Simulation is the process of designing a model of a real system and conducting experiments on the model to understand the behavior of the system. The key steps in simulation include formulating the problem, introducing important variables and decision rules, constructing a simulation model, validating the model, designing experiments, performing the simulation, determining if the simulation problem is complete, and selecting the best course of action. Simulation is useful for solving complex problems, experimenting, studying long-term effects, validating analytical solutions, assessing stability, generating data, saving time, and as a last resort approach. Monte Carlo simulation and simulation applications in business, inventory control, and financial decision making are discussed. An example problem on generating a 10-day demand sequence using a given

1. SIMULATION
Presented by
Sunaina Dubey
Mba 3rd sem

2.  Definition
 Steps in simulation
 Singnificance of simulation
 Techniques of simulation
 Example –problem of Monte carlo
simulation and its solution

3. “Simulation is the process of designing a
model of a real system and conducting
experiments with the model for the
purpose of understanding the behaviour
for the operation of the system”

4. In general terms simulation involves
developing a model of some real
phenomenon and then performing an
experiment on the model evolved.It is a
descriptive and not optimisting
techniques.

5. Steps for simulation
Formulation of problem
Introduce important varibles
decision rules of system
parameters
Construct simulation model
Validate the model
Design experiments Modify the modele by
changing the input data
Perform simulation
Is simulation process is
complete?
Select the best course of
action

6. 1. Formulation of problem
2. Introduce important variables decision
rules of system parameters.
3. Construct simulation model
4. Validate the model
5. Design experiments
6. Perform simulation
7. Is simulation problem is complete
8. Select the best course of action

7.  To solve cumbersome problem
 Experiment
 Study the long term effect
 To best proposed analytical solution
 Stability
 Generation of data
 Time saving
 Last resort

8.  Monte carle simulation
 Simulation and buisness situation
 Simulation and inventory control
 Simulation and financial decision

9.  Steps:-
1. Establishing probabilty distribution
2. Comulative probability distribution
3. Setting random number intervals
4. Generating random no

10. QUE:- Over 100 days period the daily demands of a certain
commodity shown the following frquency distribution pattern
Daily 0 1 2 3 4 5
demand
No. of 10 20 40 20 6 4 Total
days 100
Using the given data simulate a ten day sequence of the
demands values

11. Solution:-
Daily demand Probabilty=no Cumulative Rendom no
of days/total probability assignment
no of days
0 10/100=0.10 0.10 00-09
1 20/100=0.20 0.30 10-29
2 40/100=0.40 0.70 30-69
3 20/100=0.20 0.90 70-89
4 6/100=0.06 0.96 90-95
5 4/100=0.04 1.00 96-99
How the random no.assignment has been made is very simple to
figure out (on the basis of cumulative frequency)

12. Day number Generated random Generating demand
demands
1 67 2
2 84 3
3 01 0
4 77 3
5 90 4
6 14 1
7 15 1
8 74 3
9 44 2
10 77 3
Total=22
Average daily demand =22/10
=2.2 answer

13. THANK YOU