Decision Support Systems Concepts,
Methodologies, and Technologies:
An Overview
Learning Objectives
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




Understand possible decision support system (DSS)
configurations
Understand the key differences and similarities
between DSS and BI systems
Describe DSS characteristics and capabilities
Understand the essential definition of DSS
Understand important DSS classifications
Understand DSS components and how they
integrate
Learning Objectives
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Describe the components and structure of each DSS
component
Explain Internet impacts on DSS (and vice versa)
Explain the unique role of the user in DSS versus
management information systems
Describe DSS hardware and software platforms
Become familiar with a DSS development language
Understand current DSS issues
Opening Vignette:
“Decision Support System Cures for Health Care”
 Company background
 Problem
 Proposed solution
 Results
 Answer and discuss the case questions
Opening Vignette:
“Decision Support System Cures for Health Care”
- Projected Vacancy Rate versus Desired Vacancy Rate
Opening Vignette:
- Projected Vacancy Rate vs. Desired Vacancy Rate "What-if"
scenario with 6 additional RN recruiters
Opening Vignette:
- Demanded Hours versus Total Actual Hours versus Total Actual
Hours with New Hires
DSS Configurations

Many configurations exist; based on
 management-decision
situation
 specific technologies used for support

DSS have three basic components
1.
2.
3.
4.
Data
Model
User interface
(+ optional) Knowledge
DSS Configurations

Each component
has several variations;
are typically deployed
online
 Managed by a
commercial of custom
software


Typical types:
Model-oriented DSS
 Data-oriented DSS

DSS Description

A DSS is typically built to support the solution of a
certain problem (or to evaluate a specific
opportunity). This is a key difference between DSS
and BI applications
BI systems monitor situations and identify problems and/or
opportunities, using variety of analytic methods
 The user generally must identify whether a particular
situation warrants attention
 Reporting/data warehouse plays a major role in BI
 DSS often has its own database and models

DSS Description

DSS is an approach (or methodology) for supporting
decision making
uses an interactive, flexible, adaptable computer-based
information system (CBIS)
 developed (by end user) for supporting the solution to a
specific nonstructured management problem
 uses data, model and knowledge along with a friendly
(often graphical; Web-based) user interface
 incorporate the decision maker's own insights
 supports all phases of decision making
 can be used by a single user or by many people

A Web-Based DSS Architecture
i.
ii.
iii.
Multi-tiered architecture uses a web browser to run the programs on an
application server.
The server access data to construct one or more models. Data may also be
provided by a data server that optionally extract data from a data warehouse.
When the user requires that the model be optimized, the model , populated with
the data, is transferred to an optimization server. The optimization server accesses
additional data from data server, if needed, solves problem, and provides the
solution directly to the user’s web browser.
DSS Characteristics and Capabilities
Both DSS and BI include analytical tools to run the
systems


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Business analytics implies the use of models and data to improve an
organization's performance and/or competitive posture. OLAP and
Data Mining system have embadded in them.
Web analytics implies using business analytics on real-time Web
information to assist in decision making; often related to eCommerce. Eg. CRM, SCM ect.
Predictive analytics describes the business analytics method of
forecasting problems and opportunities rather than simply
reporting them as they occur. They include advance forecasting and
simulation model
Components of DSS Application
Components of DSS
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
Data Management Subsystem
 Includes the database that contains the relevant data and its
managed by Database management system (DBMS)
 Can be connected to a data warehouse
Model Management Subsystem
 Model base management system (MBMS) : is software package
that includes financial, statistical, management science, or other
quantitative models that provide the system’s analytical
capabilities and appropriate software management.
Components of DSS
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
User Interface Subsystem
 The user communicate with and commands the DSS
through the user interface subsystem. The web browser
provides a familiar, consistent graphical user
interface(GUI) structure for DSS
Knowledgebase Management Subsystem
 KMS can support any of the other subsystems or act as an
independent component.
How the DSS Components Integrate

The four components can be connected to a corporate
internet, to an extranet, or to the internet.
DSS Components
Data Management Subsystem
The data management subsystem is composed of the
following elements
DSS database
DBMS
Data directory
Query facility
Database Management Subsystem
Key Data Issues

DSS Database : A DB is a collection of
interrelated data, organized to meet the needs
and structure of an organization that can be used
by more than one person for more application.
There are several possible configuration for a
database.
DSS Components
Data Management Subsystem
Figure showing Data are extracted from internal and External data
sources, as well as from personal data belonging to one or more
users. The extraction results go to the specific application’s DB or to
the corporate data warehouse.
Database Management Subsystem
 DBMS
: A DB is created , accessed, and update by a
DBMS.
 The Data Directory : The Data Directory is a catalog of
all the data in a DB. It contains data definition, and its
main function is to answer questions about the availability
of data items, their sources, and their exact meaning. It
also supports the addition of new entries, deletion of
entries, and retrieval of information about specific objects.
 Query
Facility : It accepts requests , and determines how
the request can be filled, formulates the detailed request,
and returns the results to the issuer of the request. E.g. SQL
Database Management Subsystem
Key Data Issues

Data quality
 “Garbage

Data integration
 “Creating

in/garbage out" (GIGO)
a single version of the truth”
Scalability
 Large
DB present major scalability problems, part of the
problems can be solved by carefully spiliting data up
and having them span multiple disk drives, perhaps each
access by several processor.

Data Security
 Data
can protected by unauthorized user. i.e SQL guard
DSS Components
Model Management Subsystem




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Model base
MBMS
Modeling language
Model directory
Model execution,
integration, and
command processor
DSS Components
Model Management Subsystem

Model base : A Model base contains routine and special
statistical, financial, forecasting, management science, and
other quantitative models that provides the analysis
capabilities in a DSS. It can invoke, run, change, combine,
and inspect. The model in the model base divided into four
categories :
 Strategic
models : Support top managers’ strategic plan.
 Tactical models : Used by the mid managers to assist in
allocating and controlling the organization’s recourses.
 Operational models: Support day-to-day working activities.
 Analytic models : Used to perform analysis on data through
statistical, management science, data mining etc.
DSS Components
Model Management Subsystem

MBMS software has four (4) functions
1.
2.
3.
4.


Model creation, using programming languages, DSS
tools and/or subroutines, and other building blocks
Generation of new routines and reports
Model updating and changing
Model data manipulation
Model directory : It is catalog of all the models
and other software in the model base.
Model execution, integration and command are
usually controlled by model management.
DSS Components
User Interface (Dialog) Subsystem
User Interface covers all aspects of communication between
a user and the DSS or any MSS. It includes not only the
hardware and software but also factors that deal with ease
of use, accessibility, and human-machine interactions.
It’s the user’s standpoint because it is the only point of the
system that the user sees. A difficult user interface is one of
the major reasons managers do not use computers and
quantitative analyses as they could.
The web browser has been recognize as an effective DSS
GUI because it is flexible, user friendly, and gateway to
almost all sources of necessary information and data.
DSS Components
User Interface (Dialog) Subsystem

Interface


Application interface
User Interface


DSS User Interface


Portal
Graphical icons



Graphical User Interface (GUI)
Dashboard
Color coding
Interfacing with PDAs, cell
phones, etc.
DSS Components
Knowledgebase Management System


Incorporation of intelligence and expertise
Knowledge components:
Expert systems,
 Knowledge management systems,
 Neural networks,
 Intelligent agents,
 Fuzzy logic,
 Case-based reasoning systems, and so on


Often used to better manage the other DSS
components
DSS Components
Future/current DSS Developments

Hardware enhancements
 Smaller,

faster, cheaper, …
Software/hardware advancements
 data
warehousing, data mining, OLAP, Web
technologies, integration and dissemination technologies
(XML, Web services, SOA, grid computing, cloud
computing, …)

Integration of AI -> smart systems
DSS User

One faced with a decision that an MSS is designed to
support
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The users differ greatly from each other
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Manager, decision maker, problem solver, …
Different organizational positions they occupy; cognitive
preferences/abilities; the ways of arriving at a decision
(i.e., decision styles)
User = Individual versus Group
Managers versus Staff Specialists [staff assistants,
expert tool users, business (system) analysts,
facilitators (in a GSS)]
DSS Hardware
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

Typically, MSS run on standard hardware
Can be composed of mainframe computers with
legacy DBMS, workstations, personal computers, or
client/server systems
Nowadays,
usually
implemented
as
a
distributed/integrated, loosely-coupled
Web
based systems
Can be acquired from
A single vendor
 Many vendors (best-of-breed)

A DSS Modeling Language
Planners Lab (plannerslab.com)

Generating
Assumptions
A DSS Modeling Language
Planners Lab (plannerslab.com)

Creating a
new model
A DSS Modeling Language
Planners Lab (plannerslab.com)
A DSS Modeling Language
Planners Lab (plannerslab.com)
A DSS Modeling Language
Planners Lab (plannerslab.com)
A DSS Modeling Language
Planners Lab (plannerslab.com)
A DSS Modeling Language
Planners Lab (plannerslab.com)
A DSS Modeling Language
Planners Lab (plannerslab.com)
A DSS Modeling Language
Planners Lab (plannerslab.com)
A DSS Modeling Language
Planners Lab (plannerslab.com)
A DSS Modeling Language
Planners Lab (plannerslab.com)
A DSS Modeling Language
Planners Lab (plannerslab.com)
A DSS Modeling Language
Planners Lab (plannerslab.com)
A DSS Modeling Language
Planners Lab (plannerslab.com)
End of the Chapter
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Questions / Comments…