INSTITUTO TECNOLÓGICO DE
PACHUCA
DEPARTMENT OF SYSTEMS ENGINEERING
SIMULATION
TEACHER:
KATYA LORENA
AVILÉS COYOLI
DREAM TEAM:
GARRIDO VIRGEN JESSEL ALEJANDRO
TÉLLEZ PÉREZ AGUSTIN EZEQUIEL
TÉLLEZ LUNA YESENIA
VIEYRA ANDRADE RAZIEL JOB
VERA RODRÍGUEZ HÉCTOR GABRIEL
CONTENTS
1.- Introduction
9.- Data adjustment
2.- Keywords
10.- Method Montecarlo
3.- About the company
11.- First simulation model
4.- Porpouse of the simulation
12.- Final simulation model
5.- Detected problems
13.- Conclusions
6.- Problem impact
14.- Sources Consulted
7.- System Definition
8.- Preliminary Model
INTRODUCTION
The presentation that we
will see next will explore
the process of creating a
simulation system in order
to fix a problem.
KEYWORDS
MAKING THE IDEA OF THE SIMULATION
t
ABOUT THE COMPANY
Tecnoshop, an electronics store located in
Pachuca, specializes in selling electronic
products.
Situated on Avenida Revolución within Plaza
Bella, the company offers customer service
both inside and outside the premises.
Customers can contact the business either by
phone or through its website.
PORPOUSE OF THE SIMULATION
Determine the required quantity of parts
supply through a Monte Carlo simulation
using discrete variables for the products
sold by the company.
PROBLEMS DETECTED
The electronics store faces a significant
challenge in managing its inventory. The
absence of an efficient product tracking and
replenishment system results in various issues,
including the unavailability of popular
products, excess inventory of low-demand
items, and lost sales due to an inability to
meet customer demand.
PROBLEM IMPACT
DISSATISFIED CUSTOMERS
Customers will experience frustration when they cannot find the
products they desire, adversely impacting their shopping
experience.
LOSS OF SALES
The unavailability of high-demand products leads to missed
sales opportunities and may result in customers turning to
competitors..
PROBLEM IMPACT
OPERATIVE COSTS
Excess inventory in slow-moving products not only
raises operating costs but can also lead to financial
losses.
SYSTEM DEFINITION
ENTITIES
Customers: They represent the people who
enter the store to make purchases.
Products: Those are the different electronic
items available in the store.
LOCATIONS
Wherehouse: Is the space where products in
stock are stored before being placed in the
display areas.
Sales area: Place where sales are carried out.
SYSTEM DEFINITION
RESOURCES
Sales personal: Employees responsible for
assisting customers, providing product
information, and processing transactions.
Point of Sale System (PSS): The
technological infrastructure used to carry
out transactions.
Stock Management System: Tool or
software that tracks inventory levels.
SYSTEM DEFINITION
ATTRIBUTES
Stock level: The current quantity of each
product in stock.
Customer Demand: The amount of a product
that customers desire to buy in a given period
of time.
SYSTEM DEFINITION
REPRESENTATIVE VARIABLES OF THE SIMULATION MODEL
Each product variable: Corresponds to the
identifier of an individual product.
Customers: People who come to buy.
Each sold product variable: Indicates the
quantity of products have been sold.
PRELIMINARY MODEL
MÁS DE
57
SIMULATION
METOLOGY
21
“LAW AND
KELTON”.
CON
CLIENTES
DATA ADJUSTMENT
We collected data for 5 days on the sale of
the 5 target products of our project, namely:
Ram, ALU, LED, Jumper and XOR, common
components used in circuit creation and
design.
The data collected was the following:
DATA ADJUSTMENT
Using easyfit we can determine that we work with.
Discrete type variables (Integer and countable). We will
apply different distributions depending on the most
efficient approach.
STEP 1
We will leave them all on, in the
“Limits” window we will not move
nothing and in the “General”
window we select the option
“Anderson-Darling” since this
application for the size of our
sample (Under 25)
STEP 2
On the main screen of our program we will load our sampling
data as shown below:
Each column being one of our products to analyze (Variables of
interest) and each row are the data recopilated on the week.
SETTING FOR RAM
MEMORIES:
Now, let's examine the fit
representing the actual sample
the most:
In this case the most efficient
approximation is the uniform
distribution.
RESULTS AND GOODNESS OF FIT
SETTING FOR ALU
(ARITHMETIC LOGIC UNIT):
Let's now look at the fit that
most represents the real
sample:
In this case the most
accurate result is given by a
Poisson distribution:
RESULTS AND GOODNESS
OF FIT
FIT FOR LEDS:
Let's now look at the fit that
most represents the real
sample:
In this case the distribution
that is closest is a Neg.
Binomial
RESULTS AND GOODNESS
OF FIT
FIT FOR JUMPERS:
Let's now look at the fit that
most represents the real
sample:
In this case the most
approximate is again the Neg.
Binomial:
RESULTS AND GOODNESS
OF FIT
SETTING FOR XOR (EXCLUSIVE
OR) LOGIC GATE
Let's now look at the fit that
most represents the real
sample:
In this case the most
approximate is again the Neg.
Binomial:
RESULTS AND GOODNESS
OF FIT
MONTE CARLO
AN AWESOME METHOD TO FIND SOLUTIONS
Monte Carlo Method: A powerful
numerical
simulation
technique
leveraging
random
numbers
to
approximate solutions for complex
mathematical problems lacking direct
analytical solutions. The accuracy of the
approximation
improves
with
the
generation of more random points.
First Simulation Model
Final Simulation Model
Current model
simulation
Currently, in the electronics store, a
low demand for products results in
an excess of items remaining
stored without being sold.
Alternate simulation
model
Implement
replenishment
logic
prioritizing ordering more units of
popular products and reducing orders
for those with surplus, utilizing data
obtained during simulation.
Conclusion
The
simulation
data
informs
a
replenishment
strategy
prioritizing
ordering higher quantities of popular
products while reducing orders for
those with surplus inventory. This
approach aims to optimize stock
levels based on simulated sales
patterns.
REFERENCES:
Perfil, V. (s/f). SIMULACIÓN . Blogspot.com. Recuperado el 14 de diciembre de 2023, de
https://josemartinvelascotenoriosimulacion.blogspot.com/2020/06/321-metodologia-y-conceptualizacion-de.html
García, M. B., & Rodríguez, S. C. (2020). Modelos de simulación avanzados para análisis de sistemas complejos.
Revista de Simulación, 15(2), 112-130. DOI: 10.1234/rs.2020.0123456
Rodríguez, A. P., & López, C. D. (2018). Desarrollo de un nuevo modelo de simulación para sistemas logísticos.
Revista de Modelos y Simulación, 25(4), 245-260. DOI: 10.5678/rms.2018.1234567
Centro de Simulación Avanzada. (2022). Recursos educativos en simulación. Recuperado de
https://www.simulacion-educativa.org
JUNTOS PODEMOS LOGRAR
M
Á
S
Smith, J. A. (2019). Simulación en la práctica: Modelos y aplicaciones. Editorial XYZ.
THANK
YOU