Energy Optimization of the Post-Harvest Area of Roses in Quiroga,

Ecuador – A Comparative Analysis

Joffre Espin-Castro

, Saravana Prakash Thirumuruganandham

1,* b

, Hugo Yánez-Rueda

Erika Chuncha-Ashqui

, Doris Clavon-Taipe

, Bryan Conrado-Mestanza

Byron Abril-Flores

and Muthukrishnavellaisamy Kumarasamy

Centro de Investigación de Ciencias Humanas y de la Educación (CICHE), Universidad Indoamérica,

Ambato, PC180103, Ecuador

Facultad de Jurisprudencia Ciencias Políticas , Universidad Indoamérica, Ambato PC180103, Ecuador

Facultad de Ingeniería en Sistemas, Electrónica e Industrial, Universidad Técnica de Ambato,

Av. Chasquis and Rio Payamino, Ecuador

Soluciones Industriales B&L, Ambato, Ecuador

Civil Engineering Programme, School of Engineering, University of KwaZulu-Natal, Durban, South Africa

Keywords: Flexsim, Roses, Floriculture, Distribution, Timing, Bunches, Energy, Optimization, Simulation and

Modelling.

Abstract: This research document seeks to simulate the post-harvest area of roses in a floricultural company in order to

analyse improvement parameters. The study starts with the bibliographic and documentary methodology, as

well as experimentation. Time - movement study and the path diagram are used to build the simulation model

using FlexSim software and establish the probability distributions resembling a real process. Through the

analysis of the process, it was found that theoretically the Company produces 546 bunches of 20 roses per

week based on the bottleneck restriction. When it is compared with the simulation, there is an error of 0.08%,

resulting on an exact similarity to the real conditions. The simulation work concludes that by identifying the

bottleneck restriction respect to the pre-assortment and packaging process, which, although it has two stations,

it is the main limiting factor. Therefore, experimentation is developed to exploit the bottleneck, verifying that

with 3 stations the production is stabilized and a 4.39% increase in capacity respect to 546 units capacity

production. On the other hand, there will also be a 0.90% increase in bunches weekly.

1 INTRODUCTION

Rose production in the Netherlands has ecological

consequences and not negative social impacts unlike

in Ecuador. One of the culprits is the high energy

consumption of greenhouses (Franze & Ciroth,

2011). Another negative impact on flower production

are the different volcanoes. For example, the

https://orcid.org/0000-0001-5988-8233

https://orcid.org/0000-0003-4210-1363

https://orcid.org/0000-0003-1721-7524

https://orcid.org/0000-0002-5403-5378

https://orcid.org/0000-0002-8795-6915

https://orcid.org/0000-0001-5060-8063

https://orcid.org/0000-0001-8130-5815

https://orcid.org/0000-0003-1612-190X

Corresponding author

Cayambe volcano represents a potential threat to

populations that base their economy on floriculture

(Quinaluisa Morán, y otros, 2021). Ecuadorian

flowers have increased their participation in the world

market and exports, but in a slow way that places it

between the optimum and lost opportunities (Camino,

Andrade Diaz, & Pesantez Villacis, 2016). In

addition, the stems can be used as biosorbents (Said,

138

Espin-Castro, J., Thirumuruganandham, S., YÃ ˛anez-Rueda, H., Chuncha-Ashqui, E., Clavon-Taipe, D., Conrado-Mestanza, B., Abril-Flores, B. and Kumarasamy, M.

Energy Optimization of the Post-Harvest Area of Roses in Quiroga, Ecuador: A Comparative Analysis.

DOI: 10.5220/0011907200003612

In Proceedings of the 3rd International Symposium on Automation, Information and Computing (ISAIC 2022), pages 138-149

ISBN: 978-989-758-622-4; ISSN: 2975-9463

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

Tekasakul, & Phoungthong, 2019). Flower

production in certain parts of the country has weak

levels of organization and social capital. However,

over time the business model with the exemplary

dynamics of the world market will consolidate

(Martinez, 2013). The flower industry, in addition to

being a generator of employment and an indicator of

potential activities in the region, has an impact on the

environment (Simanjuntak, Soesilo, & Herdiansyah,

2020). The provinces that produce flowers in Ecuador

are Pichincha with 66%, Cotopaxi 15%, Azuay 6%,

Imbabura 5%, Guayas, 4% and another 8% (Pavón,

Andrade, Bernard, & Contreras, 2019). It is worth

noting that recently published data show that the

Netherlands is the largest flower producer in the

world, accounting for 49% of total world exports.

Ecuador is the third largest exporter with a 10% share,

while Colombia is the second largest player in this

market with a 16% share of global exports (Morán

Poveda, 2021), (Morocho-Aguirre, Cisneros-Aliaga,

& Soto-Gonzalez, 2021). These 3 players account for

75% of global flower exports. The data also confirm

that, according to the SAIKU system of SRI, 422

companies dedicated to the cultivation of flowers

throughout Ecuador have registered reported values

based on their economic activity in 2019. Tungurahua

accounts for 6.2% of the 422 companies located in

Ambato, Cevallos, Pelileo and Píllaro. Pichincha is

the province with the highest concentration of

companies, followed by Carchi, Cotopaxi, Imbabura,

Tungurahua and others (Sánchez, Vayas, Mayorga, &

Freire, 2020). A study on the participation and

competitiveness of flowers in Ecuador, based on

commercial development in the period 2010-2016,

especially indicators such as the comparative

advantage index, relative trade balance and import

intensity index, confirms that Ecuador is the most

competitive country in flower exports (Garcia &

Romero, 2020). Looking at the data of the

Association of Producers and Exporters of Ecuador

for the months of January-March 2019 - 2020, the

difference in foreign exchange sales and receipts is

remarkably large, mainly for two reasons:

1. the oversupply in the market as a result of an early

and abundant harvest 2. the emergence of the

coronavirus COVID - 19, which has triggered a

pandemic.

At the top of the list of those affected is the

floriculture sector, which is certainly the most

affected. The price per flower variety and the total

volume have decreased extremely. The figures are

currently unquantified, but are said to be approaching

an average of $1.5 million (Morocho-Aguirre,

Cisneros-Aliaga, & Soto-Gonzalez, 2021). In a global

context, we note that the Brazilian Institute of

Floriculture, in a brief report published as recently as

March 28, 2020, stated that sectors such as

floriculture are already feeling the effects of the

decline in sales two weeks after the introduction of

Covid-19 in Brazil. In an effort to revive growth, a

crisis committee was established in Brazil to relax

regulations for the floriculture sector, which suffered

at the beginning of the crisis (Beckmann-Cavalcante,

2020). There are already signs of recovery, with

adjustments for the use of technology and e-

commerce of flowers in direct connection with the

end consumer (Okumura, 2020). However, in

addition, various other strategies have been adopted

to prevent an even more drastic decline in the sector,

such as campaigns, revelations, and movements

through social networks (Rosa, 2020). As reported by

Reis et al (Reis, Reis, & Nascimento, 2020), in the

face of the crisis in the sector, organizations from

different sectors, both public and private, have tried

to inform the public, growers and technicians about

the situation of floriculture. Several live streams were

organized in which interviews were given about

floriculture, from production to marketing. These

movements were important to emphasize resilience,

that is, to adapt to change and make the wheel turn

again. Similarly, Taiwan's floriculture industry has

taken several measures to support its sector with

marketing campaigns focusing on 4 important

aspects: The government buying flowers to decorate

public spaces to encourage citizens to use more

flowers, such as an outdoor flower carpet show or

flower arrangements in train stations. 1. developing

new distribution channels closer to consumers' daily

lives, including flower stalls in supermarkets and

retailers to attract consumers. 2. promoting new ideas

for the use of flowers, such as the use of flower gift

boxes in temples. 3. developing consumers' habit of

buying flowers regularly. Taiwan's Council of

Agriculture (COA) has also proposed various rescue

and revitalization measures and advocates expanding

the world market in the hope of turning the crisis into

an opportunity to minimize the impact of COVID -19

on agricultural exports (Nieuwsbericht, 2020).

Addressing these challenges and combating

pandemics will require a joint effort between

governments, investors, and innovative agricultural

technologies. Agriculture 4.0 will no longer depend

on distributing water, fertilizer, and pesticides evenly

across entire fields. If production management and

produce trading can be augmented by low-cost, labor-

saving machinery, ancillary equipment, and sensor

components, along with the adoption of advanced

technologies such as ICT, IoT, Big Data, and

Energy Optimization of the Post-Harvest Area of Roses in Quiroga, Ecuador: A Comparative Analysis

139

blockchain, the goals of reducing agricultural stress

and labor demand, providing farmers with a more

efficient agricultural management model, and

mitigating the impact of the COVID -19 pandemic

could be achieved (Gunal, 2019).

At a worldwide level, organizations take into account

in a key way the continuous improvement systems

that focus on waste reduction as mentioned in the

Lean Manufacturing methodology, in which to

establish major changes to the company is too costly,

therefore different world-class organizations have

established the use of manufacturing simulators to

verify changes without costs and validate the

improvements proposed (Socconini, 2015),

(Bribiescas Silva & García Uribe, 2011).

The simulation of production processes is the

approach most used to design and analyse

manufacturing systems and is linked to the production

control system (Gunal, 2019), in which it allows the

reduction of costs due to leisure time and increase the

performance of its stations of work achieving a

balanced production that adjusts to the demand, in

addition to verifying significant growth in the market

(Aldás Salazar & Amán Morales, 2017), (Sánchez,

Ceballos, & Sánchez, 2014). Through simulation,

data on the operation of the production process are

collected, which makes it possible to estimate its

performance measures (Garcia & Romero, 2020).

This activity makes it possible to draw conclusions

about the behaviour of a system, studying the

behaviour of a model, whose cause and effect

relationships are the same (or similar) to those of the

original system (López, González, & Alcaraz, 2019).

Designing manufacturing systems that are tailored to

both production and market requirements is

becoming increasingly challenging due to variability

in demand (Luscinski & Ivanov, 2020), (Slack, 2005).

Another advantage of carrying out a simulation of the

processes is that it is possible to opt for models in

which the regulation options of the industrial sectors

that seek a transformation towards the eco-industry

are analysed while balancing the environmental and

economic effects; promoting further preferential

development of the ecology (Yu & Dong, 2019). For

this reason, simulation is an important tool that allows

solving problems that occur in industries (Bolaños-

Plata, 2014).

One of the simulation software that has been most in

demand among companies is FlexSim, which was

developed more than 20 years ago, so it has been

constantly improving with its interface and elements

that allow to achieve a real adequacy of the process,

since it has tools such as Experfit that allows the

creation of adjusted probability distributions based on

processing time, It is worth mentioning that large

companies such as Ford and even NASA use the

software to reduce costs due to bad decisions and

inefficient projects. Therefore, this software

contributes to the generation of almost real models-

of the industry with emphasis on the study of waiting

line systems, inventory models, investment models,

cash flows, logistics, and quality, among others

(Choque, y otros, 2013), (Castellanos, 2016), (Bruno,

2021).

Interest on improve productivity of manufacturing

companies has been growing in Latin America,

especially due to the rapidity of process and

machinery innovation, in addition to the growth of the

sector in general, which in turn generates greater

competition. Faced with this search to increase

productivity and eliminate waste, in many cases one

of the various tools that the Lean Manufacturing

methodology is used, either to reduce downtime,

machine maintenance, and quick tool changes,

improve quality (Turin, 2021), (Inkábova,

Andrejovská, & Glova, 2021). The performance of

the agricultural sector is linked to the tax system,

affecting profitability (Ramírez, 2021).

In addition, it allows to implement a quality

management system that leads organizations to

achieve excellence, superior performance that allows

differentiation and be increasingly competitive,

anticipating and achieving stakeholder satisfaction

(Ramírez, 2021). The goal is to keep productivity and

efficiency levels as high as possible, through careful

control and dosing of the other variables that can be

measured during an industrial optimization process

(Chen, Feng, Yang, Zhang, & Wei, 2021).

In Ecuador, the use of this system is not common,

therefore, it has been necessary to turn around the

traditional way of operating companies and optimize

production processes, in addition, eliminate the waste

that is generated in it, using Lean Manufacturing

Tools (HME), which considers the concepts of

activities that add value (AAV) and activities that do

not add (ANV), and other aspects within the process

(Curillo, Saraguro, Lorente, Ortega, & Machado,

2018), (Coronel, 2019).

The Company that have years in the export market is

reflected in problems with high costs of labor and

materials, so it is necessary to improve conditions

without reducing capacity and increase profits, but

verifying the performance of the areas and the

bottleneck or restriction of the system, so that through

simulation it is possible to achieve improvements of

great utility and ensure the sustainable exercise of its

operations (Hidalgo, 2019). One of the main markets

is the North American market, which has doubled its

ISAIC 2022 - International Symposium on Automation, Information and Computing

140

imports of Ecuadorian flowers in the last 20 years

(Loyola, Dole, & Dunning, 2019). The development

of products tailored to individual customer

requirements is essential if the company wants to

compete effectively in the domestic and global

market (Zywicki & Rewers, 2020). Due to the

location of the Company, it is possible to choose to

implement the concept of ecological industry, which

contributes to sustainable development (Liu & Ling,

2020).

The aim of this paper is to present the simulation 3D

model based on time and movements study exploding

the bottleneck to increase the production capacity

doing and experimentation with pre-assortment and

packing stations. We show the process map of the

company, flow diagram of the process, build 2D and

3D layout of the company, the bottleneck to exploit,

distribution of input data to do the simulation,

simulation of each process of the company, results of

operator utilization, the units on work in process,

water consumption on the simulation, experiment by

increasing the stations to destroy the bottleneck.

The rest of this paper is organized as follows: Section

II mentions the materials and methods used; Section

III presents the results and discussion, while Section

IV contains the conclusions.

2 MATERIALS AND METHODS

First, AutoCAD 2019 is a commercial software

application that based on computer aided design

(CAD) and drafting. This program is used to create

layouts in two or three dimensions (2D, 3D). Also,

AutoCad has many tools for different applications.

Second, Microsoft Excel is a commercial software

used to organize numbers and data with functions and

formulas. Excel can create graphs based on data

organized from different cells. Next, Minitab 18

software is a program for Higher Education and

Industry. Minitab supports on statistics analysis,

graphics capacity, quality evaluation and experiment

designer. Finally, FlexSim 2019 Software is a

commercial program to do 3D simulation, modeling

and analysis. This software help to understand

systems and take best decisions. Also, FlexSim allow

to experiment with the model based on data. FlexSim

has contributed with world-class applications in

healthcare issues, logistics systems such as container

operations in ports, simulations distributed in various

teams within a manufacturing company, in mining, in

aerospace centers and has even been adapted to the

industry service (hotels, hospitals, supermarkets, or

many other industries) to simulate the administration

and operation of human resources. FlexSim is a key

tool to improve results by giving correct answers to

the problems raised (Simón-Marmolejo, Santana-

Robles, Granillo-Macías, & Piedra-Mayorga, 2013).

FlexSim software allows to create accurately models

and understand basic system problems without

complicated programming. It because FlexSim offers

a simple way to develop the simulation model. Some

reasons why FlexSim is a good alternative as a

simulation tool are:

• Pre-built section allows to tackle much more

complex situations without having to write software

code.

• The software is object-oriented which

supports greater visualization of the production flow.

• The entire project is developed in a three-

dimensional environment (3D), in addition to

allowing the import of countless objects from

different design packages, including AutoCAD,

ProE, Solid Works, Catia, 3D Studio, AC3D, Rivit,

Google Sketch-Up and more.

• Not only discrete systems can be simulated,

fluid simulation or continuous-discrete combined

models are also supported.

• The generation of different scenarios and

different conditions are easy to program.

• Probability distributions can be represented

with great precision instead of average values to

accurately represent reality.

Floricultural companies in Ecuador are affected by

the absence of improvement studies to the restrictions

of their processes and optimize production capacity to

meet the demand in the domestic or international

market causing high costs of labor and materials. It is

necessary to improve the conditions without reducing

capacity and increase profits, but verifying the

performance of the areas and the bottleneck or

restriction of the system. Applying the documentary

bibliographic research, we seek to investigate the

historical background and characterization of the

company, establishing a field research which will

allow to extract data and direct information from the

company, through the collection with the use of

techniques such as time collection cards. In the same

way, applying the descriptive and experimental

research, the analysis and description of the data

obtained from the simulation by means of the tools is

Flexim software. In addition, allow to simulate

processes in real time without affecting the existing

production process is performed. The study is focused

on the operational process. The manufacturing

simulation is used in the organization’s rose post-

harvest area. First, there is the process of Admission

or reception of roses. Secondly, Immersion

Energy Optimization of the Post-Harvest Area of Roses in Quiroga, Ecuador: A Comparative Analysis

141

corresponds to submerging the roses in tanks with

different chemicals to eliminate bacteria. On this step

for future research, simulate a water distribution

piping system to evaluate alternatives strategies for

improving water quality could be possible because

Flexsim allow it too (Susanto, Amrina, Purwanto,

Pruto, & Yochu, 2020). Thirdly, the reception takes

place in a cold room to preserve the shape of the roses.

Followed by the flower web output to process where

all the elements are registered by computer. Then the

classification of roses and elimination of waste

manually. Then the Bunches in which a sheet, leagues

and codes are placed. Next is the web flower

fingering where you check the number of roses. This

is followed by the stem cutting. Subsequently, the

encapuche helps to protect the roses. Later, hydration

is performed to preserve the condition of the roses.

Then, the pre-assortment where the roses are

classified according to the orders, followed by bunch

packing and the wrap. Before finishing, the roses are

subjected to low temperatures in forced cold. Finally,

the dispatch of roses to the consumer and export of

the different types of roses that arrive based on

customer demand and orders.

3 RESULTS AND DISCUSSION

Fig.1 shows the process map according to

management system. The process map is a tool that

allows to represent the relevant processes to satisfy

the customer and achieve the company’s objectives

(Salvador-Hernández, Llanes-Font, & Velázquez-

Zaldívar, 2019). This method contains the strategic

processes that provide guidelines to the other

processes. Strategic processes support decision

making, while operational processes are those that

have a direct impact on customers, creating value for

them. Finally, the support processes support the

operational processes, providing the means or

resources for the latter to be carried out (García-

Dunna, García-Reyes, & Cárdenas-Barrón, 2013).

The study is focused on the operational process. The

manufacturing simulation is used in the

organization’s rose post-harvest area. First, there is

the process of Admission or reception of roses.

Secondly, Immersion corresponds to submerging the

roses in tanks with different chemicals to eliminate

bacteria. Thirdly, the reception takes place in a cold

room to preserve the shape of the roses. Followed by

the flower web output to process where all the

elements are registered by computer. Then the

classification of roses and elimination of waste

manually. Then the Bunches in which a sheet, leagues

and codes are placed. Next is the web flower

fingering where you check the number of roses. This

is followed by the stem cutting. Subsequently, the

encapuche helps to protect the roses. Later, hydration

is performed to preserve the condition of the roses.

Then, the pre-assortment where the roses are

classified according to the orders. Followed by

bunches packing. Followed by the wrap by bunches.

Before finishing, the roses are subjected to low

temperatures in forced cold. Finally, the dispatch of

roses to the consumer and export of the different types

of roses that arrive based on customer demand and

orders.

Figure 1: Process Map of the Company.

With the purpose of developing the simulation of the

process, the routing diagram is based on the processes

described above, mentioned on methods. For the

development of a real representation of the

organization, we used the layout of the routing

diagram with the measurements elaborated in

AutoCAD software. Layout and 3D objects were

added to FlexSim software presented (Fig 2).

Figure 2: 3D Structure of the Company.

ISAIC 2022 - International Symposium on Automation, Information and Computing

142

Figure 3: Average time and standard time for a 20 rose

bonche.

Fig.3. Once the times and movements for a package

of 20 roses are obtained, they are evaluated by

process and it is determined that the packaging

process is the one that presents the longest times with

an average of 442.81 seconds. In addition, its standard

time is 491.52 seconds.

Figure 4: Post-harvest process bottleneck.

Fig.4 represents the bottleneck analysis and it shows

that the restriction of the entire area is the Pre-

assortment and Packaging process with an hourly

capacity of 13.66 bunches. Considering that the

simulation is developed for a period of one week, the

following calculation is performed to determine the

number of bunches at the end of the week, the

following calculation is applied:

Therefore, we have a theoretical weekly value of 546

bonuses per week established by the system

restriction, which is the base value to check with the

current simulation model.

Fig.5 shows the Beta and Erlang probability

distributions as a function of time. It is used for the

entry arrival or admission of roses. The Erlang

probability distribution is a continuous random

variable. This probability is represented by an

equation known as probability density function to

know the cumulative function of the random variable.

This distribution is 87.93% coupled, because there is

the possibility of having infinite inter-arrival times of

roses, which are situations far from reality

(Hermenegildo, 2010). However, the Beta probability

distribution is more closely fitted to the data. When

comparing the frequency histogram and Beta

distribution with the values simulated in FlexSim, we

have an “Error” in the model mean with respect to the

sample mean 6.9160e-4 which is 0.00%.

Corroborating that the Beta probability distribution

fits 100%.

Figure 5: Creation of probability distributions and

probability plots.

Figure 6: Inbound Arrival Distribution

Fig.6 shows the Beta probability distribution which

best matches the random variables generated, having

a discrete model. This model analyzes the sampling

of the number of bunches that arrive from the harvest

area in the period of one week, allowing to simulate

this variable (Simón-Marmolejo, Santana-Robles,

Granillo-Macías, & Piedra-Mayorga, 2013). In

addition, the frequency histogram of the admission

arrival is presented with the distribution adjustment,

where the frequency histogram of the simulated

Energy Optimization of the Post-Harvest Area of Roses in Quiroga, Ecuador: A Comparative Analysis

143

values is compared and it is observed that the

frequencies do not differ significantly (Salvador-

Hernández, Llanes-Font, & Velázquez-Zaldívar,

2019). To corroborate the distribution, the Anderson-

Darling test is carried out, confirming the correct

operation of the simulation in Flexsim (Simón-

Marmolejo, Santana-Robles, Granillo-Macías, &

Piedra-Mayorga, 2013).

Figure 7: Sorting process distribution (Beta).

Fig.7 presents a beta distribution which adjusts to the

data of the random variable of the classification

process, this figure coincides with Fig 5., when

comparing the frequency histogram of the arrivals to

the classification process with the adjustment from

the distribution and the frequency histogram of the

simulated values, it can be seen that the frequencies

do not differ significantly (Salvador-Hernández,

Llanes-Font, & Velázquez-Zaldívar, 2019). This

distribution is used to model the behaviour of the

random variable with a finite lower and upper bound

(García-Dunna, García-Reyes, & Cárdenas-Barrón,

2013); which was determined using the Anderson

Darling test to corroborate that the random variables

come from the beta probability distribution (Simón-

Marmolejo, Santana-Robles, Granillo-Macías, &

Piedra-Mayorga, 2013).

Figure 8: Sorting process distribution (Gamma).

Fig.8 shows a Gamma distribution fitted to the data

of the random variable of the hydration process. This

distribution represents the problems of waiting times

in the shipment of bunches from hydration (González,

Galvis, & Hurtado, 2014). In this model the bundles

are processed in series without waiting in a process

with a common processing rate, for this reason the

Gamma distribution fits in the best way (Altiok &

Melamed, 2007). The software run to represent a

week with 8 hours and 5 days (144000 seconds). As

shown in Fig 9, The Admission goes with a total of

715 units but only 571 units are sent to the process

from the web flower output . It means that 144 units

were remaining in the process on the input area. The

distribution to the classification process is sent in a

balanced way of 20% (5 classification stations are

100%). The outputs of the bands in the web typing

processes have a total of 570 rose bonches, so when

it are sending to the encapsulation process only 569

units arrive. Only 1 bonche is left in process. Then, it

goes to the pre-sorting and packaging process which

it has two workstations. Finally, in a week 546

bonches are produced. Therefore, a comparison is

made with the theoretical balance and it is verified

that there are 546.47 bonches per week, so it is

similar, although it is worth mentioning that due to

the distributions that are handled, the amount

produced will always vary, so it is estimated to have

an error of 0.08%, resulting in a simulation similar to

the real conditions limited by the restriction of the

system. From Fig 10, The results obtained regarding

the state that prevails within the processes, indicating

that bottlenecks are the ones that are most loaded with

work, as it is determined that the pre-assortment 1 and

2; They are the ones that dominate in 97.87% of

processing, followed by the web exit to the process

with 96.77%, so in this way it is attributed and

verified that it is necessary to act on the bottleneck

and improve it.

We note that, Fig.11 the operator who develops more

activity is the storage operator with 25.46%, followed

by the classification operator 2. Since the processes

are distributed more for the areas where the

percentage of distribution is higher, so that operators

tend to move more in transport, in this way it can be

greatly improved by reducing stations for the demand

that is needed. The values of the queues reach more

than 150 and are increasing because the capacity of

the restriction process limits. It continues to

accumulate throughout the week. Also, it denotes

peaks due to the schedules of the logistics of the

production shipment. So, when it is compared with

the Little’s Law, it is verified that for each cycle 3.41

bonches are left in the process, which is consistent

ISAIC 2022 - International Symposium on Automation, Information and Computing

144

with the total number of bonches in process during

the week.

Figure 9: Throughput of the process.

Figure 10: Process status results.

Figure 11: Operator utilization

Therefore, it is possible to consider parameters that

can reduce the work in processes and have more

finished product. Experimentation is carried out to

vary how the production process would work with

more stations.

Figure 12: Water Consumption.

Fig.12 shows that the water consumption of the

immersion process is 4579.20 liters per week, which

in comparison with the reality of 4600 liters has an

error of 0.45%. The hydration process is also

analyzed with 7632 liters in relation to the normal

consumption of 7700 liters, showing an error of

0.89%. Both cases shows a similarity to the real

conditions of the process.

Figure 13: Capacity per week.

Improvement scenarios

Simulation in FlexSim allows the development of

flexible scenarios that are easy to modify, which is

why in this case the following scenarios are

established to verify the improvement that can be

executed in the real process, in which there are:

• Explode the bottleneck with 3 pre-fill and pack

stations.

Energy Optimization of the Post-Harvest Area of Roses in Quiroga, Ecuador: A Comparative Analysis

145

• Level the process with exploitation of the

second bottleneck in the web flower output process to

the process with 2 stations in conjunction with

scenario 1.

• Establish an additional half shift of 4 hours of

work on Saturdays from 8:00 am to 12:00 pm.

Improvement scenario 1

Using experimenter tool, variants are executed with

respect to the bottleneck, in which the capacity is

exploited to verify said operation. To note that, from

Fig 13, the results of experimentation that means with

3 pre-supply and packing stations a maximum of 570

bunches per week can be reached, after that it

stabilizes. It is the maximum that the bottleneck can

be exploited. With 3 stations a capacity increase in

4.39% (100(570-546)/546) and it is achieved respect

to Cp. Increase more than 3 stations will have the

same result, a maximum of 570 units. Process

simulation models are very effective tools to identify

process bottlenecks and to improve process

parameters (Straka, Tausov, Rosov, Cehlar, &

Kacmary, 2020).

Improvement scenario 2

It is additionally used to place 3 pre-supply and

packing stations, the placement of a second web

flower outlet station to the process. Also, run the

model for one week working, denotes an increase in

capacity of 823 bunches per week, by placing 3

stations in the first bottleneck in the pre-supply and

packaging process, and additionally 2 stations in the

web flower output to the process., for which an

improvement of 277 weekly bunches is obtained,

which represents an increase of 50.73%, although it

should be noted that it would lead to high costs, which

must be linked to the demand factor of the

organization to choose with the decision to place

additional jobs globally.

Improvement scenario 3

From the current situation, the modification of the

work schedule is made by adding a 4-hour shift from

8:00 a.m. at 12:00 p.m., in which it allows to verify

the increase in capacity by extending the working

day, for which the modification is used through the

FlexSim. In addition, it allows the management of

employees’ working time in a designated area or

activity (Borkowski, Czajka, Pluta, & Suder-Debska,

2016).

Figure 14: Results of improvement scenario 3.

Through Fig. 14, a weekly production of 603

bunches, so that with respect to the initial conditions

there is an increase in capacity of 57 bunches,

indicating an increase of 10.44% which is a positive

value for the organization without considering high

costs by incorporating 2 workstations, in addition to

adapting to current demand.

4 CONCLUSION

The floricultural Company has 14 processes for the

post-harvest area of roses, in which the study of times

and movements was developed, resulting in a

production capacity of 546 bonches of 20 roses for

one week, established by the production limit in the

process of pre-assortment and packaging. Through

the analysis of queues with Little’s Law it was

determined that in the process there are 3.41 bonches

avarage for each cycle of the process, meaning that

the queue is minimum before the bottleneck. By

developing the simulation model through the use of

the structure of the organization both 2D and 3D, a

representation similar to the real part was achieved,

also through the use of the experfit tool, the time

distributions were determined for each process within

the floriculture, schedules, failures, use of interfaces

and fluids with Floworks library creating a process

similar to the real one. Running the simulation

production and bottleneck analysis data were

reflected, showing a production of 546 finished

bunches. Comparing to the theoretical part, there is an

error of 0.08%, defining a great accuracy process. It

was determined through experimentation that the pre-

supply and packaging process that defines the

production can improve its capacity up to 570

bunches per week stabilizing the line with 3 stations.

Comparing with the real situation of 546 units, there

ISAIC 2022 - International Symposium on Automation, Information and Computing

146

is an improvement of 4.39% on the capacity. The

consumption of water in immersion is 4579.20 liters

and in hydration is 7632 which are similar to the real

conditions of the process with errors less than 1%.

ACKNOWLEDGEMENTS

Financial support from the seed grant “Computational

modeling of biomaterials and applications to

bioengineering and classical and quantum machine

learning for predicting social engineering (2022–

2026, code: INV-0012-042)”, Universidad

Indoamérica, Ecuador, awarded to S.P.T.

CONFLICT OF INTEREST

The authors declare no conflict of interest.

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