The Methodology of Simulation
Simulation involves setting up a model of a real system and conducting
repetitive experiments on it.
1) Problem
definition
The real world problem is examined & classified.
2) Construction of
the simulation
model
This step involves the determination of the variables & their
relationships & the gathering of necessary data.
3) Testing and
validating the
model
The simulation model must properly represent the system
under study.
Testing and validation ensure this.
4) Design of the
experiments
Once the model has been proven valid, and experiment is
designed.
There are two important & conflicting objectives :
Accuracy & Cost
Best case and worst case scenarios
5) Conducting the
experiments
This involves issues ranging from random number generation
to presentation to presentation of the results.
6) Evaluating the
results
We determine the meaning of the results. In addition to
statistical tools, we may use sensitivity analyses.
7) Implementation The chances of implementation are better because manager
is usually more involved in the simulation process than with
other models.
Types of Simulation
1) Probabilistic
Simulation
One or more of the independent variables (Such as the
demand in an inventory problem) are probabilistic.
2) Discrete
Distributions
Involve a situation with a limited number of events (or
variables) than can take on only a finite number of values.
3) Continuous
Distributions
These are situations with unlimited numbers of possible
events that follow density functions such as the normal
distribution.
Probabilistic simulation is conducted with the aid of a
technique called Monte Carlo.
4) Time dependent
versus time
independent
simulation
Time independent refers to a situation in which it is not
important to know exactly when the event occurred.
5) Simulation
software
These include spreadsheet ad-ins.
6) Visual simulation The graphic display of computerized results, which may
include animation.
7) Object oriented
simulation
Some recent advances in the area of developing simulation
models using the object oriented approach.
Multidimensional Modeling
The original spreadsheets were two dimensional.
Later, with the introduction of windows, spreadsheet packages introduced
what thy called a 3-D approach.
Multidimensional modeling tools provide the solution.
A typical multidimensional tool such as CA-Masterpiece/200.
It has data manipulation and drags and drop capabilities through which
users can change the shape of the spreadsheets.
Financial and Planning Modeling
Definition and background of planning modeling
Financial planning models may have a very short planning horizon and
entail no more than a collection of accounting formulas for producing
proforma statements.
On the other hand, corporate planning models often include complex
quantitative and logical relationships amount a corporation’s financial,
marketing and production activities.
Educom’s financial planning model (DFPM)
Ready made Quantitative software Package
Some DSS tools offer several built in subroutines for constructing
quantitative models in areas such as statistics, financial analysis,
accounting, and management science.
These models can be called up by one command such as SQRT.
Many DSS tools can easily interface with powerful standard quantities
stand alone software package.
Data Management Warehousing Access & Visualization
Data & its management are the foundation on which DSS application are
constructed.
The centralized database of data warehouse collects data from the
different sauces and organizes them, so they are easily accessible by
DSS and EIS applications.
Organizations, private and public, are continuously collecting data,
information, and knowledge at an accelerated rate and storing them in
computerized systems.
The accessed data must be analyzed and presented to the users.
Data Warehousing
There is a need for specialized, localized hardware and software
solutions.
There is a need for a cost effective means of uniting those information
resources into a manageable business asset.
Organizations today have a mixture of older, centralized systems and
newer, distribute systems, a wide variety of technologies is provided by an
even larger number of vendors.
Data Warehousing
Data warehousing (or information warehousing) is a concept designed to
provide a solution to the data access problem.
The data warehouse combines various data sources into a single
resource for end user access.
End users can perform ad hoc queries, reporting analysis, and
visualization of the warehouse information.
There can be several data warehouses in once company.
Benefits
It should provide ready access to critical data, insulate operation
databases from ad hoc processing that can slow TPS systems, and
provide high level summary information as edge, provide competitive
advantage, enhance customer services and satisfaction, facilitate decision
making and help in streamlining business process.
Simulation involves setting up a model of a real system and conducting
repetitive experiments on it.
1) Problem
definition
The real world problem is examined & classified.
2) Construction of
the simulation
model
This step involves the determination of the variables & their
relationships & the gathering of necessary data.
3) Testing and
validating the
model
The simulation model must properly represent the system
under study.
Testing and validation ensure this.
4) Design of the
experiments
Once the model has been proven valid, and experiment is
designed.
There are two important & conflicting objectives :
Accuracy & Cost
Best case and worst case scenarios
5) Conducting the
experiments
This involves issues ranging from random number generation
to presentation to presentation of the results.
6) Evaluating the
results
We determine the meaning of the results. In addition to
statistical tools, we may use sensitivity analyses.
7) Implementation The chances of implementation are better because manager
is usually more involved in the simulation process than with
other models.
Types of Simulation
1) Probabilistic
Simulation
One or more of the independent variables (Such as the
demand in an inventory problem) are probabilistic.
2) Discrete
Distributions
Involve a situation with a limited number of events (or
variables) than can take on only a finite number of values.
3) Continuous
Distributions
These are situations with unlimited numbers of possible
events that follow density functions such as the normal
distribution.
Probabilistic simulation is conducted with the aid of a
technique called Monte Carlo.
4) Time dependent
versus time
independent
simulation
Time independent refers to a situation in which it is not
important to know exactly when the event occurred.
5) Simulation
software
These include spreadsheet ad-ins.
6) Visual simulation The graphic display of computerized results, which may
include animation.
7) Object oriented
simulation
Some recent advances in the area of developing simulation
models using the object oriented approach.
Multidimensional Modeling
The original spreadsheets were two dimensional.
Later, with the introduction of windows, spreadsheet packages introduced
what thy called a 3-D approach.
Multidimensional modeling tools provide the solution.
A typical multidimensional tool such as CA-Masterpiece/200.
It has data manipulation and drags and drop capabilities through which
users can change the shape of the spreadsheets.
Financial and Planning Modeling
Definition and background of planning modeling
Financial planning models may have a very short planning horizon and
entail no more than a collection of accounting formulas for producing
proforma statements.
On the other hand, corporate planning models often include complex
quantitative and logical relationships amount a corporation’s financial,
marketing and production activities.
Educom’s financial planning model (DFPM)
Ready made Quantitative software Package
Some DSS tools offer several built in subroutines for constructing
quantitative models in areas such as statistics, financial analysis,
accounting, and management science.
These models can be called up by one command such as SQRT.
Many DSS tools can easily interface with powerful standard quantities
stand alone software package.
Data Management Warehousing Access & Visualization
Data & its management are the foundation on which DSS application are
constructed.
The centralized database of data warehouse collects data from the
different sauces and organizes them, so they are easily accessible by
DSS and EIS applications.
Organizations, private and public, are continuously collecting data,
information, and knowledge at an accelerated rate and storing them in
computerized systems.
The accessed data must be analyzed and presented to the users.
Data Warehousing
There is a need for specialized, localized hardware and software
solutions.
There is a need for a cost effective means of uniting those information
resources into a manageable business asset.
Organizations today have a mixture of older, centralized systems and
newer, distribute systems, a wide variety of technologies is provided by an
even larger number of vendors.
Data Warehousing
Data warehousing (or information warehousing) is a concept designed to
provide a solution to the data access problem.
The data warehouse combines various data sources into a single
resource for end user access.
End users can perform ad hoc queries, reporting analysis, and
visualization of the warehouse information.
There can be several data warehouses in once company.
Benefits
It should provide ready access to critical data, insulate operation
databases from ad hoc processing that can slow TPS systems, and
provide high level summary information as edge, provide competitive
advantage, enhance customer services and satisfaction, facilitate decision
making and help in streamlining business process.
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