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LUZ in its 130th
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Established since
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VOLUME 44
JANUARY - APRIL 2021
NUMBER 1
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, January-April, 2021, 21-28
industry of Sancti Spiritus
Damaris Taydi Castillo Jiménez
1
* , Higinia Bismayda Gómez Avilés
2
, Orlando de la
Cruz Rivadeneira
3
, Dariel Rivadeneira Casanueva
1
, Arelys López Concepción
1
,
Yadira Rodríguez Fernández
1
1
Dpto. de Ingeniería Industrial, Facultad de Ciencias Técnicas y Empresariales, Universidad de Sancti Spíritus
¨José Martí Pérez¨, C.P. 60100, Cuba.
2
Centro de Estudios de Energía de Procesos Industriales, Universidad de Sancti Spíritus ¨José Martí Pérez¨, C.P.
60100, Cuba.
3
Empresa Pesquera de Sancti Spíritus ¨PESCASPIR¨, C.P. 60100, Cuba.
*Corresponding author: damariscj@uniss.edu.cu
https://doi.org/10.22209/rt.v44n1a03
Received: 03 de marzo de 2020 | Accepted: 15 de septiembre de 2020 | Available: 01 de enero de 2021
Abstract
availability, quality and quantity of raw materials; also for the changes experienced by the quality of the product in the
different processes. The research shows the main results of the application of a procedure for the diagnosis of supply
performance. With the design of indicators, attributes were proposed to evaluate the quality of the raw material, based on
the quality index method.
Keywords:
Diagnóstico del Sistema logístico de aprovisionamiento de la
industria pesquera en Sancti Spíritus
Resumen
Las industrias pesqueras forman parte de cadenas de suministros complejas, por el limitado ciclo de vida del
pescado, la alta variabilidad en la disponibilidad, calidad y cantidad de las materias primas; además, por los cambios
que experimenta la calidad del producto en los diferentes procesos. El trabajo muestra los principales resultados de la
aplicación de un procedimiento para el diagnóstico de la logística de aprovisionamiento a la industria pesquera, mediante
un caso de estudio en una empresa de la provincia de Sancti Spíritus en Cuba. Para ello, se emplearon herramientas como el
índice de calidad.
Palabras Clave: cadena de suministro pesquera; mejora de la calidad; vulnerabilidad.
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
22
Castillo et al.
Introduction
Supply chain management facilitates integration
between the customers, the distribution network,
internal enterprise activities and the supply [1]. This has
a predominant role in the competitiveness of companies
Consequently, there is a prevailing need to eliminate
operations that do not add value in processes, in order
to minimize cycle time, to increase productivity and
minimize inventory levels throughout the supply chain,
and at the same time improve product quality, and
customer satisfaction as high as possible. As a result,
supply chains are very vulnerable to disturbances, due to
unforeseen events in each process [2]. Consistent with this
approach Van der Vorst et al. [2-4] refer that food supply
more vulnerable, as they are products with a limited
life cycle, and high variability in availability, quality and
quantity of raw materials, and also the fact that the quality
of the product can change as it is transformed through
the different processes. These vulnerabilities make the
management of food supply chains more complex when it
comes to strengthening their performance.
In general, the vulnerability of supply chains is
key performance indicators (KPIs). The performance of
supply chains generates post-harvest losses that affect
product quality, productivity and costs, among others [2].
In today’s competitive business environment,
supply chain performance is one of the most critical
issues in various industries [5]. Supply chains are complex
in themselves, each component that is part of them
involves details that are essential to take into account in
decision-making, with the most up-to-date and accurate
information from all members of the chain [6].
A supply chain is a network of enterprises
that produce, sell, and deliver a product or service to
a predetermined market segment. It not only includes
producers and suppliers, but also carriers, storage,
retailers and own customers, among others [7]. According
to Yared Lemma and Gatew [8] supply chains are
composed by four logistics systems: supply, production,
merchandising and inverse logistics. In perishable food
supply chains, the supply logistic system is where the
greatest losses occur, and their causes are different
from developed and developing countries; in the later,
approximately 64% of the losses occur in the supply
infrastructure (such as transportation and storage), and
techniques for harvesting, transportation and storage.
In developed countries, losses along the entire value chain
of food products range between 40% and 50% of this 42%
come of total food waste and in developing countries,
losses can be as high as 30% to 50%, and 40% of that
losses occur at the post-harvest and processing level [9].
Now a days Cuba is immersed in a process of
transformations of its economy, to lay the foundations
for the economic development that allows to perfect its
socialist social system. This process is taking place within
alimentary, energetic and environmental crisis; in an
increasingly globalized environment. At the beginning of
these transformations, certain symptoms are manifested
concept, and the new concept of economy functioning
that is beginning to be instituted. At present the
individual management of each enterprise do not result
in high competitiveness, for that reason it is necessary to
integrate the management of the supply chain [10].
The research integrates various tools that
aquaculture industry, their application permits to
strengthen the process and the quality index method
is proposed as an indicator of system management and
reliability.
Materials and methods
The procedure depicted in Figure 1, allowed
to carry out a diagnosis of the supply logistics to the
applied in the different stages such as Failure Mode and
Effects Analysis (FMEA); and Variant Mode and Effects
Analysis (VMEA) that allow to identify and to classify the
disturbances in the chain and the proposed indicators
also the use of the quality index method (QIM), as well as
Starting point
Characterization of the enterprise
Diagnosis of the selected process
Identification, classification and impact of disturbances
Analysis of the impact of the vulnerabilities detected in the
supply chain
evaluation of stages
End
Figure 1. Procedure for the diagnosis of supply logistics
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
23
Diagnosis of the logistics supply system
Characterization of the enterprise
A description of the enterprise under study and
the supply chain was made, followed by the creation of
specialists work team with knowledge about the supply
chain and the different logistics systems that comprise it,
to lead the implementation of the proposed procedure,
regarding the organization and the contribution of criteria
with experts providing assessments and recommendations
with maximum competence [12, 13]. This made it possible
to carry out a general characterization of the supply chain
and a graphic representation where each of the processes,
as well as to select the logistics system to study.
Diagnosis of the selected logistics system
Through Brainstorming, information is obtained
from the experts on the main causes of the problems that
the logistics system presents. A cause-effect diagram was
carried out, a tool that allows with increasing details,
graphically to show the possible causes related to a
problem or condition.
disturbances
With the graphic analysis of the process and
the assessment of the cause and effect relationships of
the problems detected in the diagnosis, disturbances are
disturbance in the selected logistics system through FMEA
[14, 15].
The analysis continues with the development
of VMEA, as a tool to determine the magnitude of the
disturbance, in terms of the variability that appears in the
process performance.
Impact analysis of vulnerabilities detected in the
supply chain
which made it possible to detect the existing relationships
among each of the aforementioned processes. Indicators
minimize variability of the quality characteristics.
For the design of the quality indicators, the QIM
with a system of score for demerits on a scale of 0 to 3 [16,
17].
of time, quantity and quality that characterize them are
declared, in correspondence with the results of the failures
Results and discussion
shown in Figure 2, the description of the systems involved
in its operation are described as follows:
Extensive and intensive
cultiva tion
Key Suppliers
Acuiza (Extensive)
Acuisier (Claria)
Jaulaspir (Til apia)
Another suppliers
FAR
MININT (Extensive)
Transportation Fishing industry Trader
wholesale customers
• Copmar
• Pescacaribe
• Caribex
• Pescavilla
• Pescasan
fish ma rket
• retail customers
• Population
• private sector
Supply logistics
system
Production logistics
system
Distribution
logistics system
Part of the Supply Chain in the Sancti Spíritus Fishing Enterprise
Figure 2. Representation of the supply chain
Next, with the opinion of the experts, the degrees
of relationship between the logistics systems are obtained.
Determining that the system to study is the logistics supply
system.
Diagnostics of the selected system
The supply logistics system in the enterprise
begins at the moment of capture and ends with the delivery
of raw material to the industry. In the investigation with
this system were analyzed, they are shown in Figure 3
Quality deterioration
Capture duration
Sensory analysis
Situation of the reservoir
Fishing arts
Characteristics of the boats
Temperature
Figure 3.
and impact, the FMEA was used, which allowed to analyze
the quality, safety and / or reliability of the operation in
each of the studied processes. An experts teamwork was
evaluating their severity, occurrence and detection,
allowing to calculate the risk priority number (RPN), to
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
24
Castillo et al.
prioritize the causes, in order to avoid the occurrence of
such failure modes. Table 1 shows the main processes
Table 1. Failures mode and effects analysis results.
Process steps Failure mode Effects of the ruling Causes of failure Current control RPN Corrective actions
Capture
Deterioration
of quality
characteristics
Microbiological
contamination of raw
material
High
temperatures
Visual analysis
504
Compliance with freeze and
cold storage regulations
based on ambient
temperature
Reception at
the shery
gathering point
Deterioration
of quality
characteristics
Microbiological
contamination of raw
material
High
temperatures
Sensory
evaluation
(quality index
method)
846
Compliance with freeze and
cold storage regulations
based on ambient
temperature
Transfer to
industry
Raw material
transportation
problems
Microbiological
contamination of raw
material
Environmental
contamination
Sensory
evaluation
(quality index
method)
486
Check that the transfer of
raw material complies with
the established freeze and
cold storage standards.
High
temperatures
846
Use of insulated sh
holder, boxes and proper
refrigeration mode
of the process, the VMEA was used. Brainstorming was
also applied to identify key product characteristics (KPC)
and sub KPCs. Fish quality is selected as KPC taking into
account the needs of the industry. The description of each
sub KPC is shown in Table 2 and allows us to understand
the characteristics that most contribute to KPC variations.
Table 2. Results of Variant Mode and Effect Analysis application.
Key Product
Characteristics
KPC
Sub-KPC Sensitivity NF Sensitivity
size
variation
(NF)
RPNV (NF)
RPNV
(Sub-
KPC)
Fish quality
Location of the
reservoir
9
Water
contamination
8 3 216
306
Food availability 5 2 90
Capture methods 7
Duration of
captures
10 5 350
574
Weather condition 8 4 224
Microbial growth 10 Temperature 9 8 720 720
Boats 5
Characteristics of
the boats
6 5 150 150
NF: noise factor; RPNV: risk priority number variation
The results of VMEA show the relative contribution
of each sub -KPC and each NF in a Pareto diagram (Figures
4 and 5). These graphical representations show that
microbial growth (sub-KPC) and temperature (NF) are the
quality characteristics. Secondly, there is the variability
of the reservoir situation (sub -KPC), in correspondence
with water contamination and food availability (NF). It is
for these reasons that improvement efforts are focused on
these areas. It can be seen that there are others sub -KPCs
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
25
Diagnosis of the logistics supply system
and NFs that their relative contribution to KPCs was not
on the consumption rates of fruit and vegetable products;
In this way, the situation presented by the
reservoir is visualized due to the contamination of the
existing water and food availability, which also generates
variability. Therefore, it is necessary to work on them to
improve the quality assurance of the enterprise supply
logistics.
Figure 4.
Figure 5.
Impact Analysis of vulnerabilities detected in the
supply chain
For the impact analysis of vulnerabilities, a
shown in Figure 6, where the following is revealed: the
causes of the disturbances, the relationship of each
relationship with their quality indicator. In this way, the
type of relationship of the variables with the system is
visualized, and the improvement actions that correspond
to minimize the quality characteristics variability.
Quantity indicator
Time indicator
Quality indicator
High tempera tures
Poor handling of raw
materials
Capture method and
fishing art
Environmental
contamination
Insufficient supplies
(boxes and ice)
Minimize
variability in
quality
characteristics
Figure 6.
of NF and Sub-KPC with the system to minimize the
quality characteristics variability.
For the impact analysis of vulnerabilities in the
supply chain, detected during the VMEA, a proposal of
indicators for time, magnitude and quality is made, as
shown in Table 3.
important parameters to perform the sensory analysis
with scores ranging from 0 to 3 (odor) and 0-2 (other
attributes), depending on the score of each characteristics.
The total sum ranges from 0 (total freshness) to 16 (total
loss of freshness).
Scenario evaluation
The scenarios behavior evaluation is carried out
and the variability provided by the VMEA, it is represented
in Table 5, which allows to show these disturbances effect
Time is taken as a reference to locate the
scenarios in the matrix, according to NF determined in
the current months since the tendency is to obtain high
In the capture process, failures in the methods
duration of the capture is prolonged and the boats do
cold storage and when exposed to high temperatures, it
increases microbial contamination. These disturbances
are located in scenario E6 in an altered regime, with
preventive and reduced response.
point is located in scenario E5, under an altered regime,
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
26
Castillo et al.
Table 3. Proposed indicators for time, quantity and quality
Process Calculation method Evaluation Legend
Reception at the
shery gathering
point
Good: IC = 0
Bad: IC > 0
Quantity indicator (IC)
Number of lots that do not meet the quality index
(CLNMIC)
Total lots (TL)
Collection point
Good: IT = 0
Bad: IT > 0
Time indicator (IT)
Number of lots that fail to comply with the time dened in
the deterioration curve (CLNTDCD)
Total lots (TL)
Collection point
Good: IQ = 0
Bad: IQ > 0
Quality indicator (IQ)
Quantity of sh that do not meet the quality index
(CPCIQ)
Total amount caught (CTC)
Reception in the
industry
Good: IQ = 0
Bad: IQ > 0
Quality indicator (IQ)
Quantity of sh that do not meet the quality index
(CPCIQ)
Total amount received (CTR)
Reception in the
industry
Good: IT = 0
Bad: IT > 0
Time indicator (IT)
Number of lots that fail to comply with the time dened in
the deterioration curve (CLNTDCD)
Total lots (TL)
The attributes to be considered as product quality
indicators for the quality index method (QIM) use are
shown in Table 4.
Table 4. Quality attributes to measure quality indicator.
Quality parameters Scores
General appearance
Color 0 - 2
Odor 0 - 3
Skin 0 - 2
Scales 0 - 2
Eyes
Shape 0 - 1
Pupil 0 - 2
Cornea 0 - 2
Gills
Color 0 - 2
Quality index
0 - 16
Table 5. Relationship matrix between time and quantity
indicators
Time indicators
Low
E0
Ideal regime
robustness
zone
E1
Normal regime
robustness zone
E4
Regime
interruption
Reduced
response
Medium
E3
Normal regime
Preventive
response
E1, E3
Transfer to
industry
Normal regime
Reduced response
(T,I,M)
E5
Fishery gathering
point reception
Altered regime
Reduced response
(MCU,T,I,M)
High
E3
Altered regime
Preventive
response
E2, E6
Altered regime
Preventive
response
E6
Capture process
Altered regime
Preventive and
reduced response
(MCU,T,I,M)
Low Medium High
Quality indicators
since the average time is taken and the quantity levels are
guarantee the quality of the raw material at that point in
the process, the necessary inputs supplies such as plastic
boxes and ice for cold storage must be guaranteed. The
transfer to the industry is located in the E1, E3 scenarios,
under a normal regime, with reduced response, with
medium time and quantity, to guarantee the quality of
the raw material, an adequate handling must also be
guaranteed, as well as the ice supplies and plastic boxes, to
maintain the proper temperature. The scenarios may vary
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
27
Diagnosis of the logistics supply system
under a given situation at a given time, such as weather
conditions, high capture volumes, and the necessary of not
available inputs.
Conclusions
As the research emphasizes, supply chain
managers need to be better equipped with the methods
of measuring and managing vulnerability. The designed
order to help assess the levels of vulnerability in the
supply chain and proactively manage the response to
be considered for each type of scenario. The procedure
studied the supply chain vulnerabilities depending on
the amount captures exposure time to high temperatures
and their impact on the quality characteristics, evaluated
through sensory analysis, achieving a direct relationship
in the management of the different processes. In this way,
top management obtained more reliable information
on the enterprise “health” and could assess whether the
philosophy of continuous quality improvement.
Acknowledgment
The authors thank the management of the
carrying out the investigation and help to alleviate the
vulnerabilities detected in the supply chain
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