General Model Simulation of the Mexican Poultry Value Chain
Luis Antonio Calderón
1
and Pablo Nuño
2
1
Huatusco Institute of Technology, Veracruz, Mexico
2
Interdisciplinary Graduate Programs and Research Center, UPAEP University, Puebla, Mexico
Abstract: The use of simulation contributes to better analysis and understanding of the interactions between the major
variables of the Mexican poultry value chain. This general model of the value chain poultry considers
biological processes such as growth and reproduction, as well as economic factors and food costs operating
at different stages of industrial poultry farming. The program was designed for poultry producers in general
and for the Mexican poultry industry as an aid in the evaluation of different economic scenarios for the
production of chicken meat. Although this model is based on the Mexican poultry industry, this can be used
where operating conditions are presented here assumed. The integrated simulation model can perform
poultry simulations to establish correlations between various parameters of production and their output
under standard operating conditions at farms and stands. It can provide answers to parameters such as
growth and viability, the number and weight of birds at feedlots, the number of births and the total
production cost per kilogram. The model provides a holistic description of the production system and its
outputs, reflecting the random variation in the stalls and booths between birds, which is important to
represent the production risk. Thus, simulations of poultry value chain through this model can provide
answers to what would result if changes were made to specific production parameters.
1 INTRODUCTION
In Mexico, poultry can be classified as the livestock
industry with more historical background, before the
arrival of the Spaniards to the Americas, raising
poultry was practiced mainly in turkeys. With the
arrival of settlers, breeds and varieties of birds were
introduced to the conquered territories that were
adapted to the operating conditions of Mexico.
Production began on a small scale. It should be
noted that at the time of the colony, it was allowed
to employees of poultry farms to maintain self-
sufficiency, which is considered the origin of the
current system of backyard poultry or rural,
practiced in large parts of the country. The
predominant pattern of production and trade until the
early 50's consisted of medium and small farms
supplying urban areas, a system that was interrupted
by the outbreak of the Newcastle disease in México.
Following this event, the authorities in coordination
with producers developed an intensive poultry
development program, which marked the foundation
for the current poultry endeavor. It may be noted
that from the second part of the early 80's, technical
production has replaced both semi-technical
production and backyard that was practiced in areas
close to expanding urban areas. Currently, the
poultry sector is a branch of farming that has
reached a technological level of efficiency and
productivity, which is comparable to that of
developed countries, adjusting quickly to the levels
demanded by the population. In the past 10 years,
the poultry industry has experienced a phenomenon
of expansion that has led this segment to be the
second place in the consumption of meat produced
in México, and the lowest price meat alternative in
the country.
2 METHODS AND MODEL
DESIGN
It can be assumed for the purpose of the simulation
model, (Calderón Ph.D. Dissertation), that the
system of interest consists of a set of lots of birds
that pass through the stages of breeding and egg
production, to later produce batches of eggs entering
the stage of incubation. Once this is completed, a
new batch of birds is sent to the broiler process. A
lot of (13700 males and 4725 females) birds is
considered to be the transaction flowing through the
model for the purposes of this model, which must
complete their breeding cycles, egg production,
incubation and growth on the farm stands. Likewise,
637
Calderón L. and Nuño P..
General Model Simulation of the Mexican Poultry Value Chain.
DOI: 10.5220/0005118906370643
In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2014),
pages 637-643
ISBN: 978-989-758-038-3
Copyright
c
 2014 SCITEPRESS (Science and Technology Publications, Lda.)

lots of fertile eggs should complete its cycle in
incubation machines. Each process facilities, stalls
and setters have a finite capacity to determine the
number of transactions (lots) that are able to
accommodate for each of these elements within the
system, and in turn, each lot is composed of a
number of specific units at the corresponding stage
(birds or eggs). Each stage requires a specific time to
complete the cycle before moving on to the next
process. These cycles must be coordinated and
balanced, so that there is an adequate flow of
transactions in the system. The explanation of the
process just presented, forms the basis of the
conceptual model of the system under study. It is
noteworthy that the conceptual model can be used
for any poultry value chain under the assumptions
and conditions presented here. Figure 1 shows the
conceptual model approach to simulate the poultry
value chain.
Source: Own elaboration
Figure 1: Conceptual model.
For this study, the operation manuals and
performance objectives by Ross Bird (2001), form
the basis for the progeny of breeding birds as well as
for broiler. Such data also establishes the empirical
distributions of times of chick placement of birds,
the mortality of females and males at different
stages, the average weight of birds at the beginning
of the cycle, and its uniformity in breeding flocks,
posture and broilers. To estimate the probability
density functions, we associated empirical data
relevant to the above input, using the results of
operations for one year of a Mexican Poultry
Company operating under the conditions assumed in
this research work.
2.1 Conceptual Model Validation
Conceptual model validation is defined as
determining the theories and assumptions underlying
the conceptual model correctly, and that
representation of the problem in the model is
"reasonable" for the purpose required. The
verification of the model ensures the correct
implementation of the conceptual model and of the
computer program. Operational validation of the
simulation model is achieved if the model output
behavior closely resembles the real system having
sufficient accuracy for the intended purpose. The
validity of the data is to ensure that the data needed
for building, evaluating, testing and conducting
experiments using the model in solving the problem
are adequate and correct (Sargent, 2004). By using
earlier approaches to model the stages of breeding,
egg production, and broiler hatching we may
conduct trials in order to compare with available
historical results of poultry operations at each stage.
This is done in order to validate the theories and
underlying assumptions in the conceptual model, as
well as to validate the representation of the model
structure, its logic and to verify that the
mathematical causal relationships are “reasonable”
responding to the objective of the model. In order to
verify the simulation program in SIMNET II, we
performed several simulation runs having the model
already validated and under different conditions and
scenarios. The resulting values are compared against
the historical data available to determine whether the
computer program implementation is correct. The
behavior of the system model can be explored by
examining the output of various sets of experimental
conditions of interest. To support the verification
and complementing the above, the simulation
language SIMNETII provides a powerful TRACE
command to verify the logical flow of transactions
through the stages of the simulation model,
observing the consistency of input and output
relations, verifying also the internal consistency of
the model. Behavioral output is plotted for different
experimental conditions. The number of
experiments is determined according to the accuracy
required for the purpose for the model. Expert
opinion complements the determination of the
accuracy by providing feedback to contemplate
adjustments in the structure of the program code.
The purpose of this is to improve the representation
of the overall integrated model and the operation of
the submodels to reflect a better approximation to
the real system under study. In order to obtain
reliable results, the first step is a proper analysis of
the steady state of the system. This is achieved by
considering the stability of the response variables
with respect to the next process. We consider being
BREEDING
EGG
PRODUCTION
INCUBATIO
N
BROILER
1daychicken
Thereisa
ratioof13%
ofmalesover
females.
20weeks
later,egg
production
starts.
Average
production:
167eggs/bird
fromweek21
to25.10of
theseeggs
Average
hatching:
138
eggs/Bird.
18.5days
incubating.
2.5daysin
49days
cycle
OnAverage
thereisa7%

mortalityin
theentire
SIMULTECH2014-4thInternationalConferenceonSimulationandModelingMethodologies,Technologiesand
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at steady state if the current variability does not
affect the next process performance. Once this is
achieved, the selection of the number of runs is
determined based on the accuracy required for the
specific problem at hand. The length of each cycle
time in this case is several weeks, which leads to
evaluate horizons of operation cycles for at least one
year or more. The analysis of results considers the
overall operation of the integrated model but
focusing on the growing stage since it generates the
final value to the consumer.
3 EXPERIMENTATION
An experiment was carried out to determine if one or
more independent variables affects one or more
dependent variables and the reasons behind. From
experience and evidence of key causal relationship,
it was decided to manipulate the independent
variables but keeping track of the variation of the
dependent variable. This manipulation is
synonymous to the assignment of different values to
the independent variables (Hernández et al., 2003).
In this paper we have defined the critical
research variables (dependent variables) based on
the key strategic indicators of operation of the
poultry value chain under study, which include:
• The production of fertile eggs in the laying stage
• The density of birds in breeding stages, posture and
Broilers
• The final mortality in the breeding stage, posture
and
broilers
• The conversion of feed into meat in broilers
• The rate of productivity in the growing stage
• Production of kilograms of meat in broilers
• The cost of production in the breeding stage,
posture, hatching and broiler production.
Independent variables investigated are:
•
Initial number of breeding birds, egg production
and broilers
• Mortality in the 1st week of the broiler cycle
• Fertility rate, egg hatching at the incubation stage
• Food costs in the production broiler
• Direct and indirect costs of breeding, posture,
hatching and broiler stages.
The Model construction is based on established
performance standards for the Ross 308 breed type,
both for breeding and broiler birds. Ross 308 broiler
chickens have genetic characteristics for rapid
growth, efficient feed conversion and viability.
These broilers have strong legs and powerful
cardiovascular system. They are designed for high
meat production. Performance of birds can be
influenced significantly by many factors including
flock management, quality of feed, health and
climate conditions (Ross, 2001).
Six experiments were performed using the model.
The first one evaluates the amount of birds that
begin breeding at this stage to establish the
appropriate range of birds that provide the best
balance between increased productivity and lower
costs in the value chain. The second experiment
assesses the square meters required in the bird flocks
to provide a better first approximation of the range
of outcomes as a result of greater comfort and space
for the birds without increasing operating costs. The
third experiment focuses on mortality in the first
week of the cycle of broilers, which is critical in
poultry production. The fourth experiment evaluates
the range of values of key input variables that are
directly related to the birth of poultry production that
feed the growing stage. The fifth experiment
evaluates the operating range of values of the costs
of the feeding cycle with the least possible impact on
cost. Finally, the sixth experiment analyzes the range
of values for direct and indirect costs of operation
for all stages of the poultry value chain. The
followings variables are estimated by the simulation
model, which vary according to the case in study:
fertilized eggs production, density of birds in flocks,
dead birds at the end of each cycle, feed conversion
into meat, productivity rate, kilograms of meat
production, and complete cycle costs. Since then the
experiments mentioned above are illustrative. There
is a wide range of applications that can be tested
with the simulation model. For example, we can
perform simulations where changes could be
considered simultaneously, like an increase in the
number of birds at the beginning, with an increase or
decrease in the available area in the breeding flocks;
or separate experiments to find out the model
response to shifts of certain factors.
3.1 First Experiment: Number of Birds
at the Beginning of the Breeding
Stage
It is important to know the behavior of the breeding
and production stages. The number of birds at the
start is critical, in order to take the necessary actions
to prevent or reduce negative consequences such as
high mortality, excessive costs and low production.
It is assumed that increasing the number of birds at
the beginning of breeding in flocks, also increases
the number of fertile eggs in the laying stage, the
density of the flocks, and as a side effect to some
GeneralModelSimulationoftheMexicanPoultryValueChain
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extent rising mortality. The magnitude of these
increases can be estimated quantitatively by the
simulation model. In the following tests, it is
assumed that the capacity of the flocks is fixed, with
a standard value of 1800 m2 for both breeding and
egg production flocks. The simulation considers 9
flocks in the process of breeding and 16 flocks in the
egg production stage. It may consider any other
capacity of the bird houses, since is only necessary
to reflect these values in the simulation model set for
initial conditions. It is also assumed that the major
constraint of the breeding farms and egg production
is the available space for bird growth. This also
includes the associated costs of having more space.
To reflect the increase in the birds at the beginning
of breeding and egg production, the number of
flocks (male and female quantities) can be modified
in the attribute value of the corresponding
transactions, in order to evaluate what the system
can process at a real operating poultry company. The
results are the mean and confidence intervals for
fifteen observations using the simulation model and
under the prevailing conditions at a Mexican Poultry
Company that is taken as the basis of this research.
The summary of results for these tests is shown in
Table 1.
Table: 1: First Experiment.
Experiment
Density
(Birds/m2)
Fertile egg
production
Final
Mortality
Costs
No. of starting
Birds
6
6
7
19225
20629
22032
4234/1623
4544/1748
4855/1868
60/76.2
60/76.2
59.8/76
We performed 15 simulation runs in order to obtain
representative observations of the behavior of the
poultry value chain. Three levels for the number of
birds at the start were considered. The first level
(six) is based on the standard operation of a poultry
company in Mexico and increases at other levels are
taken into account according to any increases that
may exceed the current demand for the facilities
which were used as based values. By increasing the
number of birds at the start, increases the number of
fertile egg production, but as a logical consequence,
note that with an increase of the density of birds in
the flocks, produces a negative effect on the comfort
of birds, therefore reducing the space available for
breeding, which increases the number of dead birds
at the end of the cycle. On the other hand,
production costs decrease in terms of kilograms of
meat at this stage. It is noteworthy, that the main
purpose of this stage is raising healthy males and
females to produce as many fertile eggs as possible.
Therefore, what we seek is the balance between total
costs and the optimal production of fertile eggs.
Although this experiment only considered variables
or indicators of interest at the strategic breeding
stage, is of great importance because the output
stage are breeding birds (female and male healthy
enough for reproduction at sexual maturity). It is the
entrance to the position stage, and if the birds do not
arrive in optimal conditions, appropriate results may
not be obtained in these first two links of the value
chain. This will result in the underutilization of
poultry at the company. Accordingly, as a first
approximation to the final conclusions, we could say
that a controlled increase in birds at the start of the
stage of breeding under the conditions and
assumptions of this study, may lead to increase the
production of fertile eggs at a lower cost in the
production stage.
3.2 Second Experiment: Available
Area
The infrastructure of poultry varies from region to
region depending on the space and capital available
for each farmer. A common goal is to streamline any
poultry infrastructure to maximize its production
capacity. The dimensions of the stalls for breeding
and position are generally the same; this paper
builds on an area of 1800 m2. For broiler houses an
area of 2016 m2 was considered. Simulations assess
changes that could arise if there is more or less
space. Table 2 assesses the density for breeding
flocks, egg production and broilers in a smaller area,
and higher than the standard. Similarly, evaluating
the impact on mortality at the end of the cycle, and
for the case of the broiler stage, effects are observed
in the final kilograms production of chicken meat.
That is the main objective in the value chain for
poultry. The importance of space for the birds is a
direct function of comfort in the flocks where they
grow. Having the appropriate space and climatic
conditions, the desired production standards can be
achieved.
Table 2: Second Experiment.
Experiment
Density
(Birds/m2)
Final Mortality
Kg. Production
(Kg/1000)
Available
Area (m2)
Female
7/12/5
Male 3/4/2
Broiler
12/14/10
4234/4242/4227
1623/1630/1615
607/608/507
26556/26557/26556
12737
79500/79589/79669
The bird comfort directly affects the rates of poultry
mortality at any stage and represents a critical point
of attention in monitoring operations in poultry
production. It can be inferred form Table 2, that a
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larger production area with a low ratio of birds per
square meter, decreases the amount of mortality in
birds and consequently increases the production of
kilograms of meat in the final stage of broiler. As
mentioned earlier, these experiments as well as the
other ones are the results of basic operating
characteristics of a Mexican poultry company. The
generic simulation model developed has no limits
for these variables for future exploratory research
experiments.
3.3 Third Experiment: Broilers
Mortality at First Week
In the broiler growing stage, the first week is critical
for best bird’s performance. This situation arises
depending on the good or bad operating conditions
and health of birds in the first week which affect
considerably the end of the cycle and production
goals. The purpose is to get the best yields of poultry
operations along the entire value chain. A high
mortality rate in broiler farms or houses during the
first week will affect the operation of the booth
throughout the cycle and the conversion of the birds
and their productivity. The simulation model is used
to evaluate the effects of an increase in the mortality
rates during the first week of broiler chicken. It is
assumed that the standard mortality rate in the first
week of broiler´s cycle is between 0.2% and 0.3%
(based on observations taken from a poultry
company in Mexico). On the other hand, we
explored two different mortality rate ranges, from
1.0% to 1.1%, and also from 1.4 to 1.5%. These
interval values are specified in the initial parameters
conditions of the simulation model. The summary
results are shown in Table 3.
Table 3: Third Experiment.
Experiment
End
Mortality
Conversion
Productivity
Rate
Kg.
Production
Costs
First
Week
Mortality
608
769
830
1.65
1.66
1.67
377
371
369
79600
78765
78445
4.73
4.75
4.75
The simulation model shows that there are large
negative impacts as we increase the mortality rate in
the strategic indicators of the value chain. Increased
mortality results in poor conversion of birds, which
negatively affects the resulting productivity index of
the flock, and therefore the production of chicken
meat in kilograms, affecting costs. In the previous
simulation, the adverse effects are contemplated for
the first week only, assuming no problems the rest of
the cycle. For research purposes, it is possible to
explore both cases.
3.4 Fourth Experiment: Appropriate
Percentage of Births
A wide variety of experiments can be performed
with the simulation model with variables of interest
in the poultry chain, such as fertility rate, percent of
egg hatching, percent of defects, and the incubation
capacity represented by the number of hatching
machines. This will assess the main effects that are
directly related to the number of resulting births,
which will become healthy birds to enter the
growing stage. For this research, simulation
experiments were performed using fixed percentage
amounts, however the simulation model can handle
ranges of values in frequency distributions. It is
assumed that the standard values for fertility percent
of eggs produced in the laying stage is 92%. The
percent of eggs hatching handled in the incubation
stage is 92%. The percent of defects present in the
egg entering the incubator is 8% and there are 20
machines available for incubation. The Capacity of
these machines is 30240 eggs, which is the number
of eggs hatch by each machine. For the case of the
number of hatching machines, the simulation
experiments considered de minimum installed
capacity that is needed to process the number of
eggs produced in the previous stage position, under
current operating conditions. The critical variables
of interest or strategic indicators that were evaluated
due to the expected impact were fertile egg
production expressed in volume, the productivity
rate, production of meat in kilograms and the cost in
the early stages of laying, hatching and broiler
chicken.
Table 4: Fourth Experiment.
Experiment
Fertile egg
production
Productivity
rate
Kilograms
Production
Costs
Births
19226
18616
19819
377
378
377
79600
79585
79596
1.80
2.75
4.74
An increase in the fertility egg production not
necessarily increases the number of kilograms
production, and the cost per kilogram of meat
increases in the growing stage. It is noteworthy that
the numerical combination of selected variables has
a wide area of opportunity for experimentation. The
main purpose of this experiment is to evaluate the
main cause and effect relationships. Although this
experiment only shows part of the wide variety of
possible strategic indicators at the stage of
incubation, is of great importance since the outcome
of the incubation stage will be the input of the
broiler´s cycle, stage at which the birds should arrive
GeneralModelSimulationoftheMexicanPoultryValueChain
641

in optimal conditions. A first conclusion from this
experiment is that a high percentage of fertility is
necessary to achieve a solid production of meat in
kilograms, along with a smaller percentage of
defects in the next stage.
3.5 Fifth Experiment: Operating
Range of Feeding Costs
The common objective when discussing these costs
is to find the point where the marginal gain is the
greatest. Any poultry infrastructure to be
competitive needs to maximize their production
while reducing operating costs. In the present study,
the effects of feed costs are evaluated in the broiler
chicken, since they represent 70% of the total cost of
chicken production. The base value is a production
cost of 2.8 pesos per kg. feed given to birds in the
first week, 3.1 pesos per kg. feed during the second
and third week, 2.5 pesos per kg. feed supplied in
the fourth and fifth week of the cycle, and finally 2.8
pesos per kg. feed for the remaining weeks. Table 5
presents the results, evaluating higher and lower
values than the standard, showing the impact on the
final cost of chicken production. The effects
observed in the final production of meat kilograms
are directly proportional to the total cost of the
feeding cycle. The importance of cost control is vital
because it makes the difference between competitive
poultry companies.
Table 5: Fifth Experiment.
Experiment
Costs ( $ x Meat Kilogram)
Scenario 1:
Standard
Scenario 2:
Quality grains
Scenario 3:
Reprocessing
Broiler’s Feed
Costs 4.73 5.16 4.31
This experiment evaluates the scope or range of
competitive fluctuating costs, looking at pessimistic
and optimistic scenarios in the production and cost
of feed for birds. Table 5 shows that an increase in
feed costs increases the total production cost of meat
kilograms in the final broiler´s cycle stage. This
experiment as well as the other five are for
exploratory purposes, but are the guidelines for
future research experiments.
3.6 Sixth Experiment: Direct and
Indirect Costs
As the poultry full-cycle ends, the full picture of the
costs incurred in each of the stages of the poultry
value chain is obtained. In order to take action to
avoid or reduce costs, both direct and indirect, the
simulation model can be used to analyze the
scenarios, to obtain a first approximation of what
areas or activities should be monitored to prevent an
increase in operating costs. The scenarios considered
in this experiment are based on amounts present in a
poultry company in Mexico. In the following tests,
we included direct and indirect costs of breeding
stages, egg production, and broiler hatching. Direct
costs include: feed, gas, electricity, vaccines, straw,
cleaning, disinfection and direct labor. Indirect costs
include indirect labor, depreciation, maintenance,
and laboratory analysis. As mentioned above, the
simulation considers 9 flocks in the breeding stage,
16 flocks in the laying stage, 20 and 60 incubation
broiler houses. Of course, you may consider any
amount of poultry facilities. The ultimate goal of this
experiment is to take the first steps toward an
administrative structure that results in higher profits
for any poultry enterprise. In this experiment, as in
the previous ones, the results come from the average
of fifteen observations of the simulation model. The
summary of cumulative results for each scenario is
shown in Table 6. We considered three levels of
costs; the first level is based on the standard
operation of a poultry company in Mexico, the other
levels being an increment and a decrement from the
standard that might occur in a normal operation of a
poultry enterprise at a Mexican poultry company in
the state of Veracruz.
Table 6: Sixth Experiment.
Experiment Costs ( $ x Meat Kilogram)
Scenario 1:
Standard
Scenario 2:
Higher quality
Scenario 3:
Cost efficient
Direct and
Indirect
Costs
Breeding 76.27
59.99
Egg prod. 1.82
Incubation 2.77
Broiler 4.73
78.26
61.16
2.05
3.45
5.43
76.22
59.08
1.36
2.16
4.53
Observe in this experiment, the relationship of
fluctuations in costs. Subsequent tests with the
model can complement experiments to simulate the
behavior of the total operating costs, as a function of
critical variables of poultry operations. Production
costs of meat kilograms are critical and a
fundamental part of the poultry sector, determining
the gains or losses for companies at this point in
time. A successful industrial poultry operation
contemplates the right balance between costs and
optimal production. There is a wide variety of
analysis on costs across the entire value poultry
chain that can be addressed using the simulation
model. It can be used as the basis for evaluating the
use of poultry resources available at Mexican
SIMULTECH2014-4thInternationalConferenceonSimulationandModelingMethodologies,Technologiesand
Applications
642

companies, with the aim of increasing their
competitiveness.
4 SUMMARY
The simulation experiments presented above showed
that there are many areas of opportunity at each of
the poultry stages. Strategic indicators were taken
from the poultry value chain to assess their impact
and to establish initial cause-effect relationships that
could improve the overall results of operations in the
flocks and poultry farms for the production of
chicken meat. The final objective is to meet the
needs of consumers, and to increase the
competitiveness of the poultry sector to conquer new
markets.
For the first strategic indicator, the production of
fertile eggs, it is observed based on the input
variables, that the number of birds that begin at the
stage of breeding, the fertility rates, egg hatching
and the percentage of defects are major factors that
influence a high yield. The evaluation of the density
of birds in the stands, allowed us to evaluate the bird
comfort in their living space which has a direct
relationship with the number of birds at the start of
the simulation, and the square meters available for
birds in production. The main objective is to find
the right balance between these two variables for the
optimization of the poultry chain. The third strategic
indicator is mortality, which is set according to the
assumptions of the system under study. The input
variables directly affecting this indicator are: the
number of birds at the start, the available area for
breeding flocks, egg production, and broiler chicken.
For broiler chicken an important factor of great
weight is the evolution of birds in the first week of
the cycle, which directly affects the performance of
broiler houses at the end of the production of
chicken meat. The conversion is a strategic indicator
directly related to the growing stage, which is
affected by the mortality that occurs early in the
cycle. This indicator is also closely related to the
welfare of birds in the flocks, which requires control
and care of various factors, such as the climate and
the health of birds. For the purposes of this research,
the number of variables and indicators are the most
representative of a poultry operation. However,
there is a wide spectrum of research to be addressed
in subsequent projects. Finally, for the cost of
production of chicken meat kilograms, the mortality
behavior in the first week of the broiler’s cycle is the
main factor that directly affects the direct and
indirect feed costs of poultry operations.
5 CONCLUSIONS
Simulation is a good tool to get started and provide a
basis for holistic solutions. The simulation model
developed focuses on the core part of the supply
chain to evaluate strategic poultry production
opportunities areas for taking decisions to improve
the system-wide integrated poultry from producers
to consumers. The poultry industry faces challenges
with the opening of global markets. The simulation
model provides an effective mathematical support to
improve the growth of Mexican poultry companies
and their production operations at all levels.
REFERENCES
Aviagen, (2001), Objetivos de Rendimientos de
Reproductoras Ross 308, USA: Aviagen Incorporated,
pp. 2-10.
Calderón, L (2008), Ph.D. Disssertation: Simulation model
of the value chain in the Mexican poultry industry.
Hernández Sampieri, Fernández Collado, y Baptista Lucio
(2003), Metodología de la Investigación, México:
Editorial McGraw-Hill.
RISTIC, M., (2004), Meat quality of organically produced
broilers, Rev. World Poultry, Vol. 20 No. 8 2004, pp.
30-31.
ROBBINS, S. (1994)., Administración: Teoría y Práctica,
México: Editorial Prentice Hall Hispanoamericana 4ta
Edición.
Sargent, R., (2004), Validation and Verification of
Simulation Models, 2004 Winter Simulation
Conference, pp. 17-26.
SOLARES, A. M. G., (2004), El ALCA y su
trascendencia para la avicultura: Visión
Latinoamericana, Rev. Avicultura Profesional, Vol.
22, No. 7/8 2004, pp. 28-29.
TAHA, H. A., (1992), Simulation with SIMNET II, 2da
Ed., SimTec Inc. Fayetteville, AR, USA.
TAHA, H. A., (2003), Operations Research an
Introduction, USA:7 Ed. Prentice Hall, pp. 645-673.
UNA, (2005), Monografía de la Industria Avícola
Mexicana 2005, http://www.una.org.mx, Unión
Nacional de Avicultores, 2005.
URRUTIA, S. (1999), El Broiler del año 2001, Revista
Avicultura Profesional, 15 Ed., pp. 23-28.
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