ppi 201502ZU4659
This scientific digital publication is
continuance of the printed journal
p-ISSN 0254-0770 / e-ISSN 2477-9377/ Legal Deposit pp 197802ZU38
UNIVERSIDAD DEL ZULIA
An international refereed journal
indexed by:
de referencia y comentarios:
• SCOPUS
• SCIELO
• LATINDEX
• DOAJ
• MIAR
• REDIB
• AEROSPACE DATABASE
• CIVIL ENGINEERING ABTRACTS
• METADEX
• COMMUNICATION ABSTRACTS
• ZENTRALBLATT MATH, ZBMATH
• ACTUALIDAD IBEROAMERICANA
• BIBLAT
• PERIODICA
• REVENCYT
DE LA FACULTAD DE INGENIERÍA
REVIST
A TÉCNICAREVISTA TÉCNICA
“Post nubila phoebus”
“After the clouds, the sun”
“Post nubila phoebus”
“After the clouds, the sun”
LUZ in its 130th
anniversary
Established since
1891
LUZ in its 130th
anniversary
Established since
1891
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, 51-58
Improvement to quality management in supply logistics.
Dariel Rivadeneira Casanueva
1*
,Orlando de la Cruz Rivadeneira
2
, Damaris Taydi
Castillo Jiménez
1
, Higinia Bismayda Gómez Avilés
3
, Arelys López Concepción
1
,
Alain Ulloa Zaila
1
1
Dpto. de Ingeniería Industrial, Facultad de Ciencias Técnicas y Empresariales, Universidad de Sancti Spíritus
“José Martí Pérez”, C.P. 60100, Cuba.
2
Empresa Pesquera de Sancti Spíritus “PESCASPIR”, C.P. 60100, Cuba.
3
Centro de Estudios de Energía de Procesos Industriales, Universidad de Sancti Spíritus “José Martí Pérez”, C.P.
60100, Cuba.
*Corresponding author: darielrc@uniss.edu.cu
https://doi.org/10.22209/rt.v44n1a07
Received: 01 de julio de 2020 | Accepted: 20 de octubre de 2020 | Available: 01 de enero de 2021
Abstract
Keywords:
Mejoramiento a la gestión de calidad en la logística de
aprovisionamiento. Caso de estudio: empresa pesquera
acuícola
Resumen
La investigación se realizó en una empresa pesquera de Sancti Spíritus, con el propósito de mejorar la calidad en
el sistema logístico de aprovisionamiento de productos acuícolas, mediante modelos predictivos de calidad. La propuesta
temperatura de almacenamiento en el sistema logístico de aprovisionamiento. En el transcurso de la investigación y en
encuestas, consulta de documentos, entre otros. Se obtuvo como resultado la relación entre las curvas de deterioro y el
tratamientos 1, 2, 7 y 8, creándose las condiciones para la toma de decisiones, en la evaluación de la calidad del pescado
Palabras clave:
pesqueras.
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
52
Rivadeneira et al.
Introduction
balanced and healthy diet, mainly due to its valuable
acid, and eicosapentaenoic acid. In addition to this, its
proportions iodine, magnesium, phosphorus, and zinc. To
the consumer is increasingly evident [1].
high content in certain constituents such as water, with
where bacteria actively participate, carrying out processes
and the time, which has resulted in an immense loss and
quality and nutritional attributes, avoiding waste and
losses [5].
above all, at reducing these enormous losses, preserving
logistical supply system [2].
review [7, 8], the main losses in the quality characteristics
carp, based on storage time and temperature in the Cuban
harvest losses, constituting this the problem to solve. As a
decisions that will reduce post-harvest losses.
Experimental
the problematic situation, the research problem was
answered by designing a procedure to obtain a predictive
quality model, based on microbial analysis, total volatile
The procedure proposed in Figure 1 goes through 8 stages,
partially applying it up to stage 3.
Start
Assembly and Training of the work team
Determination of the factors to be controlled. Desing of experiment
Identify the relationship between deterioration curves and the QIM
Document the traceability system
Determine the traceability diagram
Implement the traceability system
System audit
Proposals for improvement
End
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
53
Improvement to quality management in supply logistics
step, the procedure approved in the NC 49:1981 [9] is
done. The panel should be between 7 and 15 to maintain a
a binomial distribution is assumed [10]. For this purpose,
Equation 1 is used:
level.
and its durability are determined through methods and
tools to be applied, such as: brainstorm, Delphi method
1. Determine the quality problem.
2. Determine factors to be studied or investigated.
3. Choose the response variables to be measured.
4. Select the appropriate experimental design for the avail-
able factors and the objective of the experiment.
5. Planning and organizing experimental work.
6. Carrying out the experiment.
7. Statistical analysis.
8. Interpretation.
9. Final conclusions.
to calibrate and support mathematical or computational
models that are related to the phenomenon under
investigation [12].
In stage 3, to determine the relationship between
logical association between the variables to be measured
table mapping.
To document the traceability system in stage 4,
Table 1.
material according to its origin.
Source
Fishing
point
Fishing
brigade
Code (raw material)
Dam X
X
1,
X
2…,
X
n
Brigade Y 01: X 01: X
1
01: Y
Dam X
X
1,
X
2…,
X
n
Brigade Z 01: X 02: X
2
02: Z
Dam A
A
1,
A
2…,
A
n
Brigade Y 01: A 01: A
1
01: Y
Dam A
A
1,
A
2…,
A
n
Brigade Z 01: A 01: A
1
02: Z
Table 2.
Company logo
Species
Tench Carp Tilapia Clarias
Time of capture
Collection point
reception time
Industry reception time
Lot number L- : ________________
Table 3.
Parameter
Usefulness.
Name of the
sh
Identication of the species.
Source
Identication of the reservoir where it was
caught (origin of the sh).
Lot
Identication of the shing brigade that made
the catch. Delimitation of responsibilities.
Capture
method
Fishing gear used (working methods and
styles).
Exposure time
To know the exact time that elapsed from
capture to Reception at the Collection Point
in the aquaculture shing establishment and
then to Reception in the aquaculture shing
industry.
Characteristics
of the sh
To state the characteristics that affect sh
quality and safety. Determine the level of
deterioration of the raw material.
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
54
Rivadeneira et al.
Table 4.
Control model for sh identication Month: ___________
Source: Fishing brigade
n
: Method of capture:
Date Species
Time of
capture
Time of arrival at
collection point
Time of arrival in
the industry
Demerit value of the
collection point
Lot number
transmission.
raw materials into the industrial process; and determine
harvest losses.
improvements.
losses evaluation is proposed, once the traceability system
Results and Discussion
selected.
factors to be controlled. Design of experiment
criteria on the procedure to be applied, and using a
Microsoft Excel
adequate= 3, poorly adequate= 4 and inadequate= 5); an
where:
;
y P= Average by aspects.
When the condition: N-P
out, are: species, age, weight, production conditions,
water temperature, chemical contamination, microbial
contamination, poor handling and transport practices,
time-temperature phase, non-compliance with health
procedure:
1.
post-harvest losses, hence, studies in developing coun-
-
-
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
55
Improvement to quality management in supply logistics
2. -
sponse variable are: species, age, weight, production
Table 5.
Production
condition
Contamination of the reservoir (CR)
- Less contaminated point + Most contaminated point
Cold storage capacity (SC) Cold storage capacity (SC)
+ In the
capture
- On the ground + In the capture - On the ground
Environmental
temperature
(ET)
- Morning shift
-ET-CR+SC
(1)
-ET-CR-SC
(2)
-ET+CR+SC
(3)
-ET+CR-SC
(4)
+ Afternoon
shift
+ET-CR+SC
(5)
+ET-CR-SC
(6)
+ET+CR+SC
(7)
+ET+CR-SC
(8)
Fishing gear: mouth; species: common carp; age: 2-3 years; and weight: 1.8-2.3 kg
3. -
-
4. Factorial design 2
-
els each) and within this the 2
3
model, because three
3
=
Equation 2:
Where p
0
2
0
= 0.6 and when it is
3
the 2
-
With this Equation 3, are obtained as result, 3
-
5.
-
-
the boat and the rest when it reached land. Then, these
tests were carried out.
6. -
cro-organisms at 30
o
C that can be present in common
5
CFU per gram, according to
o
C in common carp
are considered harmless as long as their value is zero,
-
the ninth day and the treatment 2 to the seventh day,
microbial growth values remained the same until that
Table 6.
Days stored in ice
1 2 3 4 5 6 7 8 9
Treatments
1
148 1800 1960 2258 2848 5240 62600
90600 99800
2
742 2000 3260 3760 6920 59540 86800 - -
7
410 940 5280 5760 5920 - - - -
8
422 1620 5660 5800 - - - - -
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
56
Rivadeneira et al.
With the data obtained in the previous stages
common carp to the point where it ceases to be harmless
to human health. Overall sensory analysis with a demerit
common carp in ideal conditions can reach up to nine days
Table 7.
indicator measurement.
Quality parameters Scores
Skin
Appearence/color 0 --- 2
Mucus 0 --- 2
Smell 0 --- 3
Rigidity/texture
0 --- 3
Eyes
Cornea 0 --- 3
Pupil 0 --- 2
Gills
Adherence 0 --- 3
Color 0 --- 2
Smell 0 --- 2
Quality Index 0 --- 22
Table 8.
Days stored in ice
1 2 3 4 5 6 7 8 9
Treatments
1
0 0 1 2 3 4 8 14 22
2
0 2 3 3 7 12 19 22 -
3
0 0 1 8 9 15 20 22 -
4
0 0 6 10 16 19 22 - -
5
0 1 2 3 3 10 15 22 -
6
0 2 3 5 11 18 22 - -
7
0 0 4 9 13 17 22 - -
8
0 1 8 10 15 21 22 - -
7.
-
%.
Table 9. ANOVA: general microbiological analysis vs.
days.
Source
Degree of
freedom
Sum of
squares
Middle
square
F
value
p
value
Day
8 23121215834 2890151979 25.76 0.000
Error
16 1794788097 112174256
Total
24 24916003930
S= 10591 R
2
= 92.80 % R
2
(adjusted)= 89.19 %
F: variation between samples averages/variation within samples,
and p: proportion.
Table 10. ANOVA: general sensory analysis vs. days.
Source
Degree of
freedom
Sum of
squares
Middle
square
F
value
p
value
Day 8 3147.1 393.4 27.48 0.000
Error 52 744.4 14.3
Total 60 3891.4
S= 3.784 R
2
= 80.87 % R
2
(adjusted)= 77.93 %
F: variation between samples averages/variation within samples,
and p: proportion.
-
-
-
-
.
8. In the microbiological analysis, only the deterioration
the test cost. Figure 2 shows the microbiological anal-
ysis, in which it was determined that the common carp
-
day.
Figure 2.
relation to the CFU per grams count.
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
57
Improvement to quality management in supply logistics
In the sensory analysis, the deterioration curves
3. In this analysis, it was determined that under ideal
consumption until the seventh day.
Figure 3.
according to the demerit score in Table 7.
9.
that the common carp in relation to the microbi-
the improvements are maintained, the correct hygien-
this, it must be cold stored as soon as the boat arrives
-
At this stage, the relationship between the
account, which is shown in Table 11.
Table 11.
Maximum
lifetime
limi
t
Treatment 1
(-ET-CR+SC)
Day 1 2 3 4 5 6 7 8 9
Scoring for
demerits
0 0 1 2 3 4 8 14 22
Microbial growth
(CFU.g-
1
)
148 1800 1960 2258 2848 5240 62600 90600 99800
Treatment 2
(-ET-CR-SC)
Day 1 2 3 4 5 6 7 8 -
Scoring for
demerits
0 2 3 3 7 12 19 22 -
Microbial growth
(CFU.g-
1
)
742 2000 3260 3760 6920 59540 86800 - -
Minimum
lifetime
limi
t
Treatment 7
(+ET+CR+SC)
Day 1 2 3 4 5 6 7
- -
Scoring for
demerits
0 0 4 9 13 17 22 - -
Microbial growth
(CFU.g-
1
)
410 940 5280 5760 5920 - - - -
Treatment 8
(+ET+CR-SC)
Day 1 2 3 4 5 6 7
- -
Scoring for
demerits
0 1 8 10 15 21 22 - -
Microbial growth
(CFU.g-
1
)
422 1620 5660 5800 - - - - -
Rev. Téc. Ing. Univ. Zulia. Vol. 44, No. 1, 2021, January-April, pp. 04-58
58
Rivadeneira et al.
CFU.g
-1
as a strong positive relationship.
Conclusions
age, weight, production conditions, water temperature,
transport practices, time-temperature phase, non-
= 23= 8).
In the procedure, it was also determined that there are
was determined, which made it possible to obtain the
reduction.
References
[1] Cabellos García J. M.: Aplicación de hidrocoloides,
conservación de pescado. Universidad Nacional de
[2] FAO: El estado mundial de la pesca y la acuicultura.
Cumplir los objetivos de desarrollo sostenible.
[3] Xue M., Zhang J. y Tang W.: “Optimal temperature
[4] Lemma Y., Kitaw D. y Gatew G.: “Loss in Perishable
Food Supply Chain: An Optimization Approach
Roma, Italia, 2016.
[6] Shi C., Lu H., Cui H. y Luo Y.: “Study on the
Food Processing and Preservation, Vol. 38, No. 1,
Caso de estudio en empresas cubanas”. DYNA, Vol.
979-999.
[10] Lao León, Y. O., Pérez Pravia, M. C. y Delgado, F. M.:
“Procedimiento para la selección de la Comunidad
[11] Gutiérrez Pulido H. y de la Vara Salazar R.: Análisis
2008.
con nudos híbridos. Rev. Téc. Ing. Univ. Zulia, Vol.
“Contaminantes microbiológicos en alimentos-
REVISTA TECNICA
www.luz.edu.ve
www.serbi.luz.edu.ve
www.produccioncienticaluz.org
OF THE FACULTY OF ENGINEERING
UNIVERSIDAD DEL ZULIA
This Journal was edited and published in digital format
on December 2020 by Serbiluz Editorial Foundation
Vol. 44. N°1, January - April 2021_________________
