Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 83-102
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ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay
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Determining the correlation between line balancing and productivity: a
proposal for process improvement
Determinación de la correlación entre el balanceo de línea y la productividad, una
propuesta para la mejora de los procesos
Determinando a correlação entre balanceamento de linha e produtividade: uma
proposta de melhoria de processos
Fabiola Hermosillo-Villalobos
1
, Jorge Luis García-Alcaraz
2
(*), Omar Celis-Gracia
3
Recibido: 15/10/2025 Aceptado: 12/01/2026
Summary. - Line balancing (LB) is a key tool in lean manufacturing that optimizes task allocation, reduces downtime,
and increases operational efficiency. This study was conducted in a manufacturing company in the industrial sector,
evaluating four of its 19 assembly lines (21%) that presented low productivity, high levels of waste, and late deliveries.
This study shows the impact of line balancing on process performance and the savings associated with its
implementation. Additionally, two objectives are set: first, to identify external factors, such as employee absenteeism,
that negatively affect production and quantify their effect on operational performance. Second, manufacturing tools
that help mitigate these impacts must be explored, and solution strategies oriented towards continuous improvement
must be developed. This study used a quantitative approach to analyze the relationship between the percentage of added
value activities and productivity (pieces per man-hour), demonstrating that the correct application of LB can
significantly improve system performance. The results offer practical and sustainable solutions for addressing
operational variability and increasing production efficiency.
Keywords: Line balancing; added value activities; absenteeism; productivity; lean manufacturing.
(*) Corresponding author.
1
PhD Student, Department of Electrical Engineering and Computer Science, Autonomous University of Ciudad Juárez (Mexico)
al232734@alumnos.uacj.mx, ORCID iD: https://orcid.org/0000-0003-1644-7598
2
Full-Time Professor, PhD, Department of Industrial Engineering and Manufacturing, Autonomous University of Ciudad Juárez (Mexico),
jorge.garcia@uacj.mx, ORCID iD: https://orcid.org/0000-0002-7092-6963
3
PhD Student, Department of Electrical Engineering and Computer Science, Autonomous University of Ciudad Juárez (Mexico),
al232735@alumnos.uacj.mx, ORCID iD: https://orcid.org/0000-0003-2061-3384
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 83-102
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ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay
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Resumen. - El balanceo de líneas (LB) es una herramienta de manufactura esbelta que optimiza la asignación de
tareas, reduce el tiempo improductivo y aumenta la eficiencia operativa. Este estudio se desarrolla en una empresa
manufacturera del sector industrial, evaluando 4 de sus 19 líneas de ensamble (21%) que presentan baja
productividad, altos niveles de desperdicio y entregas tardías. Esta investigación muestra el impacto de los balanceos
de línea en el desempeño de los procesos, así como los ahorros asociados a su implementación. Además, se plantean
dos objetivos: el primero se identifica los factores externos como el ausentismo laboral que afectan negativamente la
producción, y se cuantifica su efecto sobre el desempeño operativo; y el segundo, explorar herramientas de
manufactura que ayuden a mitigar estos impactos, desarrollando estrategias de solución orientadas a la mejora
continua. El estudio utiliza un enfoque cuantitativo, analizando la relación entre el porcentaje de actividades que
agregan valor y productividad (piezas por hora-hombre), demostrando que una correcta aplicación del LB mejora el
rendimiento del sistema productivo. Los resultados del estudio ofrecen soluciones prácticas y sostenibles para
enfrentar variabilidades operativas y elevar la eficiencia productiva.
Palabras clave: Balanceo de línea; actividades que agregan valor; ausentismo; productividad; manufactura esbelta.
Resumo. - O balanceamento de linha (LB) é uma ferramenta de manufatura enxuta que otimiza a alocação de tarefas,
reduz o tempo de inatividade e aumenta a eficiência operacional. Este estudo foi conduzido em uma empresa de
manufatura do setor industrial, avaliando quatro de suas 19 linhas de montagem (21%) que apresentam baixa
produtividade, altos níveis de desperdício e atrasos nas entregas. Esta pesquisa demonstra o impacto do
balanceamento de linha no desempenho do processo, bem como a economia associada à sua implementação. Dois
objetivos também são abordados: primeiro, identificar fatores externos, como o absenteísmo de funcionários, que
afetam negativamente a produção e quantificar seu efeito no desempenho operacional; segundo, explorar ferramentas
de manufatura que ajudem a mitigar esses impactos, desenvolvendo estratégias de solução voltadas à melhoria
contínua. O estudo utiliza uma abordagem quantitativa, analisando a relação entre o percentual de atividades que
agregam valor e a produtividade (peças por homem-hora), demonstrando que a implementação adequada do LB
melhora o desempenho do sistema de produção. Os resultados do estudo oferecem soluções práticas e sustentáveis
para lidar com a variabilidade operacional e aumentar a eficiência da produção.
Palavras-chave: Balanceamento de linha; atividades que agregam valor; absenteísmo; produtividade; manufatura
enxuta.
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
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1. Introduction. - Production line balancing (LB) allows process optimization and is a critical element in operations
management. It minimizes downtime and ensures continuous operation by allocating tasks across different
workstations. This constant flow improves productivity indicators on production lines, allowing for the efficient use of
available resources [1], [2].
Previous studies suggest that a more balanced workload is associated with improved working conditions, which may
lead to lower fatigue, fewer errors, and lower absenteeism in the workplace. Thus, LB aligns with the principles of
lean manufacturing, as it promotes waste elimination, continuous improvement, and customer focus [3][4], [5].
To perform proper LB, activities must be classified according to the value they add to the product, distinguishing
between added-value (AV) activities, auxiliary work (AW), and non-added-value (NAV) activities. AV activities are
those that transform the product and for which the customer is willing to pay, such as assembly, welding, and
packaging, among others. However, NVA activities, such as auxiliary activities, are necessary to keep the process
running. Finally, pure waste activities are identified, which can be eliminated without affecting the value of the product,
such as idle time (waiting), excessive transportation, and unnecessary movements [6].
The identification and quantification of these activities allow for the prioritization of improvement actions. For
example, reducing the percentage of non-added value activities has been associated with improvements in productivity,
often without the need for significant infrastructure investment [7][8]. Implementing LB techniques in manufacturing
industries increases productivity and improves the efficiency of production processes by 15% [8], [9], which indicates
the importance of designing and implementing effective balancing strategies to maximize operational performance and
reduce inefficiencies.
Simultaneously, recent research indicates that absenteeism affects productivity levels in manufacturing environments.
A negative correlation has been identified between absenteeism rates and performance, suggesting that high
absenteeism disrupts the standard workflow and reduces overall productivity [9][10]. This situation becomes critical
when workers replace absent staff without adequate training, which affects the quantity and quality of production.
In manufacturing systems, absenteeism has been identified as a factor that disrupts production lines, particularly when
personnel with varying levels of experience are reassigned to cover absences in the workforce. Several studies have
reported a negative relationship between absenteeism and productivity levels, suggesting that variations in employee
attendance often coincide with changes in operational performance[11][12][13]. This link underscores the need to
establish effective strategies to manage work absenteeism in conjunction with line balancing, as adjustments to the line
are required to accommodate workers.
For example, when a worker is absent, the impact is most evident in AV activities, leading to production delays and
reduced operational efficiency. To mitigate this effect, it is recommended that cross-training programs be implemented
to allow other workers to take on critical tasks in the event of illness-related absences. In this sense, the LB, by
providing a breakdown of AV and NVA activities, is a valuable tool for identifying opportunities for improvement and
optimizing production in the face of unforeseen circumstances [14].
Various studies have examined the relationship between absenteeism and productivity and found a negative direct
relationship. It has also been studied in administrative [15], mental health [16], and socioeconomic settings [17] and
in various qualitative studies. However, few case studies have quantitatively addressed this issue in the manufacturing
industry, particularly in maquiladoras.
In this context, a maquiladora in the industrial sector with 19 assembly lines across two plants is experiencing low
productivity. A sample was taken from four of these lines, representing 21% of the total assembly line. Analysis of the
information indicates that the current situation has led to delivery delays on one of the lines, with an absenteeism rate
of 75%. In addition, the annual costs associated with idle time and waste on the other lines are estimated at a total of
$57,582.4 USD per year, plus $12,153.91 USD for late deliveries due to the same problem.
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
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To solve this problem, this study proposes implementing a lean manufacturing tool, LB, to determine the relationship
between the percentage of activities that add value to the process and the performance and productivity metrics. In this
study, productivity was defined as the ratio of parts produced by an operator per hour of work [18]. It is measured by
recording the total number of parts manufactured and dividing it by the hours worked by the number of operators, thus
obtaining the indicator of parts per man-hour. This study also seeks to reduce delivery delays and the costs associated
with idle time and waste.
This study will enable the development of strategies to manage work absenteeism by quantitatively analyzing its impact
on productivity metrics and identifying its effects on operational performance. The first step is to numerically assess
the magnitude of absenteeism's effect, then apply tools such as line balancing to demonstrate its positive impact on
productivity. This study aims to examine and understand the variables that negatively affect productivity metrics from
a quantitative perspective and to counteract their effects by implementing solutions that improve overall process
performance, are easy to implement, and are familiar to the engineers in charge of the production lines.
Section 2 includes the methodology for this research, following the introduction. Section three discusses the results,
section four reports the conclusions, and section five presents the limitations and future research Directions
2. Methods. - The methodology used in this research is based on the DMAIC cycle (Define, Measure, Analyze,
Improve, and Control), complemented by lean manufacturing tools, to improve processes affected by external
variations. This approach is based on five phases, as shown in Figure 1, which indicates the techniques used in each
stage and the integration of the lean manufacturing tools.
Figure I. - Proposed methodology.
2.1 Define phase: Identification of absenteeism in the process. This phase aimed to gather the necessary information
to identify the impact of absenteeism on process performance, measured through productivity. To this end, operational
data was collected from the most recent reports, including the number of pieces produced, the number of operators
present on the work team, the number of absences, and the time worked. This information enabled the calculation and
analysis of productivity using an X
-R control chart in Minitab 17®. The use of the X
-R chart is justified by its ability
to monitor process variability and distinguish it from variability associated with specific causes. A rational subgrouping
strategy was applied, in which each subgroup consisted of five consecutive data points produced under homogeneous
operating conditions, such as the same shift and manual assembly process characteristics. In this way, the variation
within each subgroup primarily reflects the inherent variability of the process (common causes), while the variation
between subgroups allows the identification of changes in process conditions, particularly those associated with
fluctuations in labor availability due to absenteeism. Furthermore, sampling was conducted at intervals aligned with
the production ID cycles to ensure stable sampling logic. Under these conditions, the X
–R chart constitutes a valid
method for identifying significant variations due to special causes, considering absenteeism as a central explanatory
variable of productivity performance.
Define:
Identification of
external factors
in the process
•Control chart
Measure:
Quantify the
relationship
between the
external factor
and the
performance
variable
•Boxplot
•Normality test
•Pearson
correlation
Analyze:
Transform lean
manufacturing
tools into levels
and quantify the
relationship
between the
internal factor
and the
performance
variable
•Line balance
•Percentage of AV
activities
•Correlation graph
Improve:
Evaluate before
and after
implementation
•Paired T-Test
Control: Verify
the solution's
implementation
•Work instructions
•Standardization
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Equation 1 measures the percentage of absenteeism, and Equation 2 calculates the average.
(1)
(2)
Where:
= each of the percentage values
= total number of values
2.2 Measure Phase: Quantification of the relationship between absenteeism and productivity. Once the variations
attributable to special causes of absenteeism in the process are identified, the collected information is analyzed using
a box plot to detect outliers. If such values are identified, the data are cleaned to ensure the sample is representative of
the process's normal behavior.
Normality tests were then applied to verify whether the data followed this distribution, a necessary condition for
calculating the correlation coefficient between two variables. Additionally, each productivity point represented in the
Xbar-R control chart was associated with its corresponding absenteeism value. It is used to analyze the relationships
between external variables and process variables in the context of productivity.
The assumptions of linearity and independence of the data were reviewed using residual analysis. Subsequently, linear
regression analysis and Pearson's correlation coefficient were calculated to quantify the strength and direction of the
relationship between absenteeism and productivity. All statistical calculations, including the estimation of the
regression equation, were performed using the Minitab statistical software.
Data quality was assessed by reviewing system data records and comparing them with paper records to identify typos.
If this typo matched the outlier, the data were removed from the sample so that the data's correct behavior could be
represented.
2.3 Analyze: Transformation of the line balancing tool into levels and quantification of the relationship between
line balancing and productivity. -Within the analysis phase, to quantify a value for the line balancing tool that allows
a relationship with productivity to be established, the analysis phase is divided into three stages. The first stage applies
the line-balancing methodology. In the second stage, activities are quantified by their contribution to balancing; that is, all
activities that add value are identified and measured to determine the percentage they represent in the total process. Finally,
in the third stage, the AV activities are quantified following the steps from the previous stage, with the difference that this
stage extends them across multiple production lines within the manufacturing company. In addition, the productivity
corresponding to each line balancing was quantified to identify the relationship between the percentage of AV activities
and productivity.
In the first stage, the methodology corresponding to the selected tool, namely line balancing, is applied. Figure II presents
the specific methodology used to apply the line balancing tool, corresponding to step one of the analysis, which consists of
three steps: In step one, the takt time is calculated, which starts from knowing the actual demand and distributing this
demand in equal amounts over a given period of time, that is, knowing the demand from week one to week four to calculate
the weekly average of parts that must be built to meet the customer demand. Next, the time available for assembling the
parts was estimated by subtracting the mealtimes per shift from the total available time. This data is then used to calculate
the takt time, as shown in Equation 3:
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
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Figure II.- Methodology for the implementation of the line balancing tool
Takt time =
=
(3)
In the second step, the cycle time of each station was measured in seconds. In the third step, the workload is distributed to
each station as close as possible to the takt time. These steps completed the first stage of the analysis phase, and stages two
and three were then carried out.
In the second stage, all activities necessary to execute the production process are classified in detail and grouped into three
main categories: added-value activities (AV), auxiliary activities (AW), and non-added-value activities (NAV).
In addition to these three categories, an analysis of the waste generated between the stations was incorporated. This type of
waste can cause idle time due to cycle-time imbalances between stations or to overproduction when one station continues
to operate. Simultaneously, the next station has a longer cycle time, which leads to product accumulation in the process.
To determine the percentage of added value activities (AV) within line balancing or within a workstation, we began by
classifying all tasks into four categories: added value activities (AV), auxiliary work (AW), non-added value activities
(NAV), and idle time (waste between stations).
We began by calculating the total time per workstation by summing the times for the first three categories (AV, AW, and
NAV). Next, we identified the station with the longest total time, which represents the process bottleneck. The difference
between this maximum time and the times of the other stations is considered waste by balance and is classified as a fourth
category: idle time.
In this way, the four components per station are calculated: AV, AW, NAV, and idle time, the sum of which represents
100% of the components within the balancing. The percentage of added-value activities is calculated by dividing the sum
of AV time (either for a specific station or for the entire balancing) by the total time for the four categories of activities.
This quotient was multiplied by 100 to obtain its value as a percentage, as shown in Equation 4:
(4)
In the third stage, the relationship between the percentage of added-value activities and the productivity performance of the
different production lines is graphically presented. In addition, a linear regression analysis was performed to model the
relationship between these variables, including the estimation of the regression equation, the slope parameter, and its
corresponding confidence intervals. The normality of the residuals was assessed to verify the model's assumptions. All
statistical analyses were performed using Minitab v.19.
2.4 Improve: Alternative solutions for line balancing and evaluation of levels (initial and final status). The
proposed alternative solution involves implementing the LB tool to optimize production process performance by more
Takt time calculation Measuring the cycle
time of each station
Distribute the
workload to each
station as close to the
takt time as possible
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
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efficiently allocating tasks. The improvement considers a quantitative evaluation of the initial state and the state after
implementation based on the analysis of the percentages of activities that add value within line balancing.
To validate the solution's effectiveness, a statistical hypothesis test was conducted to determine whether the achieved
levels represented a significant improvement over the process's initial state. This validation provides an objective basis
for confirming that the application of these tools not only generates operational benefits but also that these benefits are
statistically relevant and sustainable over time.
2.5 Control: Verification of the application of line balancing. – This phase seeks to ensure the sustainability of line
balancing as an improvement solution so that it continues to be implemented over time to preserve the benefits obtained
during the study. To this end, adjustments are made to the instructions and production system to maintain the desired
level; that is, the percentage of added-value activities is preserved in the analyzed processes and consistently applied
during the execution of the manufacturing process.
3. Results and Discussion. – This section is divided into sections according to the information on the DMAIC
methodology reported earlier.
3.1 Define: Identification of absenteeism in the process of training. – Process behavior is monitored using the
Xbar-R control chart, which shows how productivity is influenced by absenteeism. Figure III shows the data collected
over the five months of production, with a total of 115 observations grouped into subgroups of five, yielding 23 points
on the graph. Point 12 falls below the lower control limit (LCL), indicating an abnormal drop in productivity for this
subgroup. This drop indicates the presence of a specific cause of absenteeism, leading to a significant decrease in
process performance.
However, points 12, 13, and 22 on the range chart showed signs of being out of control, suggesting unusual variation
within those subgroups. Although in samples 13 and 22 the mean remained within the control limits, the variability
exceeded the upper control limit (UCL) due to external factors that disrupted the normal process development.
Figure III.- Control chart of productivity
3.2 Measure: Quantification of the relationship between absenteeism and productivity. – Outliers in the process
were excluded from the sample to measure the relationship between absenteeism and productivity. Figure IV shows
the box plot, in which it can be seen that the data are concentrated in the first quartile (Q1) of 14.95 and the third
quartile (Q3) of 15.55, with minimum and maximum values within the range of 14.39 to 16.07, thus showing that
sample number 12 with a value of 12.15 is outside the process range. Therefore, it was removed from the analysis,
leaving 22 points.
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Figure IV- Boxplot for productivity
Once the outlier corresponding to observation 12 was identified and removed, the Anderson-Darling normality test
was applied to assess the data's statistical behavior. As shown in Figure V, the p-value obtained was 0.847, indicating
insufficient evidence to reject the null hypothesis of normality; therefore, the data exhibited an approximately normal
distribution. Furthermore, Figure VI shows a plot of residuals versus fitted values, which displays random dispersion
around the zero-reference line without any systematic patterns or evident curvilinear trends, suggesting that the model's
linearity assumption is adequately met. Additionally, the residuals' variability remained approximately constant across
the range of fitted values, indicating homoscedasticity and supporting the suitability of the linear regression model.
Finally, Figure VII presents the evaluation of the independence of observations by analyzing the residuals based on
the order of data collection, as well as the Durbin-Watson statistic, which had a value of 1.89. This result did not
indicate autocorrelation, confirming the assumption of independence.
Figure V.- Normality test for productivity
Figure VI.- Residuals versus Fits
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
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Figure VII.- Residuals versus Order
After verifying that the assumptions of the absence of outliers, normality, linearity, and independence of the data were
met, a linear regression analysis was performed to evaluate the relationship between absenteeism and productivity.
The results, presented in Figure VIII, show a statistically significant negative relationship, described by the model
Productivity = 16.0958 − 0.1900 · Average Percentage. Under these conditions, a Pearson correlation coefficient of
−0.960 was obtained, indicating a strong inverse linear association between the two variables.
Figure VIII- Relationship between productivity and absenteeism
3.3 Analyze: Transformation of the line balancing tool into levels and quantification of the relationship between
line balancing and productivity. In this phase, we explored whether the line balancing tool had the opposite effect on
absenteeism. In line balancing, we analyze its contribution to increasing productivity and then identify the simplest
operations to assign to inexperienced workers, who cover the work of absent operators.
The results obtained in the first stage, which involve applying the line balancing methodology, consist of determining
customer demand to calculate the takt time. Table I shows the projected demand for the next four weeks, with a weekly
average of 6,493 units per week. Table II shows the time available during the first shift, and Table III shows the time
available during the second shift, for a total of 16.5 h per day.
Week
Forecast
Average
1
5,293
6,493
2
7,593
3
7,639
4
5,393
Table I.- Average weekly demand
Activities
Time (h)
Percentage
Available work time
8.5
91.39
Breakfast
0.3
03.00
Lunch
0.5
05.00
Total
9.3
100
Table II.- First shift available time
Productivity= 16.0958-0.1900Absenteeism percentage
14.20
14.40
14.60
14.80
15.00
15.20
15.40
15.60
15.80
16.00
16.20
0.00 2.00 4.00 6.00 8.00 10.00 12.00
Productivity
(Pieces/man-hour)
Absenteeism percentage (%)
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Activities
Time (h)
Percentage
Available work time
8.0
90.9
Lunch
0.3
05.68
Dinner
0.5
03.40
Total
8.8
100
Table III.- Time available second shift
Takt time is defined as the time interval expressed in seconds in which a unit must be produced to meet customer demand
within the available operating time, that is, the total time available between demands, which in this case corresponds to five
working days, with a daily shift of 16.5 hours (considering the first and second shifts) and expressed in seconds when
multiplied by 3600 and divided by the 6,493 weekly pieces, yields a value of 45.74 seconds per piece.
The next step in the line-balancing process is to measure the cycle time at each workstation. Table IV presents the average
times recorded for each activity within the process, along with their sum, which corresponds to the total cycle time at that
station, yielding a cycle time of 55.70 seconds. The information for calculating the times for the other stations is in the
supplementary material. The cycle times for each station were S1 = 55.71 seconds, S2 = 46.65 seconds, S3 = 55.1 seconds,
and S4 = 41.3 seconds.
Figure IX compares the cycle times of each workstation (S1, S2, S3, and S4) with the previously calculated takt time for
the four workstations. This analysis shows that stations one, two, and three (S1, S2, and S3) have cycle times greater than
the takt time of 45.74 seconds, indicating that, in their current state, these stations cannot meet the projected demand for
the four weeks. Therefore, lean manufacturing tools must be used to balance workloads across stations and ensure
production targets are met.
Description
Chronometry (input in seconds)
Results
Activity
1
2
3
4
5
6
7
8
9
10
Average (sec)
STATION 1
Cutting contacts
5.30
4.51
2.57
6.14
5.18
4.58
2.82
4.50
5.25
7.83
4.87
Post trimming
3.56
4.02
4.31
4.74
4.04
5.80
3.11
3.59
4.37
4.06
4.16
Ring placement
2.80
4.05
3.09
2.74
3.39
2.66
2.61
3.27
2.75
3.06
3.04
Button placement
3.59
2.50
2.59
2.82
2.45
2.44
4.26
2.70
3.60
2.79
2.97
Button placement
5.83
3.80
3.18
3.33
3.06
3.16
3.19
2.97
3.34
2.59
3.45
Button retainer placement
2.25
2.59
4.09
2.60
3.94
2.37
3.36
3.37
3.66
3.42
3.17
Ultrasonic welding
5.80
6.92
5.88
6.55
5.90
6.70
5.98
6.74
6.96
7.77
6.52
Place 2 O-rings
6.95
6.15
6.11
6.62
7.11
6.15
6.65
7.15
7.00
6.75
6.66
Cut retainer
5.30
4.83
3.45
5.20
5.30
6.50
6.10
5.20
5.30
5.50
5.27
Apply hot melt
16.35
16.60
16.32
16.30
15.20
14.50
16.30
14.90
2:90
15.00
15.60
Total sum of the steps
55.70
Table IV.- Cycle time station 1
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Figure IX- Cycle time vs takt time
Given the need to meet established demand, the line-balancing tool is used to optimize the workflow. In this case, tasks
are redistributed by adding a fifth station, S5, to reduce cycle times at stations 1, 2, and 3, which previously exceeded
the takt time.
Table V shows the new assignment of activities for station one (if you want to know the assignment for the rest of the
stations, refer to the supplementary material). Figure X shows the cycle time graph for station one and the rest of the
stations with a comparison of the cycle times per station with respect to the new takt time, in which a cycle time is
obtained for S1 40.59 seconds, S2 = 41.81 seconds, S3 = 43.57 seconds, S4 = 43.06 seconds, and S5 = 40.45 seconds.
This graph shows greater temporal alignment between the stations and a better approximation of the takt time,
reflecting a more balanced workload. As a result, the process is significantly improved, and compliance with customer
requirements within the established period is ensured.
Description
Chronometry (input in seconds)
Results
Activity
1
2
3
4
5
6
7
8
9
10
Average
(sec)
STATION 1
Cutting contacts
6.30
5.51
3.57
7.14
6.18
5.58
3.82
5.50
6.25
8.83
5.87
Post trimming
4.56
5.02
5.31
5.74
5.04
6.80
4.11
4.59
5.37
5.06
5.16
Ring placement
2.80
5.05
4.09
2.74
3.39
3.66
3.61
3.27
3.75
4.06
3.64
Button placement
3.59
2.50
2.59
2.82
2.45
2.44
4.26
2.70
3.60
2.79
2.97
Button placement
6.93
4.80
4.18
3.33
3.06
4.16
4.19
2.97
4.34
3.59
4.16
Button retainer placement
3.25
3.59
5.09
3.60
4.94
3.37
4.36
4.37
4.66
4.42
4.17
Ultrasonic welding
6.80
7.92
6.88
8.55
6.90
7.70
6.98
7.74
7.96
8.77
7.62
Walk to the packing station
5.21
4.21
3.97
4.20
4.10
3.80
4.50
4.60
3.50
3.20
4.13
Bag unit
1.49
4.18
4.75
4.04
3.90
2.30
2.50
3.60
3.90
3.20
3.39
Total sum of the steps
41.10
Table V.- Cycle time station 1
0
10
20
30
40
50
60
S1 S2 S3 S4
Cycle time Takt time
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Figure X.- Cycle time vs. takt time after balancing
Thus far, the steps corresponding to stage one of the lean manufacturing methodology for line balancing tools have
been applied. Next, we begin stage two, in which we perform a detailed classification of all activities necessary to
execute the production process. This classification allows us to analyze the impact of line balancing on productivity
based on the percentage of activities that add value within the process.
Table VI shows the classification of each of the activities corresponding to station one (the rest of the stations are found
in the supplementary material) according to four categories: Added Value (AV), Auxiliary Work (AW), Non-Added
Value (NAV), and idle time (IT). Figure XI complements this information by graphically showing the distribution of
these categories and visualizing idle times resulting from cycle-time differences between stations.
The analysis by station using Equation 5, explained in the methodology, reveals the following results, both per station
and for the total balance:
• Station one: For the time in seconds of the total activities that make up this station, we have a total AV time
of 25.94 s, an AW of 0 s, a NAV of 15.16 s, and idle times of 4.64 s. Adding all the categories gives a total
value of 45.74 seconds. Applying Equation 5 of the methodology to determine the ratio of AV activities within
Station One gives a participation percentage of 56.7%.
• Station two: The time for AV activities was 38.77 seconds, AW was 3.84 seconds, NAV was 0 seconds, and
the idle time was 3.14 seconds, for a total of 45.74 seconds. This resulted in a participation ratio of 84.71%
for AV activities.
• Station three: The AV, AW, NAV, and idle times were 34.08, 7.11, 3.72, and 0.82 s, respectively, for a total
activity time of 45.74 s. This resulted in a participation ratio of 74.5% for AV activities.
• Station four: AV activities lasted 40.35 s, AW activities lasted 0.39 s, NAV activities lasted 4.99 s, and idle
time lasted 0 s, for a total of 45.74 s of activity. This resulted in a participation ratio of 88.2% for AV activities.
• Station five: AV activities lasted 0 s, AW activities lasted 9.21 s, NAV activities lasted 31.19 s, and idle time
lasted 5.34 s, giving a total activity time of 45.74 s. This resulted in a participation ratio of 0% for AV
activities.
Overall, the balancing analysis showed a time of 139.14 s for AV activities, 20.54 s for AW activities, 55.06 s for NAV
activities, and 13.96 s for idle time, giving a total of 228.71 s of activity. This resulted in a participation ratio of 60.83%
for AV activities, which allowed us to quantify the improvement in work distribution based on system productivity.
0
5
10
15
20
25
30
35
40
45
50
s1 s2 s3 s4 s5
Time in seconds
Work Station
Cycle time Takt time
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
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Description of Study
Chronometry (input in seconds)
Results
Activity
Operation
1
2
3
4
5
6
7
8
9
10
Ave
(sec)
STATION 1
Cutting contacts
NAV
6.30
5.51
3.57
7.14
6.18
5.58
3.82
5.50
6.25
8.83
5.87
Post trimming
NAV
4.56
5.02
5.31
5.74
5.04
6.80
4.11
4.59
5.37
5.06
5.16
Ring placement
AV
2.80
5.05
4.09
2.74
3.39
3.66
3.61
3.27
3.75
4.06
3.64
Button placement
AV
3.59
2.50
2.59
2.82
2.45
2.44
4.26
2.70
3.60
2.79
2.97
Button placement
AV
6.93
4.80
4.18
3.33
3.06
4.16
4.19
2.97
4.34
3.59
4.16
Button retainer
placement
AV
3.25
3.59
5.09
3.60
4.94
3.37
4.36
4.37
4.66
4.42
4.17
Ultrasonic
welding
AV
6.80
7.92
6.88
8.55
6.90
7.70
6.98
7.74
7.96
8.77
7.62
Walk to the
packing station
NAV
5.21
4.21
3.97
4.20
4.10
3.80
4.50
4.60
3.50
3.20
4.13
Bag unit
AV
1.49
4.18
4.75
4.04
3.90
2.30
2.50
3.60
3.90
3.20
3.39
Total sum of the steps
41.10
Table VI. - Classification of activities station 1
Figure XI.- Cycle time with activity classification
As part of the third stage of the analysis, which establishes the relationship between the line balancing tool and
productivity, the participation of AV activities within the different balances for the four assembly lines of the
manufacturing company was calculated. Table VII presents the results, showing both the percentage of added-value
activities and the productivity for each balance (for information on the balances, classifications, and productivity, see
the Supplementary Material).
Figure XII shows a positive, statistically significant relationship between the percentage of added-value activities and
productivity, with a correlation coefficient of 0.9428, indicating a strong linear association between the two variables.
To further quantify this relationship, a linear regression analysis was conducted, yielding the model: Productivity =
1.57 + 0.2310·Percentage of added value (%). The estimated slope (β₁ = 0.2310) reflects the magnitude of the linear
0.00
5.00
10.00
15.00
20.00
25.00
30.00
35.00
40.00
45.00
50.00
S1 S2 S3 S4 S5
AW AV NAV Idle time
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
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effect of added value activities on productivity. The 95% confidence interval for the slope [0.1796, 0.2823] did not
include zero, confirming the effect's statistical significance (p < 0.001). Residual analysis revealed a normal
distribution.
Production line and
balancing number
Percentage of
added value (%)
Productivity
(Pieces/man-hour)
Line one balancing 1
53.42
13.03
Line one balancing 2
76.44
19.15
Line one balancing 3
78.5
19.93
Line two balancing 1
40.16
11.3
Line two balancing 2
44.29
13.2
Line two balancing 3
48
12.91
Line two balancing 4
49.72
13.99
Line three balancing 1
41.93
9.92
Line three balancing 2
48.92
11.58
Line three balancing 3
58.73
13.89
Line four balancing 1
48.46
12.66
Line four balancing 2
58.01
14.86
Line four balancing 3
60.83
15.7
Line four balancing 4
56.17
16.15
Table VII- Percentage of added value activities and their corresponding productivity
Figure XII.-Relationship between added value activities and productivity
3.4 Improvement: Alternative solutions for line balancing and evaluation of levels (initial and final status). –
The time studies corresponding to the four assembly lines were analyzed and compared with the results obtained at the
different line balancing levels. This analysis shows an increase in the proportion of added-value activities relative to
the total activities included in the studies and the balancing process.
Table VIII shows the productivity levels achieved on each line when comparing level one balancing, which
corresponds to the initial state of balancing, with level two balancing, which corresponds to the final state (if you want
to see all the data in the table, check the supplementary material), leaving the alternative hypothesis Productivity is
lower in the initial state than after the application of balancing in the final state. Table IX presents the results of the
Paired T-test, which indicate a significant difference in productivity when the participation in added-value activities
within line balancing is increased.
R² = 0.889
5
7
9
11
13
15
17
19
21
35 45 55 65 75
Productivity
(Pieces/man-hour)
Percentage of added value (%)
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Production
Line
Balancing
before
Balancing
after
One
13.17
20.25
One
11.50
17.63
One
13.33
20.50
One
13.50
20.00
One
13.17
20.00
…
…
…
Four
12.71
16.00
Four
11.14
14.25
Four
13.00
16.50
Four
13.14
16.75
Four
12.86
16.25
Table VIII.- Productivity balancing before and after
Sample
N
Mean
St Dev
SE Mean
Balancing before
28
11.687
1.368
0.259
Balancing after
28
15.924
2.522
0.477
Difference
28
-4.237
1.541
0.291
Null hypothesis
H₀:μbefore−μafter
=0
Alternative hypothesis
H1:μbefore−μafter
<0
P-Value
P<0.001
Table IX- 2 Paried T-Test and hypothesis test balancing before and after
3.5 Control: Verification of line balancing implementation. - The operating instructions for lines one, two, three,
and four were updated to detail the steps to be followed at each workstation. These instructions reflect the distribution
of activities defined by the previously analyzed line balancing, ensuring their correct execution in the production
process.
The control system was also updated to reflect the standard times established by the model on each line, in accordance
with the values determined in line balancing. As part of the operational monitoring, visual control boards were installed,
which were updated in real time by the group leaders. This tool allows for continuous monitoring of process
performance and, with the support of various support departments, facilitates the timely implementation of necessary
adjustments to maintain the expected productivity levels.
4. Discussion of the results and conclusions. - In the initial scenario of the manufacturing company in the industrial
sector, consisting of 19 assembly lines distributed across two of its plants, a problem of low productivity and delivery
delays on one of its lines, with an incidence of 75%, was reported. From the sample taken from the four lines, the
annual costs associated with idle time and waste were estimated at $57,582.4. This study has two objectives: first, to
identify the impact of absenteeism on productivity and quantify its effect on this problem; second, to explore the line
balancing tool to understand the behavior that can counteract the effect of absenteeism on productivity, thereby creating
problem-solving strategies through the use of this tool.
The impact of work absenteeism on manufacturing production processes has not been sufficiently studied, which limits
a comprehensive understanding of its implications for operational efficiency. Although there is research [19] that
addresses this phenomenon, its focus is on administrative environments without delving into the specific problems that
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 83-102
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absenteeism generates in production environments, such as delivery delays or costs associated with the interruption of
continuous flow.
To address the first objective, tools commonly used in industrial process monitoring were employed, such as those
applied in the molding [20], metal processing [21], and glass [22]sectors, where critical dimensional variables are
controlled to ensure product quality. However, in this study, the graphical control tool was used from a different
perspective, as it was not applied to product characteristics but rather to a key indicator of process performance, which
provides a comprehensive view of the system's behavior.
The analysis using control charts identified two out-of-control points in the mean chart and three in the range chart, all
of which were attributed to employee absenteeism. These points represent variations attributable to special causes that
are outside the process. Further analysis of the outliers using a box plot revealed that the value for sample 12 was an
outlier. By excluding this point and recalculating the impact of absenteeism on productivity, the Pearson correlation
coefficient obtained was -0.960, confirming a negative relationship between these two variables. In addition, the
estimated slope of the regression equation Productivity = 16.0958 − 0.1900 (β₁ = −0.1900) indicates a negative linear
effect of absenteeism on productivity, with a magnitude of −0.1900. Once the impact of the external factor on system
performance was determined, the second objective focused on developing improvement strategies using the line
balancing tool, which made it possible to address the problem of delays on one of the lines, where 75% on time delivery
was not being achieved. As a result, a substantial improvement in workflow efficiency was achieved, generating
estimated annual savings of $12,153.91 USD, in addition to a significant reduction in idle time and overproduction
between workstations.
The line balancing tool has been widely used to optimize production processes and achieve significant increases in
operational efficiency. For example, in the garment industry, its implementation increased productivity from 54.22%
to 66% [23] the manufacture of automotive parts, an improvement of 7.7% was achieved [24]; and in the electronics
industry, it contributed to the elimination of bottlenecks [25].
Despite these results, few studies have quantitatively analyzed the impact of line balancing on productivity using
correlation-based metrics to provide an objective parameter that would facilitate the design of more precise
interventions to address issues inherent in the dynamic environment faced by manufacturing organizations.
To evaluate the impact of these tools on process performance, a detailed classification of all activities involved in line
balancing was performed, including those considered waste, specifically those associated with the idle time. This
analysis enabled calculating the percentage of activities that added value for each evaluated line and relating this
indicator to the observed productivity levels. The correlation results show that the LB tool maintains a positive
relationship with productivity, with a coefficient of 0.9428. Finally, the increase in these activities within the assembly
lines resulted in additional savings of $57,582.4 USD per year, attributable to the elimination of unproductive time and
the reduction of waste.
5. Limitations and Future Research Directions. - It is important to note that this study was developed using a sample
corresponding to the assembly line of an industrial manufacturing company. However, the methodology and analysis
approach can be replicated in other production processes, such as injection molding, metal treatment, and painting.
This suggests that the findings have broader applicability in industrial environments, provided that the specific
characteristics of each process are considered.
One limitation of this study is that lean manufacturing tools were used to evaluate their impact on performance
indicators, with a focus mainly on productivity. The analysis focused on determining the direction (positive or negative)
and magnitude of this impact as a basis for proposing improvement strategies that integrate both external and internal
factors of the production system. However, this approach did not consider interactions with other tools or between the
internal factor LB and the external factor absenteeism.
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 83-102
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For future research, it is recommended to broaden the scope of the study by incorporating additional lean manufacturing
tools or by developing an analysis that considers both external and internal factors to design robust processes. In this
way, even in the face of uncontrollable external factors such as absenteeism, their impact on productivity can be
reduced, promoting the stability and efficiency of the operating system.
Availability of information/Supplementary material
Hermosillo, Fabiola (2025), "Line balancing and productivity," Mendeley Data, V1, doi: 10.17632/5d7vn57xnn.1
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 83-102
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100
References
[1] N. Boysen, P. Schulze, and A. Scholl, “Assembly line balancing: What happened in the last fifteen years?,” Eur. J.
Oper. Res., vol. 301, no. 3, pp. 797–814, 2022.
[2] M. Nikkerdar and W. ElMaraghy, “Smart adaptable assembly line rebalancing and maintenance,” The International
Journal of Advanced Manufacturing Technology, pp. 1–21, 2025, doi: 10.1016/J.EJOR.2021.11.043
[3] A. J. Alrowili et al., “Critical Review of Workload Distribution between Nurses and Health Assistants in Healthcare
Settings,” Journal of Ecohumanism, vol. 3, no. 8, pp. 10151–10163, 2024, doi: 10.62754/JOE.V3I8.5626
[4] C. N. Wang, T. T. B. C. Vo, H. P. Hsu, Y. C. Chung, N. T. Nguyen, and N. L. Nhieu, “Improving processing
efficiency through workflow process reengineering, simulation and value stream mapping: a case study of business
process reengineering,” Business Process Management Journal, vol. 30, no. 7, pp. 2482–2515, 2024, doi:
10.1108/BPMJ-11-2023-0869
[5] E. Edmund, “Risk Based Security Models for Veteran Owned Small Businesses,” International Journal of Research
Publication and Reviews, vol. 5, no. 12, pp. 4304–4318, 2024, doi: 10.55248/gengpi.5.1224.250137
[6] I. Elemure, H. N. Dhakal, M. Leseure, and J. Radulovic, “Integration of Lean Green and Sustainability in
Manufacturing: A Review on Current State and Future Perspectives,” Sustainability, vol. 15, no. 13, p. 10261, Jun.
2023, doi: 10.3390/su151310261.
[7] I. C. Medina-Fernández, H. H. Andrade-Sosa, and R. Peña-Baena Niebles, “Propuesta metodológica para el
balanceo de líneas de producción a partir de diagramas AND/OR,” Revista Ingenierías Universidad de Medellín, vol.
18, no. 34, pp. 25–44, 2019.
[8] L. Neuber, C. Englitz, N. Schulte, B. Forthmann, and H. Holling, “How work engagement relates to performance
and absenteeism: a meta-analysis,” European Journal of Work and Organizational Psychology, vol. 31, no. 2, pp. 292–
315, 2022, doi: 10.1080/1359432X.2021.1953989
[9] M. Afy-Shararah and K. Salonitis, “Integrated modeling of ‘soft’ and ‘hard’ variables in manufacturing,” The
International Journal of Advanced Manufacturing Technology, vol. 122, no. 11, pp. 4259–4265, 2022, doi:
https://doi.org/10.1007/s00170-022-09872-z.
[10] J. Aarstad and O. A. Kvitastein, “Effect of long-term absenteeism on the operating revenues, productivity, and
employment of enterprises,” Adm. Sci., vol. 13, no. 6, p. 156, 2023, doi: https://doi.org/10.3390/admsci13060156.
[11] M. Briones, “The effect of workload and work stress on employees’ work motivation and productivity in a
manufacturing company,” International Journal of Research Publications, vol. 134, no. 1, 2023, doi:
https://doi.org/10.47119/ijrp10013411020235512.
[12] I. N. Latifah, A. A. Suhendra, and I. Mufidah, “Factors affecting job satisfaction and employee performance: a
case study in an Indonesian sharia property companies,” International Journal of Productivity and Performance
Management, vol. 73, no. 3, pp. 719–748, 2024, doi: 10.1108/IJPPM-03-2021-0132/FULL/PDF
[13] B. Acosta, “Work absenteeism and its impact on productivity in the administrative area,” Revista Tecnológica
Ciencia y Educación Edwards Deming, vol. 9, no. 1, pp. 18–27, 2025, doi: 10.37957/RFD.V9I1.142
[14] D. F. de Oliveira, C. M. Balbino, C. B. Ribeiro, R. M. de Oliveira Ramos, V. J. Sepp, and L. H. Loureiro, “Frederick
Herzberg and the Theory of the Two Factors in the contribution to the prevention of absenteeism at work,” Cuadernos
de Educación y Desarrollo, vol. 15, no. 12, pp. 17557–17569, 2023, doi: 10.55905/cuadv15n12-131
[15] S. T. Gulyamova, S. F. Abdul Aziz, N. H. Omar, and R. H. Mohd, “Workplace-related socioeconomic issues
associated with job performance and productivity among employees with Various impairments: a systematic literature
review,” Soc. Sci., vol. 12, no. 5, p. 275, 2023, doi: 10.3390/SOCSCI12050275
[16] J. Gonzales et al., “Impacto de un programa ergonómico en la productividad de una empresa de fabricación de
envases de hojalata,” Agroindustrial Science, vol. 6, no. 2, pp. 213–219, 2016.
[17] W. Brouwer, K. Verbooy, R. Hoefman, and J. van Exel, “Production losses due to absenteeism and presenteeism:
the influence of compensation mechanisms and multiplier effects,” Pharmacoeconomics, vol. 41, no. 9, pp. 1103–1115,
2023, doi: 10.1007/S40273-023-01253-Y
[18] R. C. Kanu, “A Study of Process Variability of the Injection Molding of Plastics Parts Using Statistical Process
Control (SPC),” In 2013 ASEE Annual Conference & Exposition , pp. 23–110, 2013, doi: 10.18260/1-2-19124
[19] M. Rizal and S. M. Khoiroh, “Penerapan Metode Statistical Process Control dalam Pengendalian Kualitas Kawat
Baja ,” Metode: Jurnal Teknik Industri, vol. 9, no. 2, pp. 48–62, 2023.
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 83-102
https://doi.org/10.36561/ING.30.7
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay
101
[20] T. S. Imaroh and A. Mustofa, “Defect reduction analysis to improve glass bottle packaging products quality using
statistical process control (SPC) at PT. Muliaglass Container (MGC),” Journal of Social Science, vol. 3, no. 5, pp.
1003–1018, 2022, doi: 10.46799/JSS.V3I5.402
[21] M. M. Teshome, T. Y. Meles, and C. L. Yang, “Productivity improvement through assembly line balancing by
using simulation modeling in case of Abay garment industry Gondar.,” Heliyon, vol. 10, no. 1, 2024, doi:
10.1016/J.HELIYON.2023.E23585
[22] K. Rochayata and W. Widodasih, “Analysis of the line balancing assembly implementation to increase
productivity,” Formosa Journal of Multidisciplinary Research, 2023.
[23] K. M. Khairai and S. N. A. Khalil, “Line balancing study using value stream mapping tool on lean manufacturing:
A case study in an electronic industry,” Qomaruna Journal of Multidisciplinary Studies, vol. 1, no. 2, pp. 55–64, 2024,
doi: 10.62048/QJMS.V1I2.39
F. Hermosillo-Villalobos, J. L. García-Alcaraz, O. Celis-Gracia
Memoria Investigaciones en Ingeniería, núm. 30 (2026). pp. 83-102
https://doi.org/10.36561/ING.30.7
ISSN 2301-1092 • ISSN (en línea) 2301-1106 – Universidad de Montevideo, Uruguay
102
Author contribution:
1. Conception and design of the study
2. Data acquisition
3. Data analysis
4. Discussion of the results
5. Writing of the manuscript
6. Approval of the last version of the manuscript
FHV has contributed to: 1, 2, 3, 4, 5 and 6.
JLGA has contributed to: 1, 2, 3, 4, 5 and 6.
OCG has contributed to: 1, 2, 3, 4, 5 and 6.
Acceptance Note: This article was approved by the journal editors Dr. Rafael Sotelo and Mag. Ing. Fernando A.
Hernández Gobertti.
