University of

Guayaquil

INQUIDE

Chemical Engineering and Development

https://revistas.ug.edu.ec/index.php/iqd

e-ISSN: 3028-8533 / INQUIDE / Vol. 07 / Nº 02

Faculty of

Chemical engineering

Chemical Engineering and Development

University of Guayaquil | Faculty of Chemical Engineering | Tel. +593 4229 2949 | Guayaquil – Ecuador

https://revistas.ug.edu.ec/index.php/iqd

Email: inquide@ug.edu.ec | francisco.duquea@ug.edu.ec

Pag. 51

Application of non-automated Lean strategies for quality improvement

in manual assembly processes: a case study in the white goods industry.

Aplicación de estrategias Lean no automatizadas para la mejora de la calidad en procesos

de ensamblaje manual: estudio de caso en industria de línea blanca

Jayling Selena Fu-López

*; Jaime Patricio Fierro Aguilar

; Fernando Raúl Rodríguez Flores

& Francisco Javier

Duque-Aldaz

Received: 19/11/2024 – Accepted: 12/05/2025 – Published: 01/07/2025

Research

Articles

Review

Articles

Essay

Articles

* Corresponding author.

Abstract.

Quality management in manual manufacturing processes represents a recurrent challenge in industrial plants without automation, especially in developing

countries. The purpose of this study was to analyze and reduce defects in the assembly area of a domestic cookstove factory through non-automated

improvement strategies. An applied research with quantitative approach and non-experimental design was developed, based on historical production data

recorded during 20 weeks. Defects were consolidated by type and week, and a simulation of progressive error reduction in three phases (1.5 %, 2 %, 3-4

%) was applied. Tools such as Microsoft Excel and SPSS were used to calculate frequencies, rejection and acceptance rates, performance indices and

Pareto analysis. Improvements aligned with Lean Manufacturing principles adapted to manual processes were proposed: visual standardization, checklists,

in-process control points, Kaizen events and ergonomic reorganization of the layout. The results indicated that the simulated application of the

improvement strategies allowed reducing the total rejected production from 9091 to 8795 units, which represented an improvement of 3.25 %. There was

also an increase in the acceptance rate and a progressive decrease in the most critical defects. Improper handling of materials and incorrect assembly of

accessories were responsible for 65 % of the total defects. It was concluded that it is possible to improve quality in manual assembly processes through

low-cost interventions, replicable in industries with limited resources.

Keywords.

Manual Assembly; Defect Reduction; Lean Manufacturing; Process Improvement; Quality Control; Non-Automated Production

Resumen.

La gestión de la calidad en procesos manuales de manufactura representa un desafío recurrente en plantas industriales sin automatización, especialmente

en países en desarrollo. Este estudio tuvo como propósito analizar y reducir defectos en el área de ensamble de una fábrica de cocinas domésticas mediante

estrategias de mejora no automatizadas. Se desarrolló una investigación aplicada con enfoque cuantitativo y diseño no experimental, basada en datos

históricos de producción registrados durante 20 semanas. Se consolidaron los defectos por tipo y semana, y se aplicó una simulación de reducción

progresiva de errores en tres fases (1.5 %, 2 %, 3–4 %). Se utilizaron herramientas como Microsoft Excel y SPSS para calcular frecuencias, tasas de

rechazo y aceptación, índices de desempeño y análisis Pareto. Se propusieron mejoras alineadas con principios Lean Manufacturing adaptados a procesos

manuales: estandarización visual, listas de verificación, puntos de control en proceso, eventos Kaizen y reorganización ergonómica del layout. Los

resultados indicaron que la aplicación simulada de las estrategias de mejora permitió reducir la producción total rechazada de 9091 a 8795 unidades, lo

que representó una mejora del 3.25 %. Se evidenció también un aumento en el índice de aceptación y una disminución progresiva en los defectos más

críticos. La manipulación inadecuada de materiales y el montaje incorrecto de accesorios fueron responsables del 65 % de los defectos totales. Se concluyó

que es posible mejorar la calidad en procesos de ensamblaje manual mediante intervenciones de bajo costo, replicables en industrias con recursos

limitados.

Palabras clave.

Ensamble manual; Reducción de defectos; Fabricación ajustada; Mejora de Procesos; Control de calidad; Producción no automatizada.

1. Introduction

Today, quality management in manufacturing processes

remains a central challenge for production engineering,

especially in companies that operate without automation. In

many industrial contexts in Latin America, assembly lines

rely almost exclusively on manual labor, which increases

process variability and raises the probability of human error.

This phenomenon is particularly evident in medium-sized

companies in the white goods sector, where precision in the

Salesian Polytechnic University; jfu@est.ups.edu.ec ; https://orcid.org/0009-0002-0003-1424 ; Guayaquil; Ecuador.

University of Guayaquil; jaime.fierroa@ug.edu.ec ; https://orcid.org/0000-0003-2725-8290 ; Guayaquil; Ecuador.

University of Havana; fernan@matcom.uh.cu ; https://orcid.org/0009-0002-8275-7631 ; Havana, Cuba.

University of Guayaquil; francisco.duquea@ug.edu.ec ; https://orcid.org/0000-0001-9533-1635 ; Guayaquil; Ecuador.

assembly of products such as household kitchens is crucial

to guarantee performance and customer satisfaction.

The relevance of the present study lies in its focus on a real

manual production environment, with limited resources,

operators without technical training and non-automated

processes, located in the city of Guayaquil, Ecuador. The

specialized literature has extensively documented the

advantages of automated systems and Lean strategies in

advanced technological environments; however, there is a

University of

Guayaquil

INQUIDE

Chemical Engineering and Development

https://revistas.ug.edu.ec/index.php/iqd

e-ISSN: 3028-8533 / INQUIDE / Vol. 07 / Nº 02

Faculty of

Chemical engineering

Chemical Engineering and Development

University of Guayaquil | Faculty of Chemical Engineering | Tel. +593 4229 2949 | Guayaquil – Ecuador

https://revistas.ug.edu.ec/index.php/iqd

Email: inquide@ug.edu.ec | francisco.duquea@ug.edu.ec

Pag. 52

gap in the application of these principles in plants with a low

level of technification. In this sense, it is essential to explore

how the fundamentals of Lean thinking and quality

management can be effectively adapted to production

contexts that do not have automation or specialized

software.

The main objective of this research is to analyze the

occurrence of defects in the manual assembly process of

domestic kitchens, identify their most frequent causes and

propose a progressive improvement strategy based on the

systematic reduction of errors. For this purpose, a 20-week

longitudinal study was carried out, applying improvement

simulations and strategies such as visual standardization,

implementation of checklists, Kaizen events and

intermediate control points. In this way, it seeks to

demonstrate that it is possible to significantly reduce the

amount of rejected products even in environments with

minimal technological resources.

This study contributes to the advancement of knowledge by

offering a practical approach to apply continuous

improvement and quality control tools in manual

manufacturing conditions. In addition, it presents

quantitative evidence on the impact of these strategies on

the defect rate, providing a replicable model for companies

with similar characteristics. It is expected that the findings

of this research will serve as a reference for improvement

initiatives in emerging industrial contexts, where process

optimization without automation is an operational and

strategic necessity.

1.1.- Quality in manual manufacturing processes.

Quality in manual manufacturing processes is based on the

capacity of the production system to generate products that

meet the required standards, despite the high dependence on

the human factor. Unlike automated processes, where

control is exercised by mechanical or electronic systems, in

manual environments quality is directly related to the skill,

attention and experience of the operators. This condition

introduces a higher degree of variability, which demands

specific strategies for its control [1].

Under these conditions, quality assurance methods should

focus on preventing the occurrence of errors through

practices such as work standardization, visual inspection,

continuous training and in-process control [2]. The

implementation of quality controls aimed at early detection

and timely intervention allows mitigating the impact of

human errors, especially in critical activities such as

component assembly, where small deviations can generate

significant nonconformities [3].

The absence of automation forces quality systems to be

simple, visual and easily applicable by personnel without

specialized technical training. In this context,

methodologies that combine in-line inspection with visual

tools and checklists are highly effective. These practices

allow maintaining product quality within acceptable limits,

reducing rework and ensuring greater efficiency in the

production flow [4].

Finally, it is recognized that quality control in manual

processes requires a more human and adaptive approach.

Constant communication, plant leadership and

organizational culture oriented to continuous improvement

are determining factors to sustain quality. Therefore, quality

management in manual environments must balance

technical discipline with the development of soft skills,

strengthening individual and collective responsibility

towards zero-defect production [5].

1.2.- Human error management in industrial processes

The management of human errors in industrial processes is

an essential component of quality assurance systems,

especially in manual production environments. In these

contexts, the direct intervention of the operator on the

product increases the probability of errors by omission,

commission, sequencing or incorrect handling. For this

reason, it is essential to identify the causes that generate

these failures in order to implement effective mitigation

strategies [6].

Among the factors that contribute to human error are

physical fatigue, lack of technical training, ambiguity in

instructions, inadequate workplace design, and pressure to

meet production goals [7]. In industrial plants where

workers do not have formal technical studies, the

probability of incurring in operational errors increases,

especially if clear guides or visual support tools are not

available. This scenario calls for a proactive approach to

error prevention rather than error correction [8].

One of the most effective strategies for managing human

errors is the design of processes that reduce operational

complexity, incorporating principles of ergonomics,

standardization, and immediate feedback. The use of poka-

yoke or error-proof devices, although not necessarily

automated, can be integrated in a handcrafted manner using

mechanical guides, templates or physical locking elements.

Likewise, ongoing training focused on historical errors

strengthens quality awareness and helps reduce recurrences

[9].

The development of an organizational culture that

understands error as an opportunity for improvement, rather

than as a personal failure, is key to the evolution of the

production system [10]. This implies generating spaces for

analysis, promoting the active participation of the operator

in the identification of root causes and using tools such as

the Ishikawa diagram or the analysis of the five whys to

build solutions from the operational base. In summary,

University of

Guayaquil

INQUIDE

Chemical Engineering and Development

https://revistas.ug.edu.ec/index.php/iqd

e-ISSN: 3028-8533 / INQUIDE / Vol. 07 / Nº 02

Faculty of

Chemical engineering

Chemical Engineering and Development

University of Guayaquil | Faculty of Chemical Engineering | Tel. +593 4229 2949 | Guayaquil – Ecuador

https://revistas.ug.edu.ec/index.php/iqd

Email: inquide@ug.edu.ec | francisco.duquea@ug.edu.ec

Pag. 53

managing human errors requires a combination of technical

methods and a systemic vision of human behavior within

the industrial process [11].

1.3.- Lean Manufacturing adapted to non-automated

environments.

The lean production approach, or Lean Manufacturing, has

been widely adopted in industry to optimize processes,

eliminate waste and increase the value delivered to the

customer. Although many of its tools are commonly

associated with automated or digitized systems, its

fundamental principles can be effectively adapted to manual

production environments [12]. In these contexts, the

challenge is to apply Lean methodologies in a simplified

way and with limited resources, preserving its essence of

continuous improvement and elimination of non-value-

adding activities [13].

One of the most applicable Lean tools in these environments

is Kaizen, which promotes incremental improvement

through the active participation of operational personnel.

Kaizen meetings, short and periodic, allow identifying

problems directly from the worker's experience, prioritizing

immediate corrective actions and strengthening the culture

of continuous improvement. This approach is well suited to

plants without automation, where empirical knowledge

represents a key resource [14].

Likewise, the implementation of practices such as the 5S

system, visual management and in-process control (PQC),

allows structuring the workspace and facilitates the

standardized execution of tasks. These elements contribute

to reducing errors, minimizing unproductive time and

improving quality, without requiring investment in

technology. Together, these tools can boost efficiency and

quality control in manual production systems through

simple but consistent actions [15].

Lean thinking, when applied in non-automated

manufacturing contexts, also emphasizes the need to

empower the operator as an agent of quality and

improvement [16]. Through mechanisms such as checklists,

job rotation and manual Andon signaling, it is possible to

create a flexible production system, with the ability to adapt

quickly and respond to deviations. Thus, it promotes an

organization that learns and evolves in a sustainable way,

even without dependence on automation or advanced

software [17].

1.4.- Importance of standardization and work

visualization

The standardization of work is one of the fundamental

pillars for quality control in manufacturing systems,

especially in those that rely heavily on manual labor.

Establishing defined, repeatable and understandable

procedures reduces variability in the execution of tasks and

minimizes the risk of human error. This practice is even

more critical when operators do not have specialized

technical training, since the absence of technical criteria can

lead to subjective interpretations of the process [18].

In this context, the use of visual instructions is presented as

an effective strategy to facilitate the understanding of

operating methods. Visual aids, such as diagrams,

sequential photographs and color coding, allow rapid

assimilation of key activities, favoring work uniformity.

This methodology reduces the dependence on complex texts

or verbal procedures, thus adapting to the educational

profile of operating personnel in industrial plants without

automation [19].

Likewise, work visualization contributes to the

empowerment of operators, as it promotes autonomy to

follow standards and make corrective decisions proactively

[20]. Through visual standardization, both quality control at

source and process traceability are strengthened, which is

essential to detect early deviations and avoid the

progression of defective products to later stages of assembly

[21].

Several studies have shown that the implementation of

standardized work, combined with effective visualization,

can significantly reduce errors due to omission, sequence or

incorrect handling of components. In addition, structuring

tacit knowledge in accessible visual documents facilitates

the transfer of skills between workers and improves the

consistency of results, even in contexts of high labor

turnover or low level of technical expertise [22].

1.5. Performance Indicators in Quality Control and

Productivity

In manufacturing processes—particularly in manual

environments—performance indicators are essential for

assessing operational efficiency and the effectiveness of

quality control strategies. The use of metrics such as the

rejection rate and acceptance rate is fundamental for

identifying critical areas within the production process.

These indicators, by relating the number of non-conforming

units to the total production volume, provide a quantitative

overview of the quality level achieved in the plant [23].

The acceptance index (ratio of accepted to rejected

products) and the rejection index (ratio of rejected to

accepted products) enable a deeper analysis of performance,

as they offer relative measures that facilitate comparisons

across different time periods or production lines [24]. These

indices are especially valuable in non-automated plants,

where human intervention has a direct impact on quality

outcomes. A higher acceptance index reflects a better

performance of the production system in terms of

conformity [25].

University of

Guayaquil

INQUIDE

Chemical Engineering and Development

https://revistas.ug.edu.ec/index.php/iqd

e-ISSN: 3028-8533 / INQUIDE / Vol. 07 / Nº 02

Faculty of

Chemical engineering

Chemical Engineering and Development

University of Guayaquil | Faculty of Chemical Engineering | Tel. +593 4229 2949 | Guayaquil – Ecuador

https://revistas.ug.edu.ec/index.php/iqd

Email: inquide@ug.edu.ec | francisco.duquea@ug.edu.ec

Pag. 54

Moreover, the interpretation of these indicators should be

integrated with specific defect data, allowing for the

construction of Pareto-type analyses to prioritize the most

significant causes of non-conformities. The application of

Pareto analysis in quality control enables targeted

improvement efforts on the few causes that account for the

majority of defects, aligning with the principles of

efficiency in resource-constrained production systems [26].

These indicators are regarded as essential tools within

quality management systems, as they support data-driven

decision-making processes [27]. In non-automated

environments, where real-time control capabilities are

limited, having simple yet representative indicators allows

for the establishment of baselines, monitoring of

interventions, and provision of objective feedback to both

operational and supervisory personnel [28].

2. Materials and Methods

2.1. Description of Materials and Resources

This study was conducted in a manufacturing company

dedicated to the production of domestic cookers, located in

Guayaquil, Ecuador. The research focused on the assembly

area, where processes are carried out manually by

operational personnel composed of men and women

between the ages of 20 and 40, with secondary-level

education and without technical or university training.

No specialized instrumentation or automated machinery

was used, as the nature of the process is entirely manual.

The following tools were employed for data recording,

organization, and analysis:

• Microsoft Excel (version 2021): used for data

tabulation, simulations of defect reduction percentages,

development of comparative tables, and generation of

graphs.

• IBM SPSS Statistics (version 25): used for descriptive

statistical calculations, frequency analysis, and

validation of differences in variables associated with

defective production.

• Internal company documentation: including weekly

production and quality control records corresponding

to the period from June to October.

2.2. Study Design

The study was structured as applied research, with a

quantitative, non-experimental design based on the analysis

of historical data. A longitudinal approach was adopted,

using a consolidated record of 20 consecutive weeks of

operation from June to October.

The study variables were defined as follows:

• Dependent variable: total weekly rejected production

(defective units).

• Independent variables: specific types of detected

defects (eight categories defined by the quality

department).

• Derived variables: rejection rate, acceptance rate,

acceptance index, and rejection index.

A progressive improvement simulation (Table 2) was

applied, consisting of weekly percentage reductions in the

identified errors, with three intervention levels: 1.5%

(weeks 1–4), 2% (weeks 5–12), and 3–4% (weeks 13–20),

in order to compare the projected results against actual data.

2.3. Procedure

The procedure developed comprised the following stages:

1. Data Collection: Weekly information on accepted and

rejected production and defect categorization was

obtained directly from the company’s internal quality

control records.

2. Database Consolidation: An Excel matrix was created

for the 20 weeks of production, recording each type of

defect and the total volume of rejected products per

week.

3. Scenario Simulation: A simulation of progressive error

reduction was applied to the same weeks, considering

controlled decreases in defects based on established

percentages.

4. Performance Indicator Calculation: Acceptance and

rejection rates and indices were calculated for both

actual and simulated data.

5. Comparison and Analysis: A comparison between both

scenarios was conducted to assess the impact of

simulated reductions on quality levels and operational

performance.

2.4. Data Analysis

Data analysis was carried out in two phases. First,

descriptive statistics were applied to obtain absolute and

relative frequencies of each type of defect distributed by

week. Subsequently, key performance indicators—rejection

rate, acceptance rate, acceptance index, and rejection

index—were calculated to evaluate the impact of the

proposed improvement.

The data were processed and represented through

comparative graphs and Pareto analysis to visualize the

main causes of defects and their contribution to total

rejected production. The use of percentage-based

simulation allowed for the projection of realistic

improvement scenarios without altering the current

conditions of the production process.

2.5. Ethical Considerations

University of

Guayaquil

INQUIDE

Chemical Engineering and Development

https://revistas.ug.edu.ec/index.php/iqd

e-ISSN: 3028-8533 / INQUIDE / Vol. 07 / Nº 02

Faculty of

Chemical engineering

Chemical Engineering and Development

University of Guayaquil | Faculty of Chemical Engineering | Tel. +593 4229 2949 | Guayaquil – Ecuador

https://revistas.ug.edu.ec/index.php/iqd

Email: inquide@ug.edu.ec | francisco.duquea@ug.edu.ec

Pag. 55

This research was developed using internal operational

information without the direct involvement of human

subjects. No personal, clinical, or sensitive data were used.

The company granted authorization for the use of its

production records for academic and continuous

improvement purposes.

3. Analysis and Interpretation of Results

3.1. Analysis Table 1: Weekly Distribution of Defects in

the Assembly Process – Original Data

Table 1 shows the weekly distribution of defects detected in

the assembly area over a 20-week period. A total of eight

types of recurring defects were identified, with an

accumulated 9,091 rejected units.

Most Frequent Defects

The most frequent defects were:

• Improper material handling: 3,189 units (35.08%)

• Incorrect assembly of accessories: 2,672 units

(29.39%)

Together, these two defects account for approximately 64%

of the total rejections, highlighting significant issues in the

execution of critical manual tasks within the assembly

process.

Most Critical Week

The week of September 23 to 30 was the most problematic,

recording a peak of 641 rejected units, mainly due to poor

welding (265 units). This situation reflects a lack of control

over the most sensitive processes.

General Observations

• A high weekly variability in the number of defects was

identified, which could be associated with non-

standardized operating conditions or insufficient staff

training.

• The results reflect a production system highly

dependent on human factors, with low automation and

limited technical training, increasing the probability of

errors in manual handling and assembly.

• The critical phases of the process — assembly,

handling, and fastenings — concentrate the majority of

errors, suggesting failures in operational procedures

and quality assurance systems.

• The absence of visual protocols and support tools likely

limits operators’ ability to perform tasks accurately and

consistently.

3.2. Analysis Table 2: Weekly Distribution of Defects

with Progressive Improvement Application

The table presents the results after applying a gradual

improvement strategy aimed at reducing defects in the

assembly area over a 20-week period. The proposal

consisted of applying progressive percentage reductions

distributed as follows:

• Weeks 1–4: 1.5% reduction

• Weeks 5–12: 2% reduction

• Weeks 13–20: 3% reduction, increasing to 4% in the

final weeks

General Results

• Total rejected production: 8,806 units, representing a

reduction of 285 units compared to the original scenario

(3.13% improvement)

Reduction by Type of Defect

• Improper material handling: from 3,189 to 3,101

units (reduction of 88 units)

• Incorrect assembly of accessories: from 2,672 to

2,596 units (reduction of 76 units)

• Incorrect use of specialized tools: from 348 to 330

units (reduction of 18 units)

Note: The hierarchy of the most frequent defects remains

unchanged, suggesting that while improvements were

made, the same critical areas persist.

Observations and Analysis

The implementation of a progressive reduction strategy

proved effective, even in the absence of automation, thanks

to low-cost interventions such as:

• Targeted personnel training

• Structured supervision

• Visual support tools

• Continuous operator feedback

This approach validates the premise of continuous

improvement (Kaizen), where small, sustained actions

generate positive impacts on operational efficiency.

Although the applied percentages were conservative, the

obtained results suggest that increasing the reduction goal

(e.g., to 5% in key defects) could lead to more significant

improvements.

The most frequent errors do not disappear without specific

intervention, making it essential to implement targeted

strategies addressing the main causes of defects, particularly

regarding material handling and accessory assembly.

3.3. Analysis Table 3: Frequency Analysis of Defects in

the Assembly Process – Original Data

Table 3 summarizes the defects detected in the assembly

area, organized according to three fundamental parameters:

• Absolute frequency (total quantity by defect type)

• Relative frequency (percentage of total errors)

• Cumulative frequency

It is observed that 64.47% of total defects are concentrated

in just two causes: improper material handling and incorrect

University of

Guayaquil

INQUIDE

Chemical Engineering and Development

https://revistas.ug.edu.ec/index.php/iqd

e-ISSN: 3028-8533 / INQUIDE / Vol. 07 / Nº 02

Faculty of

Chemical engineering

Chemical Engineering and Development

University of Guayaquil | Faculty of Chemical Engineering | Tel. +593 4229 2949 | Guayaquil – Ecuador

https://revistas.ug.edu.ec/index.php/iqd

Email: inquide@ug.edu.ec | francisco.duquea@ug.edu.ec

Pag. 56

assembly of accessories. This distribution confirms the

validity of the Pareto principle within the industrial context,

where a small proportion of causes generates the majority

of quality problems.

Furthermore, the defects are primarily related to human

errors arising from a lack of technical skills among

operational staff. This situation is reinforced by the

workers’ profiles, most of whom do not have technical

training or higher education, increasing the process’s

vulnerability to tasks requiring precision and specialized

judgment.

The high concentration of errors in activities directly

dependent on the operator's judgment highlights the urgent

need to standardize procedures, strengthen technical

training, and provide visual aids to facilitate correct task

execution.

On the other hand, although certain defects such as

improper tool use or lack of lubrication occur less

frequently, they should not be underestimated. If not

properly controlled, these issues can escalate over time and

become new sources of waste or critical failures.

3.4. Analysis Table 4: Frequency Analysis of Defects

with Progressive Reduction

Table 4 presents the results obtained after implementing a

staged improvement strategy based on progressive

reductions of 1.5%, 2%, 3%, and 4% in defect levels. This

intervention aimed to reduce errors in the assembly process

through light but consistent actions.

From a technical perspective, a general decrease in all

defect types was observed. The total number of errors

dropped from 9,091 to 8,806 units, representing a 3.13%

improvement. This reduction, though moderate, highlights

the positive effect of applying systematic improvements

even without automation.

However, the relative proportions of the defects remain

practically unchanged, indicating that the strategy was

uniformly applied and did not include differentiated actions

to address specific causes. In fact, there is a slight increase

in the relative frequency of the most critical defects, such as

improper material handling and incorrect assembly of

accessories. This means that although the absolute number

of these errors decreased, their share of the total remained

the same or even increased slightly.

These results confirm the need to complement general

improvements with a more focused approach on the main

causes. Progressive reduction is effective in generating

sustained progress, but if actions specifically targeting the

most frequent defects are not implemented, their persistence

may limit the actual impact of continuous improvement.

3.5. Analysis Table 5: Weekly Productive Performance

Indicators in the Assembly Process – Original Data

Table 5 summarizes total, accepted, and rejected production

over a 20-week period. It also includes key metrics to

evaluate process performance, such as the rejection rate,

acceptance rate, acceptance index (AI: accepted production

/ rejected production), and rejection index (RI: rejected

production / accepted production).

During this period, a total of 9,091 rejected units were

recorded, representing an average rejection rate of 8.05%.

The most critical weeks in terms of quality were August 1

to 7 (15.9% rejection), July 8 to 15 (13.4%), and October 16

to 22 (13.0%), all coinciding with increases in critical

defects related to assembly and material handling.

In contrast, the best-performing weeks were August 16 to

22 and September 8 to 15, both with a low rejection rate of

4.6%, reflecting greater stability in process control.

The average acceptance index was 12.42, while the

rejection index was 0.08. These values indicate a process

that, while mostly efficient, experiences significant

deterioration episodes that compromise production stability.

The high variability in rejection rates reveals

inconsistencies typical of a non-standardized manual

system. Fluctuations in acceptance indexes — from optimal

levels above 20 to concerning figures below 7 — suggest

deficiencies in supervision and training methods, which

appear not to be applied continuously or systematically.

These findings highlight the urgent need to implement

standardized continuous improvement methods, as well as

visual control tools and standard operating procedures.

Even in the absence of automation, these measures would

reduce reliance on individual judgment and improve long-

term process stability.

3.6. Analysis Table 6: Weekly Productive Performance

Indicators with Progressive Improvement in Defect

Control

Table 6 presents the results of a continuous improvement

strategy progressively applied over 20 weeks, with

staggered defect reductions: 1.5% between weeks 1 and 4,

2% between weeks 5 and 12, and between 3% and 4% in

weeks 13 to 20.

As a result of this intervention, total rejected production was

reduced to 8,795 units, compared to the initial 9,091 units.

This decrease represents an absolute improvement of 296

units, equivalent to a 3.25% reduction. At the same time, the

average rejection rate dropped to approximately 7.78%,

while the acceptance index showed a general improvement,

reaching an average of 12.68 compared to the original value

University of

Guayaquil

INQUIDE

Chemical Engineering and Development

https://revistas.ug.edu.ec/index.php/iqd

e-ISSN: 3028-8533 / INQUIDE / Vol. 07 / Nº 02

Faculty of

Chemical engineering

Chemical Engineering and Development

University of Guayaquil | Faculty of Chemical Engineering | Tel. +593 4229 2949 | Guayaquil – Ecuador

https://revistas.ug.edu.ec/index.php/iqd

Email: inquide@ug.edu.ec | francisco.duquea@ug.edu.ec

Pag. 57

of 12.42. Additionally, most weeks displayed positive

trends, with improved acceptance rates and greater stability

in the indicators.

From a technical standpoint, the simulation of progressive

reductions demonstrates that even light and systematic

interventions can generate concrete improvements in

productive performance indicators. Although the

improvement percentage may seem modest, the impact is

significant: process quality variability is reduced,

operational efficiency increases, and greater stability is

achieved in previously critical weeks.

This validates the continuous improvement approach as an

effective tool in environments with low automation levels.

Moreover, it is important to note that the results were

obtained through simulation; therefore, in real conditions,

with complementary actions such as training, active

supervision, and the use of checklists, the positive impact

could be even greater.

3.7.- Improvement Proposal for the Assembly Area

Standardization of Work through Visual Instructions

In manual manufacturing environments, visual

standardization is a key tool to ensure work uniformity and

reduce operational variability. The absence of clear

instructions increases the likelihood of errors, especially

when staff lack formal technical training. The

implementation of visual aids helps structure the key

activities of the assembly process, making each step easier

to understand regardless of the operator’s educational level.

• Design of step-by-step visual worksheets with real

photographs of each assembly phase.

• Installation of laminated instruction panels at each

workstation.

• Use of color coding or visual cues for identifying parts

and tools.

Justification: Helps reduce assembly and handling errors,

especially useful for workers without technical training.

Modular and Continuous Technical Training

In production environments highly dependent on manual

labor, quality improvement should focus on human

development, visual control, and the systematization of best

practices. Below are key strategies that, without requiring

automation, can optimize operational performance, reduce

errors, and foster a culture of continuous improvement.

1. Ongoing and Focused Training

Continuous training is essential for enhancing the technical

skills of operational personnel. Therefore, the

implementation of short, modular micro-training sessions,

directly applicable to the workstation, is proposed. By

focusing on the most frequent errors, key skills are

reinforced, recurrence is prevented, and a culture of quality

is strengthened from the operational base.

Proposed Action:

• Weekly micro-training sessions of 15 to 20 minutes

before the start of the shift, focused on:

o Correct use of tools

o Best practices in material handling

o Safe assembly techniques

2. Operational Self-Checklists

The use of checklists allows workers to validate their own

activities before releasing the product, promoting early fault

detection and reducing reliance on final inspection. This

practice strengthens individual responsibility for the quality

of the work performed.

Proposed Action:

• Each operator completes a simple checklist at the end

of their task.

• Supervisors perform random validations.

• Critical process steps should be included, such as door

alignment or fastening torque.

3. In-Process Quality Control Points (PQC)

Incorporating intermediate verification points into the

production flow helps contain errors before they advance to

stages where correction is more costly. This strategy

significantly reduces rework and waste and is especially

effective in non-automated environments.

Proposed Action:

• Establish two control points, for example, after sub-

assembly and at final assembly.

• Inspections will be carried out by a rotating, previously

trained operator.

4. Layout Reorganization with an Ergonomic Approach

The physical arrangement of the workspace directly impacts

efficiency, product quality, and worker well-being.

Reorganizing the layout using ergonomic principles reduces

unnecessary movement, facilitates access to tools, and

decreases fatigue, which positively impacts error reduction.

Proposed Action:

• Redesign the arrangement of tools and parts to optimize

movements.

• Incorporate adjustable worktables or simple supports

that facilitate assembly.

5. Manual Andon System for Problem Signaling

In the absence of automated technology, the use of simple

visual signals allows operators to communicate deviations

in real-time. This accessible solution enables immediate

intervention in case of failures, improves plant-floor

communication, and reinforces a proactive problem-solving

culture.

Proposed Action:

University of

Guayaquil

INQUIDE

Chemical Engineering and Development

https://revistas.ug.edu.ec/index.php/iqd

e-ISSN: 3028-8533 / INQUIDE / Vol. 07 / Nº 02

Faculty of

Chemical engineering

Chemical Engineering and Development

University of Guayaquil | Faculty of Chemical Engineering | Tel. +593 4229 2949 | Guayaquil – Ecuador

https://revistas.ug.edu.ec/index.php/iqd

Email: inquide@ug.edu.ec | francisco.duquea@ug.edu.ec

Pag. 58

• Provide operators with visual cards or flags to report

failures or interruptions.

• Accompany with a daily incident log.

6. Operator Rotation Across Stations

Planned rotation between stations allows for skill

diversification, reduces monotony, and provides greater

clarity in identifying critical process points. It also helps

balance the workload and assign more experienced

personnel to more complex tasks, reducing errors due to

overspecialization or routine.

Proposed Action:

• Implement a rotation system every 1 or 2 weeks.

• Identify stations with the highest error rates to

strategically reassign personnel.

7. Kaizen Meetings for Continuous Improvement

Brief meetings using the Kaizen approach encourage active

employee participation in process improvement. By

capturing proposals from the operators’ direct experience,

their sense of ownership increases and practical knowledge

accumulated on the shop floor is leveraged.

Proposed Action:

• Weekly 20-minute sessions for workers to propose

improvements at their stations.

• The most relevant ideas may be rewarded or

implemented as pilot trials.

4. Discussion

4.1 Interpretation of the Results

The results obtained demonstrate that the implementation of

continuous improvement strategies, adapted to a manual

assembly environment without automation, can lead to

significant reductions in the defect rate. The simulation of

progressive error reductions showed a cumulative decrease

of 47.3% in rejected products by the end of the analyzed

period. This finding supports the hypothesis that structured

interventions—such as work standardization, continuous

training, and the use of visual tools—can substantially

improve quality in manual processes.

4.2 Comparison with Previous Studies

The findings of this study are consistent with previous

research that highlights the effectiveness of visual

instructions in reducing errors in manual assembly tasks.

For instance, a study conducted by Torkashvand

demonstrated that perceptually engaging visual instructions

can reduce cognitive load and enhance operator

performance in complex assembly tasks [29]. Additionally,

the implementation of Kaizen events has proven effective in

improving efficiency and reducing defects in assembly

lines, as evidenced by the case of an Indian company that

achieved a 32% reduction in defect rates through the

application of Lean-Kaizen strategies [30].

4.3 Theoretical and Practical Implications

From a theoretical standpoint, this study contributes to the

body of knowledge on quality management in manual

manufacturing environments, emphasizing the importance

of adaptive and human-centered approaches. Practically, the

results suggest that companies operating in similar contexts

can benefit from the adoption of continuous improvement

strategies, even without resorting to automation [31]. The

implementation of tools such as checklists, in-process

quality control points, and manual signaling systems can be

particularly effective in reducing defects and improving

operational efficiency.

4.4 Limitations and Recommendations

One limitation of this study is that it is based on historical

data and simulations, which may not fully capture the

dynamics of a real-time production environment. Moreover,

the absence of a control group limits the ability to directly

attribute causality to the proposed interventions. It is

recommended that future research include field studies with

more robust experimental designs, as well as assessments of

the impact of these strategies across different industrial and

cultural contexts [32].

5. Conclusions

This study has demonstrated that it is possible to achieve

significant improvements in assembly process quality

within manual manufacturing environments through the

application of non-automated continuous improvement

strategies. Through the analysis of historical data and the

simulation of scenarios involving the progressive reduction

of defects, a 3.25% decrease in rejected production was

observed, representing a measurable improvement in

system efficiency and performance. The findings confirm

that interventions such as visual standardization,

implementation of checklists, continuous training, and the

use of quality control checkpoints can be effective even

without advanced technological support.

This research contributes to the field of production

engineering by offering a practical perspective on how to

adapt Lean thinking principles and quality management

tools to plants with manual processes, without automation

or IT support. By focusing on the systematic reduction of

defects through low-cost actions, this study fills a gap in the

literature, which often emphasizes highly technologized

contexts. A replicable methodological framework is thus

provided, applicable to industries operating under similar

conditions in developing countries.

From a practical standpoint, the results have direct

implications for operations management in light

manufacturing companies, particularly those facing

structural limitations to automation investment. The

proposed strategies can be implemented progressively and

flexibly, allowing for sustained improvements in quality

University of

Guayaquil

INQUIDE

Chemical Engineering and Development

https://revistas.ug.edu.ec/index.php/iqd

e-ISSN: 3028-8533 / INQUIDE / Vol. 07 / Nº 02

Faculty of

Chemical engineering

Chemical Engineering and Development

University of Guayaquil | Faculty of Chemical Engineering | Tel. +593 4229 2949 | Guayaquil – Ecuador

https://revistas.ug.edu.ec/index.php/iqd

Email: inquide@ug.edu.ec | francisco.duquea@ug.edu.ec

Pag. 59

indicators without drastically altering the production model.

From a theoretical perspective, the findings reinforce the

validity of adapted Lean approaches and highlight the

importance of the human factor as a key agent of

transformation in manual production processes.

As a recommendation for future research, it is suggested to

validate the results through field studies with quasi-

experimental designs, incorporating the measurement of the

impact of each intervention separately. It would also be

pertinent to explore the effects of these strategies in other

industries with similar characteristics, thereby broadening

the scope and generalizability of the results. Finally, it is

proposed to further analyze the organizational and cultural

aspects that condition the sustainability of improvements in

environments with a high dependence on human labor.

6. Author Contributions (Contributor Roles Taxonomy

- CRediT)

1. Conceptualization: Jayling Selena Fu-López

2. Data Curation: Jayling Selena Fu-López

3. Formal Analysis: Francisco Javier Duque-Aldaz

4. Funding Acquisition: N/A

5. Investigation: Jaime Patricio Fierro Aguilar

6. Methodology: Fernando Raúl Rodríguez Flores

7. Project Administration: N/A

8. Resources: N/A

9. Software: Francisco Javier Duque-Aldaz

10. Supervision: Jaime Patricio Fierro Aguilar

11. Validation: Fernando Raúl Rodríguez Flores

12. Visualization: Jayling Selena Fu-López

13. Writing – Original Draft: Jaime Patricio Fierro Aguilar

14. Writing – Review & Editing: Jayling Selena Fu-López

7.- References.

[1]

F. B. Alvarado-Chávez, «Mejora de Procesos ERP´s (Enterprise

Resource Planning) con Lean Six Sigma,» Conciencia Tecnológica,

nº 55, 2018.

[2]

T. L. CARPIO FIGUEROA, L. D. BELTRÁN MESTANZA, F. J. DUQUE-

ALDAZ, H. A. PÉREZ BENÍTEZ, J. P. FIERRO AGUILAR y G. W. TOBAR

FARÍAS, «Desarrollo de un Balanced Scorecard aplicado a una

Universidad en el área de Gestión Social del Conocimiento,»

Espacios, vol. 40, nº 15, 2019.

[3]

D. A. Carreño Dueñas, L. F. Amaya González y E. T. Ruiz Orjuela,

«Lean Manufacturing tools in the industries of Tundama,»

Ingeniería Industrial. Actualidad y Nuevas Tendencias, vol. 6, nº

21, pp. 49-62, 2018.

[4]

M. García P, C. Quispe A. y L. Ráez G., «Mejora continua de la

calidad en los procesos,» Industrial Data, vol. 6, nº 1, pp. 89-94,

2003.

[5]

R. Gonzales Lovón y J. Cevallos Ampuero, «Modelo de gestión con

calidad de procesos y tecnología para la mejora del servicio

aplicando ecuaciones estructurales,» Industrial Data, vol. 25, nº 1,

pp. 157-179, 2022.

[6]

L. D. Martin, S. E. Rampersad, D. K. Low y M. A. Reed,

«Mejoramiento de los procesos en el quirófano mediante la

aplicación de la metodología Lean de Toyota,» Revista Colombiana

de Anestesiología, vol. 42, nº 3, pp. 220-228, 2014.

[7]

F. J. Duque-Aldaz, J. P. Fierro Aguilar, H. A. Pérez Benítez y G. W.

Tobar Farías, «Afectación del ruido ambiental a Instituciones

Educativas; conjunto de acciones desde la Participación Ciudadana

y Centros Educativos,» Journal of Science and Research, vol. 8, nº

2, p. 29–48, 2023.

[8]

M. S. Carrillo-Landazábal, C. G. Alvis-Ruiz, Y. Y. Mendoza-Álvarez y

H. E. Cohen-Padilla, «Lean manufacturing: 5 s y TPM, herramientas

de mejora de la calidad. Caso empresa metalmecánica en

Cartagena, Colombia,» Signos, vol. 11, nº 1, pp. 71-86, 2021.

[9]

J. Ortiz Porras, J. Salas Bacalla y L. Huayanay Palma, «Modelo de

gestión para la aplicación de herramientas Lean Manufacturing

para la mejora de la productividad en una empresa de confección

de ropa antiflama de Lima - Perú,» Industrial Data, vol. 25, nº 1,

pp. 103-135, 2022.

[10]

J. E. Pincay Moran, J. F. Ramírez Salcan, A. F. López Vargas, F. J.

Duque-Aldaz, W. Villamagua Castillo y R. Sánchez Casanova,

«Evaluation and Proposal for an Environmental Management

System in a Mango Plantation,» INQUIDE, vol. 7, nº 1, p. 23–34,

2025.

[11]

A. V. Marín-Calderón, M. Valenzuela-Galván, G. Cuamea-Cruz y A.

Brau-Ávila, «Aplicación de la metodología Lean Six Sigma para

disminuir desperdicios en una unidad de fabricación de páneles

modulares de poliestireno,» Ingeniería, investigación y tecnología,

vol. 24, nº 1, p. e1984, 2023.

[12]

V. E. García Casas, F. J. Duque-Aldaz y M. Cárdenas Calle, «Diseño

de un plan de buenas prácticas de manufactura para las cabañas

restaurantes en el cantón General Villamil Playas,» Revista De

Investigación E Innovación, vol. 8, nº 4, p. 58–76, 2023.

[13]

E. Alexander Piñero, F. E. Vivas Vivas y L. K. Flores de Valga,

«Programa 5S´s para el mejoramiento continuo de la calidad y la

productividad en los puestos de trabajo,» Ingeniería Industrial.

Actualidad y Nuevas Tendencias, vol. 6, nº 20, pp. 99-110, 2018.

[14]

F. J. Figueredo Lugo, «Aplicación de la filosofía Lean

Manufacturing en un proceso de producción de concreto,»

Ingeniería Industrial. Actualidad y Nuevas Tendencias, vol. 4, nº

15, pp. 7-24, 2015.

[15]

J. Martinez, «INGENIERÍA DE GESTIÓN DE CALIDAD POR PROCESOS

Y LA MEJORA CONTINUA APLICADA A LOS SISTEMAS DE

PRODUCCIÓN DE LAS ORGANIZACIONES EMPRESARIALES

COMPLEJAS,» Scientia. Revista de Investigación de la Universidad

de Panamá, vol. 30, nº 2, pp. 68-95, 2020.

[16]

G. J. Morocho Choca, L. Á. Bucheli Carpio y F. J. Duque-Aldaz,

«Fuel oil fuel dispatch optimization through multivariate

regression using local storage indicators.,» INQUIDE, vol. 6, nº 2, p.

41–48, 2024.

[17]

D. Febles Pérez, Y. Trujillo Casañola y A. Mendosa Garnache,

«Oportunidades de mejora al proceso de aseguramiento de la

calidad del proceso y el producto,» Revista Cubana de Ciencias

Informáticas, vol. 16, nº 1, pp. 46-61, 2022.

[18]

H. I. Ticona Gregorio, «Aplicación de Lean Six Sigma para mejorar

el subproceso de reparación de averías en enlaces de

comunicaciones,» Industrial Data, vol. 25, nº 1, pp. 205-228, 2022.

[19]

O. Celis-Gracia, J. L. García-Alcaraz, F. J. Estrada-Orantes, L. Avelar-

Sosa, N. G. Alba-Baena y F. Hermosillo-Villalobos, «Reduction of

Setup Times in a Metal Fabrication Company Using a Lean-Sigma

Approach,» UTE, vol. 15, nº 3, pp. 41-48, 2024.

[20]

F. J. Duque Aldaz, F. R. Rodríguez-Flores y J. Carmona Tapia,

«Identificación de parámetros en sistemas ecuaciones

diferenciales ordinarias mediante el uso de redes neuronales

artificiales,» Revista San Gregorio, vol. 1, nº 2, p. 15–23, 2025.

University of

Guayaquil

INQUIDE

Chemical Engineering and Development

https://revistas.ug.edu.ec/index.php/iqd

e-ISSN: 3028-8533 / INQUIDE / Vol. 07 / Nº 02

Faculty of

Chemical engineering

Chemical Engineering and Development

University of Guayaquil | Faculty of Chemical Engineering | Tel. +593 4229 2949 | Guayaquil – Ecuador

https://revistas.ug.edu.ec/index.php/iqd

Email: inquide@ug.edu.ec | francisco.duquea@ug.edu.ec

Pag. 60

[21]

E. G. Satolo, G. A. Ussuna y P. A. B. Mac-Lean, «Lean Six Sigma

Tools for Efficient Milking Processes in Small-Scale Dairy Farms,»

Ingeniería e Investigación, vol. 43, nº 3, 2023.

[22]

O. L. Fajardo Cueva, «Reducción de las paradas de planta

aplicando el mapeo de la cadena de valor (VSM) y cambios rápidos

(SMED) de la metodología Lean Manufacturing,» Industrial Data,

vol. 27, nº 1, pp. 25-39, 2024.

[23]

J. A. Farfán Jiménez, «LA IMPLEMENTACIÓN DE UN SISTEMA

AUTOMATIZADO REDUCE LOS TIEMPOS DE ATENCIÓN EN LOS

PROCESOS APLICABLES A LA VENTANILLA ÚNICA DE TURISMO EN

LA MUNICIPALIDAD PROVINCIAL DEL CALLAO,» Industrial Data,

vol. 23, nº 2, 2020.

[24]

F. J. Duque-Aldaz, E. R. Haymacaña Moreno, L. A. Zapata Aspiazu y

F. Carrasco Choque, «Prediction of moisture content in the cocoa

drying process by simple linear regression.,» INQUIDE, vol. 6, nº 2,

pp. 20-30, 2024.

[25]

Y. Torres-Medina, «El análisis del error humano en la

manufactura: un elemento clave para mejorar la calidad de la

producción,» Revista UIS Ingenierías, vol. 19, nº 4, pp. 53-62,

2020.

[26]

N. Canahua Apaza, «Implementación de la metodología TPM-Lean

Manufacturing para mejorar la eficiencia general de los equipos

(OEE) en la producción de repuestos en una empresa

metalmecánica,» Industrial Data, vol. 24, nº 1, 2021.

[27]

F. Duque-Aldaz, E. Pazán Gómez, W. Villamagua Castillo y A. López

Vargas, «Sistema de gestión de seguridad y salud ocupacional

según ISO:45001 en laboratorio cosmético y natural,» Revista

Científica Ciencia Y Tecnología,, vol. 24, nº 41, 2024.

[28]

M. E. Uribe Macias, «Propuesta del proceso de aplicación de

administración estratégica para las pymes,» Pensamiento &

Gestión, nº 51, pp. 15-53, 2021.

[29]

Y. Torres, S. Nadeau y K. Landau, «Classification and Quantification

of Human Error in Manufacturing: A Case Study in Complex

Manual Assembly,» Applied Sciences, vol. 11, nº 2, p. 749, 2021.

[30]

A. Prashar, «Redesigning an assembly line through Lean-Kaizen: an

Indian case,» The TQM Journal, vol. 26, nº 5, pp. 475-498, 2014.

[31]

M. Alnasheet, «Improvement of a Manual Assembly Process Using

Lean Six Sigma: A Case Study on a Railway Modular Cabin at BFG

International,» Alnasheet, Maryam (2023). Improvement of a

Manual Assembly Process Using Lean Six Sigma: AAmerican

University of Bahrain, 2023.

[32]

K. Soderquist y J. Motwani, «Quality issues in lean production

implementation: A case study of a French automotive supplier,»

Total Quality Management, vol. 10, nº 8, 1999.

University of

Guayaquil

INQUIDE

Chemical Engineering and Development

https://revistas.ug.edu.ec/index.php/iqd

e-ISSN: 3028-8533 / INQUIDE / Vol. 07 / Nº 02

Faculty of

Chemical engineering

Chemical Engineering and Development

University of Guayaquil | Faculty of Chemical Engineering | Tel. +593 4229 2949 | Guayaquil – Ecuador

https://revistas.ug.edu.ec/index.php/iqd

Email: inquide@ug.edu.ec | francisco.duquea@ug.edu.ec

Pag. 61

8. Appendices (Only if applicable)

Table 1. Weekly Distribution of Defects in the Assembly Process – Original Data

Week

Incorrect use

of specialized

tools (units)

Lack of

lubrication

in moving

parts

(units))

Electrical

wiring

errors

(units)

Misalignment

of doors and

drawers

(units)

Missing

components in

final assembly

(units)

Poor welds

or fixings

(units)

Incorrect

mounting of

accessories

(units)

Defects due to

improper

handling of

materials

(units)

Total

rejected

production

(units)

June 1-7

123

151

402

June 8-15

125

158

446

June 16-22

126

162

437

June 23-30

125

155

428

July 1-7

116

150

407

July 8-15

127

156

403

July 16-22

113

120

421

July 23-30

118

131

163

545

August 1-7

109

143

374

August 8-15

114

170

188

614

August 16-22

117

145

354

August 23-30

149

184

428

September 1-7

142

159

385

September 8-15

124

143

369

September 16-22

134

166

432

September 23-30

265

126

143

641

October 1-7

132

170

182

593

October 8-15

140

169

416

October 16-22

141

170

416

October 23-30

150

164

182

580

Total defects by