1. Applications of simulation in business
Pratima Ray
MMS Sem III
Presented by :

2. What is simulation ?
Real System.
MODEL of
Real System.
(Complex real-world
system )
A process of designing
(Conducting
experiments with THIS
model )
“SIMULATION is the process of designing a MODEL of a real system and
conducting a series of repeated trial-and-error (for which there are no
optimal solutions as such, unlike mathematical models) experiments with
this MODEL for the purpose of either understanding the behavior of the
system and/or evaluating various strategies for the operation of the system.”

3. Wooden, mechanical,
horse simulator during
World War I
Human-in-
the-loop
simulation
of outer
space
Visualization of
a direct numerical
simulation model.
Military Simulation
Life simulation games

4. When to use Simulation ?
Real System. MODEL of Real System.
• To stimulate is to duplicate (imitate) the features, appearance and
characteristics of a real system.
• The idea behind this is :- to initiate a real world situation mathematically,
then to study its properties and finally to draw conclusions.
• Real-life system is not touched UNTIL the advantages and disadvantages
are first measured on the SYSTEM’S MODEL.
• For making decisions on very complex problems for which there are no
optimal solutions.
• Problems which need analytical(logical reasoning) approaches/which
cannot be definitively quantified.
• Risky to attempt straight and optimal solutions/decisions.
• When it is not advisable to experiment with reality itself.
•To study almost any problem that involves uncertainty.
• Where mathematical simplification is not feasible.
• Lack of time to gather operating data from a real system.
• When real system can get very costly.

5. Simulation’s greatest strength is
its ability to answer
“what if” questions...
Advantages
• Straight forward and flexible
• suitable to analyze large and complex real-life problems
• Sometimes simulation is the only method available
• Does not interfere with real world system.
• It may be used over and over to analyze different situations.
• Breaking down of complicated systems into sub systems
• Data for further analysis can easily be generated
• Avoids cost of real world experimentation.
• serves as a ‘pre-service test’

6. Disadvantages
• Sometimes simulation models are expensive and take a long time to
develop.
• It is a trial and error approach that may produce different solutions in
repeated runs
• It is often too long and a complicated process to develop a model.
Simulation results are sometimes hard to interpret.
• Difficult for people to understand that they are not looking at reality but
an abstraction of the real world
• Each application of simulation is ad-hoc to
……a great extent.
• The simulation model does not produce any
`````answers by itself the user has to provide all
````` the constraints for the solutions that he `````
````` wants to examine.

8. A Simulation Model
• SIMULATION is a numerical technique for
conducting experiments on a digital computer
(It is a technique which uses computers).
• SIMULATION MODEL represents a system
using number and symbols that ca be readily
manipulated.

9. Simulation Modelling Classifications
Static vs. Dynamic:
• Static: No attempts to model a time sequence of changes.
• Dynamic: Updating each entity at each occurring event.
Deterministic vs. Stochastic:
• Deterministic: Rule based.
• Stochastic: Based on conditional probabilities.
Discrete vs. Continuous:
• Discrete: Changes in the state of the system occur instantaneously at
random points in time as a result of the occurrence of discrete events.
• Continuous: Changes of the state of the system occur continuously
over time.

10. Stochastic Simulation
• (Definition)When a system contains certain factors that
can be represented by a probability distribution
• Probability distribution is used to quantify the
outcomes. Eg. Flipping of a coin, outcome{H,T}
• A random variable assigns number to the possible
occurrence to each outcome.
• There are 2 techniques of simulation:
– Monte-Carlo: used for decision making under uncertainty
– System simulation technique: reproduction of operating
system
• Monte-Carlo technique is generally used.

11. Monte-Carlo:
• This technique uses random number and is generally
used to solve problems requiring decision making under
uncertainty and where mathematical formulation is
impossible.
• It is a recent O.R innovation(treated as a synonym for
simulation)
• Novelty lies in making use of pure chances to construct a
simulated version of process exactly as pure chances
operates the original system under working conditions
Table of random
numbers
• Monte-Carlo sim. requires generation
of random numbers(generated using
digital computers) that is an integral
part of the observations (samples)
from the probability distribution.

12. Application of simulation in business
with example
• Simulation is used in almost all fields, restricted by our
imagination and the ability to translate such imagination into
computer directives.
• BUSINESS Applications: stock and commodity analysis, pricing
policies, marketing strategies, cash flow analysis, forecasting,
etc.
• Considering an example of how sim. can be used in queuing
system.
 Sometimes careful analysis reveals a great output rate than thought
possible.
 Considering an EXAMPLE of university Health-service
Outpatient Clinic where this analysis was preferred
 Strategy was to build a Mount-Carlo Simulation to use the model
experimentally and improve the clinic operations.

15. Expected processing time = 866/10 = 86.6

16. Thank You