is that it covers all the areas and maintains a record of them. On the other hand it is
too rigid for a system that is frequently changing or updating.

8. Planning
1.identify business value system request
2. Analyse feasibility feasibility study
3. Develop work plan work plan
4. Staff project staffing plan, project plan
5. Control and direct project project mgt tools, CASE tool, standards list, project, binders/ files, risk assessment
Analysis
6. Analyse problem analyse plan
7. Gather information information
8. Model process(es) process model
9. Model data data model
Design
10. Design physical system design plan
11. Design architecture architecture design, infrastructure design
12. Design interface interface design
13. Design database and files data storage design
14. Design program(s) program design

9. • Implementation
• System delivery
• 15. Construction test plan, programs, documentation
• 16. Installati0n conversion plan, training plan
Problems faced in implementation stage
• No project team
• No defined schedule, ballooning scope
• Unclear aspects of make vs. Buy decisions
• Few project integrations are functional out of the box
• Qualitative benefits
• No user buy in
• Poor project mgt skills
• No responsibility

10. Prototype Approach
• Performing analysis, design, and implementation phases concurrently, and repeatedly
• Users can observe system functionality quickly and provide feedback
• Decision maker learns about problem but can lose gains in repetition
When constructing a DSS, prototyping approach/ iterative design process seems to be a better alternative method because of the flexibility as well as the
short development cycle needed by decisions and decision-makers. It actually clears misconceptions between builders and end users,
Advantages of prototyping
 Short development time
 Short user reaction time
 Improved user’s understanding of the system, its information needs and its capabilities
 Low cost
Disadvantages of prototyping
 Lack of detailed description of information needs.
 Gains might be lost through cycles

11. Team-developed versus User-developed DSS
 DSS 1970s and early 1980s
 Large –scale, complex systems
 Primarily provided organisational support
 Team efforts
Team-developed DSS
 Substantial effort
 Extensive planning and organisation
 Some generic activities
 Group of people to build and to manage the DSS
The group consists of ISD(Information Services Department), Executive staff group, Finance or other functional area, Industrial Eng Department,
Management science group, Information centre group.
User Developed DSS Advantages
 Short time delivery
 Eliminate extensive and formal user requirements specifications

12. End-User Computing Versus User-developed DSS
 End-user computing is the development and use of computer-based information systems by people outside the formal information systems areas.
 End-users
- At any level of the organisation
- In any functional area
- Levels of computer skills vary
Issues in Reducing End-User Computing risks
 Error detection
 Use of auditing techniques
 Determine the proper amount of controls
 Investigate the reasons for the errors
 It gives solutions

13. Risks
 Poor quality
 Quality risks
-substandard or inappropriate tools facilities
-development process risks
-data management risks
 Increased security risks
 Problems form lack of documentation and maintenance procedures

14. End-User Computing Versus User-developed DSS
End-user computing is the development and use of computer-based information systems by people outside
the formal information systems areas.
End-users
-At any level of the organisation
-In any functional area
-Levels of computer skills vary
Issues in Reducing End-User Computing risks
Error detection
Use of auditing techniques
Determine the proper amount of controls
Investigate the reasons for the errors
It gives solutions

15. DSS Technology Levels
There are three levels of DSS technology: specific DSS (application), DSS Integrated tools known as DSS generators eg excel and
the Primary tools eg programming languages. the primary tools are used to construct the generators, which in turn are used
construct SDSS. They may also used to construct tools that are more complicated. Using a DSS generator saves time and money,
making the DSS financially feasible.
Specific DSS (the application)
It involves an application that allows a specific decision-maker or group of them to deal with specific sets of related problems.
DSS Generators
Is an integrated easy-to-use package with diverse capabilities ranging from modelling, report generation, graphical presentation to
performing risk analysis. A DSS generator create a platform from which SDSS can constantly be developed without much
consumption of time and effort.
Primary tools
Lowest level and it consists of software utilities or tools. These elements facilitates the development of a DSS generator and SDSS.
Eg graphics, editors, query systems, random number generators and spreadsheets. A SDSS can be built directly from primary tools.

16. Selection of a DSS Generator and other software tools
 Some DSS generators are better for certain types of applications than others
• When selecting a decision support system (DSS) generator and other software tools for a DSS project, it is
important to consider several factors, such as:
1. Functionality: The DSS generator should have the necessary features and capabilities to support the specific
requirements of the DSS project, such as data analysis, modelling, simulation, and visualization.
2. Usability: The DSS generator and other software tools should have a user-friendly interface that enables users
to easily interact with the system and perform tasks.
3. Compatibility: The DSS generator and other software tools should be compatible with the hardware and software
infrastructure of the organization, including operating systems, databases, and other applications.

17. 4. Scalability: The DSS generator and other software tools should be able to scale up or down as needed to accommodate
changes in data volume, user traffic, and other factors.
5. Integration: The DSS generator and other software tools should be able to integrate with other systems and
applications, such as enterprise resource planning (ERP) systems or customer relationship management (CRM) systems.
6. Support: The vendor of the DSS generator and other software tools should provide adequate technical support,
including training, documentation, and troubleshooting.
7. Cost: The cost of the DSS generator and other software tools should be reasonable and within the budget of the
organization.
• By considering these factors, organizations can select a DSS generator and other software tools that meet their specific
needs and help them achieve their business goals.

18. Conclusion
Constructing a DSS is a complex process that requires careful planning and execution. By
following the steps outlined in this presentation, organizations can develop effective
decision support systems that help them make informed decisions and stay ahead of the
competition.
As technology continues to evolve, the capabilities of DSSs will continue to expand,
providing organizations with even more powerful tools for decision making.