Chapter1
Characteristics of the DSS:
1-It supports individual members and an entire team.
2-It used repeatedly and constantly.
3-It uses subjective, personal, and objective data.
4-It used in the private sector.
What is a DSS ?
DSS identified it as a system intended to support managerial decision- makers in
semi structured decision situations.
DSS Versus EDP:
Dimension
Use
DSS
EDP
active
passive
User
Line and staff management
clerical
Object
Flexibility
consistency
A DSS Application ?
Is usually built to support the solution of a certain problem or to evaluate an
opportunity. as such it is called a DSS application.
Key characteristics and capabilities of DSS?
1- support managers at all levels.
2-support individuals and groups.
3-interaction ease of use.
4-humans control the machine.
5-ease development by and users.
6-modeling and analysis.
7-data access.
Business intelligence (BI) :
Is a collection of technical and process innovation across the data warehousing and
business intelligence space.
Business analytics : implies the use of models in business intelligence.
Components of DSS ?
1- Data management subsystem : include database the contains relevant data of the
situation and is managed by software called the database management system
(DBMS).
2-Model base management subsystem : This is a software that include financial ,
statistical, management science.
3-User interface subsystem : the user communication with and commands the DSS
through this subsystem.
4-Knowledge based management subsystem : This subsystem can support any of the
other subsystem or act as independent component.
DSS
Components drowning ?
The data management subsystem is composed of the following element :
1- DSS database.
2-Database management system.
3-Data directory.
4-Query facility.
The model management subsystem of the DSS is composed of the following
element:
1-Model base.
2-Model base management system.
3-Modeling language.
4-Model directory.
Strategic model : used to support top management's strategic planning
responsibilities.
Tactical model : Are used mainly by middle management to assist in allocating and
controlling organization's resources.
Operational model : Are used to support the day to day working activities of the
organization.
Analytical model : are used to perform some analysis on the data.
Knowledge based management system : Many unstructured and even semi
structured problems are so complex that their solutions require expertise. This con
by provided by an expert system or other intelligent system. Therefore, more
advanced DSS are equipped with a component called a knowledge based
management system.
Other Classifications:
Institutional DSS vs. Ad Hoc DSS
1- Institutional DSS deals with decisions of a recurring nature.
2- Ad Hoc DSS deals with specific problems that are usually neither anticipated nor
recurring.
Intelligent DSS Categories:
1-Descriptive.
2-Procedural.
3-Reasoning.
4-Linguistic.
5-Presentation.
6-Assimilative.
DSS Classifications:
Alter’s Output Classification (1980)Degree of action implication of system outputs
(supporting decision) (Table 3.3)
Holsapple and Whinston’s Classification
1. Text-oriented DSS.
2. Database-oriented DSS.
3. Spreadsheet-oriented DSS.
4. Solver-oriented DSS.
5. Rule-oriented DSS.
6. Compound DSS.
Alternate Categories of Intelligent DSS:
1-Symbiotic.
2-Expert-system based.
3-Adaptive.
4-Holistic.
Chapter2
Static analysis: static model : take a single snapshot of a situation. During this snapshot
everything occurs in a single interval.
Dynamic analysis: Dynamic model: represent scenarios that change over time.
MSS Modeling: (select any 3 or 4)
1-Key element in DSS
2- classes of models
3-Specialized techniques for each model
4-Allows for rapid examination of alternative solutions
5-Multiple models often included in a DSS
6-Trend toward transparency
Simulations: (select any 3 or 4)
1-Explore problem at hand.
2-Identify alternative solutions.
3-Can be object-oriented.
4-Enhances decision making.
5-View impacts of decision alternatives.
DSS Models: (select any 3 or 4)
1-Algorithm-based models.
2-Statistic-based models.
3-Linear programming models.
4-Graphical models.
5-Quantitative models.
6-Qualitative models.
7-Simulation models.
Influence Diagrams: is a graphical representation of a model used to assist in model
design , development , and understanding.
Dynamic Model: (select any 3 or 4)
1-Represent changing situations.
2-Time dependent.
3-Varying conditions.
4-Generate and use trends.
5-Occurrence may not repeat.
MSS Mathematical Models
1-Symbolic relationship
2-Qualitative relationship
3-Results based upon
4-Decision selected
5-Factors beyond control of decision maker
6-Relationships amongst variables
Decision-Making: (select any 3 or 4)
Probabilistic Decision-Making
1-Decision under risk
2-Probability of each of several possible outcomes occurring
3-Risk analysis
4-Calculate value of each alternative
5-Select best expected value
Influence Diagrams: 1-Graphical representation of model
2-Provides relationship framework
3-Examines dependencies of variables
4-Any level of detail
5-Shows impact of change
6-Shows what-if analysis
Influence Diagrams
Variables:
Intermediate or
uncontrollable
Decision
Result or outcome
(intermediate or
final)
Arrows indicate type of relationship and direction of influence
Amount
in CDs
Certainty
Sales
Uncertainty
Random (risk)
Price
~
Demand
Sales
Place tilde above
variable’s name
Preference
(double line arrow)
Interest
earned
Sleep all
day
Graduate
University
Get job
Ski all
day
Arrows can be one-way or bidirectional, based upon the direction of influence
EXAMPLES:
Decision Tables: Decision tables are a convenient way to organize information in a
systematic manner.
Decision Trees: A decision tree show the relationships of the problem graphically and can
handle complex situations in a compact form.
The components of MSS mathematical models:
1-decision variables.
2-uncontrollable variables.
3- result (outcome) variables.
The components of MSS mathematical models of drowning:
Mathematical Programming: is a family of tool designed to help solve managerial problem
in which the decision maker must allocate scarce resources among competing activities to
optimize a measurable goal.
Linear programming
Consists of decision variables, objective function and coefficients,
uncontrollable variables (constraints), capacities, input and output
coefficients.
WHAT IF Analysis: is structured as what will happen to the solution if an input
variable , an assumption, or a parameter value is changed?
Goal Seeking: calculates the value of the inputs necessary to achieve a desired
level of an output (goal).
Analytical Techniques: analytical techniques use mathematical formulas to
derive an optimal solution directly or to predict a certain result.
Algorithms: analytical techniques may use algorithms to increase the efficiency
of the search. An algorithm is step by step search process for obtaining an
optimal solution.
Blind search technique: are arbitrary search approaches that are not guided.
There are two types of blind searches: a complete enumeration, for which all
the alternatives are considered and therefore an optimal solution.