Vol. 12/ Núm. 4 2025 pág. 3659
https://doi.org/10.69639/arandu.v12i4.1895
Quality Improvement in Interlining Manufacturing Through
the Six Sigma DMAIC Methodology: A Case Study
Mejora de la calidad en la fabricación de entretelas mediante la metodología Six Sigma
DMAIC: un estudio de caso
Alan Badillo Ruiz
ba454431@uaeh.edu.mx
https://orcid.org/0009-0000-6062-0568
Universidad Autónoma del Estado de Hidalgo
Instituto de Ciencias Básicas e Ingeniería
Área Académica de Ingeniería y Arquitectura Hidalgo
México
Erick Uriel Morales Cruz
erick_morales@uaeh.edu.mx
https://orcid.org/0009-0008-2071-9713
Universidad Autónoma del Estado de Hidalgo
Instituto de Ciencias Básicas e Ingeniería
Área Académica de Ingeniería y Arquitectura Hidalgo
México
Katherin Paola Martínez Olvera
ma453760@uaeh.edu.mx
https://orcid.org/0009-0005-6526-6031
Universidad Autónoma del Estado de Hidalgo
Instituto de Ciencias Básicas e Ingeniería
Área Académica de Ingeniería y Arquitectura Hidalgo
México
Estella María Esparza Zúñiga
estella_esparza@uaeh.edu.mx
https://orcid.org/0009-0008-2603-1311
Universidad Autónoma del Estado de Hidalgo
Instituto de Ciencias Básicas e Ingeniería
Área Académica de Ingeniería y Arquitectura Hidalgo
México
Eusebio Ortiz Zarco
ramiro_cadena@uaeh.edu.mx
https://orcid.org/0000-0003-4745-6198
Universidad Autónoma del Estado de Hidalgo
Instituto de Ciencias Económico Administrativas
Área Académica de Administración Hidalgo
México
Artículo recibido: 10 noviembre 2025 -Aceptado para publicación: 18 diciembre 2025
Conflictos de intereses: Ninguno que declarar.
Vol. 12/ Núm. 4 2025 pág. 3660
ABSTRACT
The Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) methodology is
widely used for quality improvement in manufacturing processes. This study presents a case study
focused on improving quality in an interlining manufacturing process within a textile company.
The objective was to identify the most frequent defects, analyze their root causes, and implement
corrective and preventive actions to increase the process sigma level. Quality tools such as
inspection sheets, Pareto analysis, p control charts, Ishikawa diagrams, and a PDCA-based action
plan were applied. The results showed that stains, creases, and tears accounted for approximately
80% of total defects. After implementing improvement actions, sigma levels increased across all
defect categories, with improvements of up to 0.6 in some cases. Although the target sigma level
was not achieved for all defects within a single cycle, the findings demonstrate the effectiveness
of DMAIC as a structured approach for quality improvement in interlining manufacturing and
highlight the importance of continuous improvement strategies.
Keywords: DMAIC, six sigma, PDCA, quality, continuous improvement, Ishikawa
RESUMEN
La metodología Six Sigma DMAIC (Definir, Medir, Analizar, Mejorar y Controlar) es
ampliamente utilizada para la mejora de la calidad en los procesos de manufactura. Este estudio
presenta un caso de estudio enfocado en la mejora de la calidad en un proceso de fabricación de
entretelas dentro de una empresa textil. El objetivo fue identificar los defectos más frecuentes,
analizar sus causas raíz e implementar acciones correctivas y preventivas para incrementar el
nivel sigma del proceso. Se aplicaron herramientas de calidad como hojas de inspección, análisis
de Pareto, gráficos de control p, diagramas de Ishikawa y un plan de acción basado en el ciclo
PDCA. Los resultados mostraron que las manchas, arrugas y desgarres representaron
aproximadamente el 80 % del total de defectos. Tras la implementación de acciones de mejora,
los niveles sigma aumentaron en todas las categorías de defectos, con mejoras de hasta 0,6 en
algunos casos. Aunque el nivel sigma objetivo no se alcanzó para todos los defectos en un solo
ciclo, los hallazgos demuestran la eficacia del DMAIC como un enfoque estructurado para la
mejora de la calidad en la fabricación de entretelas y destacan la importancia de las estrategias de
mejora continua.
Palabras clave: DMAIC, Six Sigma, PDCA, calidad, mejora continua, Ishikawa
Todo el contenido de la Revista Científica Internacional Arandu UTIC publicado en este sitio está disponible bajo
licencia Creative Commons Atribution 4.0 International.
ABSTRACT
The Six Sigma DMAIC (Define, Measure, Analyze, Improve, and Control) methodology is
widely used for quality improvement in manufacturing processes. This study presents a case study
focused on improving quality in an interlining manufacturing process within a textile company.
The objective was to identify the most frequent defects, analyze their root causes, and implement
corrective and preventive actions to increase the process sigma level. Quality tools such as
inspection sheets, Pareto analysis, p control charts, Ishikawa diagrams, and a PDCA-based action
plan were applied. The results showed that stains, creases, and tears accounted for approximately
80% of total defects. After implementing improvement actions, sigma levels increased across all
defect categories, with improvements of up to 0.6 in some cases. Although the target sigma level
was not achieved for all defects within a single cycle, the findings demonstrate the effectiveness
of DMAIC as a structured approach for quality improvement in interlining manufacturing and
highlight the importance of continuous improvement strategies.
Keywords: DMAIC, six sigma, PDCA, quality, continuous improvement, Ishikawa
RESUMEN
La metodología Six Sigma DMAIC (Definir, Medir, Analizar, Mejorar y Controlar) es
ampliamente utilizada para la mejora de la calidad en los procesos de manufactura. Este estudio
presenta un caso de estudio enfocado en la mejora de la calidad en un proceso de fabricación de
entretelas dentro de una empresa textil. El objetivo fue identificar los defectos más frecuentes,
analizar sus causas raíz e implementar acciones correctivas y preventivas para incrementar el
nivel sigma del proceso. Se aplicaron herramientas de calidad como hojas de inspección, análisis
de Pareto, gráficos de control p, diagramas de Ishikawa y un plan de acción basado en el ciclo
PDCA. Los resultados mostraron que las manchas, arrugas y desgarres representaron
aproximadamente el 80 % del total de defectos. Tras la implementación de acciones de mejora,
los niveles sigma aumentaron en todas las categorías de defectos, con mejoras de hasta 0,6 en
algunos casos. Aunque el nivel sigma objetivo no se alcanzó para todos los defectos en un solo
ciclo, los hallazgos demuestran la eficacia del DMAIC como un enfoque estructurado para la
mejora de la calidad en la fabricación de entretelas y destacan la importancia de las estrategias de
mejora continua.
Palabras clave: DMAIC, Six Sigma, PDCA, calidad, mejora continua, Ishikawa
Todo el contenido de la Revista Científica Internacional Arandu UTIC publicado en este sitio está disponible bajo
licencia Creative Commons Atribution 4.0 International.
Vol. 12/ Núm. 4 2025 pág. 3661
INTRODUCTION
In the manufacturing sector, quality is considered a strategic indicator and one of the most
crucial elements, as it directly impacts customer satisfaction, cost reduction, and market
competitiveness (Evans, 2011). In the textile industry, ensuring product quality is essential,
especially for intermediate products such as interlinings, which play a fundamental role in
garment construction by providing structure, durability, and aesthetic appearance (Hayajneh,
2013). Any deviation in their quality may lead to rework, waste, and loss of customer confidence.
The Six Sigma methodology has been widely promoted and adopted by world-class
manufacturing organizations due to its proven advantages in reducing waste, improving process
efficiency, and eliminating activities that do not add value to the process (Rifqi, 2021; Ahmed,
2019; Smith, 2011). Its successful application relies on the correct identification of critical
problems, the prioritization of improvement areas, and the implementation of structured actions
aimed at minimizing defects, errors, and variability while maximizing organizational profitability
(Coronado, 2002).
Among the different Six Sigma approaches, the DMAIC model (Define, Measure, Analyze,
Improve, and Control) provides a systematic framework for problem-solving based on data-driven
decision-making. This model enables a clear definition of the problem, the selection of
appropriate measurement tools, an in-depth analysis of process performance, the implementation
of improvement actions, and the establishment of control mechanisms to ensure long-term
sustainability (Chavez, 2025; Lynch, 2003; Monday, 2022).
Although literature reports numerous applications of DMAIC in textile and manufacturing
processes, most studies focus on final garment production or large-scale operations. Limited
research has specifically addressed the application of DMAIC in interlining manufacturing
processes, despite their critical role in garment quality. Therefore, this research aims to apply the
DMAIC Six Sigma methodology to improve quality in an interlining manufacturing process,
identifying the most frequent defects, analyzing their root causes, and proposing corrective and
preventive actions to increase the process sigma level and ensure compliance with customer
requirements.
It is important to highlight that a series of limitations may be presented by the application
of DMAIC, mainly because each of the phases must be used with the lean thinking philosophy.
Likewise, once the methodology is applied in an initial stage, a continuous improvement program
that uses the PDCA model (Plan, Do, Check, Act) is to be designed by management, thus allowing
of success of the improvement actions to be monitored and changes to be proposed based on the
variations that are undergone by the process over time (De Mast, 2012; Mandal, 2012).
INTRODUCTION
In the manufacturing sector, quality is considered a strategic indicator and one of the most
crucial elements, as it directly impacts customer satisfaction, cost reduction, and market
competitiveness (Evans, 2011). In the textile industry, ensuring product quality is essential,
especially for intermediate products such as interlinings, which play a fundamental role in
garment construction by providing structure, durability, and aesthetic appearance (Hayajneh,
2013). Any deviation in their quality may lead to rework, waste, and loss of customer confidence.
The Six Sigma methodology has been widely promoted and adopted by world-class
manufacturing organizations due to its proven advantages in reducing waste, improving process
efficiency, and eliminating activities that do not add value to the process (Rifqi, 2021; Ahmed,
2019; Smith, 2011). Its successful application relies on the correct identification of critical
problems, the prioritization of improvement areas, and the implementation of structured actions
aimed at minimizing defects, errors, and variability while maximizing organizational profitability
(Coronado, 2002).
Among the different Six Sigma approaches, the DMAIC model (Define, Measure, Analyze,
Improve, and Control) provides a systematic framework for problem-solving based on data-driven
decision-making. This model enables a clear definition of the problem, the selection of
appropriate measurement tools, an in-depth analysis of process performance, the implementation
of improvement actions, and the establishment of control mechanisms to ensure long-term
sustainability (Chavez, 2025; Lynch, 2003; Monday, 2022).
Although literature reports numerous applications of DMAIC in textile and manufacturing
processes, most studies focus on final garment production or large-scale operations. Limited
research has specifically addressed the application of DMAIC in interlining manufacturing
processes, despite their critical role in garment quality. Therefore, this research aims to apply the
DMAIC Six Sigma methodology to improve quality in an interlining manufacturing process,
identifying the most frequent defects, analyzing their root causes, and proposing corrective and
preventive actions to increase the process sigma level and ensure compliance with customer
requirements.
It is important to highlight that a series of limitations may be presented by the application
of DMAIC, mainly because each of the phases must be used with the lean thinking philosophy.
Likewise, once the methodology is applied in an initial stage, a continuous improvement program
that uses the PDCA model (Plan, Do, Check, Act) is to be designed by management, thus allowing
of success of the improvement actions to be monitored and changes to be proposed based on the
variations that are undergone by the process over time (De Mast, 2012; Mandal, 2012).
Vol. 12/ Núm. 4 2025 pág. 3662
DMAIC Cycle
Within the continuous improvement of six sigma projects, the acronym DMAIC has been widely
employed in conjunction with the PDCA cycle (Sokovic, 2010; Sin, 2015). Each of the letters
that make up the DMAIC has a particular objective and is served as the basis for the following
phase as shown below
● Define: Identify the need for change and the benefits from it.
● Measure: Quantify the actual state of the system by diagnostic and root causes
● Analyze: Compare the state of the system with the “ideal” state and determine corrective
and preventive actions
● Improve: Follow the actions and measure the results.
● Control: Continue with the successful actions and modify what is needed.
Define
The objective of this stage is to verify the actions necessary to resolve problems that are
considered critical for the organization and are directly related to the organization's available
resources. A strategic vision must be established, with a focus on external factors that generate
costs for the organization. This allows for the establishment of containment, correction, and
prevention actions, and internal cost problems can subsequently be resolved (Rahman, 2018).
Measure
In this stage, all available information on the process to be studied is gathered by the
organization, with a particular focus on information that will allow for a better understanding of
how stakeholders' expectations are being met. Statistical tools are often utilized, as well as core
tools such as failure mode and effect analysis (FMEA). Special care must be taken to include and
select information in an appropriate format that is to be presented to management so that
permanent containment actions can be established (Basios, 2017).
Analyze
In the analysis stage, different tools and methods are typically employed based on risk-
based analysis. Clear evidence must be presented for the interpretation of the results obtained in
the previous phase. For Sigma projects, the process capability is defined at the Sigma level,
which will serve as a performance indicator so that, once the improvement plan actions (PDCA)
have been implemented, the degree of impact of the Six Sigma project on the organization can
be measured (Beyene, 2016).
Improve
Considered the most important stage of the Six Sigma methodology, the carrying out of all
the management and execution actions necessary to improve the organization's functions,
financial aspects, and customer requirements is deemed important (Kurnıa, 2021). It is suggested
that the root causes of the problems must be resolved by the improvement plan (Nedra, 2019).
DMAIC Cycle
Within the continuous improvement of six sigma projects, the acronym DMAIC has been widely
employed in conjunction with the PDCA cycle (Sokovic, 2010; Sin, 2015). Each of the letters
that make up the DMAIC has a particular objective and is served as the basis for the following
phase as shown below
● Define: Identify the need for change and the benefits from it.
● Measure: Quantify the actual state of the system by diagnostic and root causes
● Analyze: Compare the state of the system with the “ideal” state and determine corrective
and preventive actions
● Improve: Follow the actions and measure the results.
● Control: Continue with the successful actions and modify what is needed.
Define
The objective of this stage is to verify the actions necessary to resolve problems that are
considered critical for the organization and are directly related to the organization's available
resources. A strategic vision must be established, with a focus on external factors that generate
costs for the organization. This allows for the establishment of containment, correction, and
prevention actions, and internal cost problems can subsequently be resolved (Rahman, 2018).
Measure
In this stage, all available information on the process to be studied is gathered by the
organization, with a particular focus on information that will allow for a better understanding of
how stakeholders' expectations are being met. Statistical tools are often utilized, as well as core
tools such as failure mode and effect analysis (FMEA). Special care must be taken to include and
select information in an appropriate format that is to be presented to management so that
permanent containment actions can be established (Basios, 2017).
Analyze
In the analysis stage, different tools and methods are typically employed based on risk-
based analysis. Clear evidence must be presented for the interpretation of the results obtained in
the previous phase. For Sigma projects, the process capability is defined at the Sigma level,
which will serve as a performance indicator so that, once the improvement plan actions (PDCA)
have been implemented, the degree of impact of the Six Sigma project on the organization can
be measured (Beyene, 2016).
Improve
Considered the most important stage of the Six Sigma methodology, the carrying out of all
the management and execution actions necessary to improve the organization's functions,
financial aspects, and customer requirements is deemed important (Kurnıa, 2021). It is suggested
that the root causes of the problems must be resolved by the improvement plan (Nedra, 2019).
Vol. 12/ Núm. 4 2025 pág. 3663
Control
The effectiveness of the measures established in the improvement plan is sought to be
monitored by the control phase while the future state of the system is simultaneously monitored
(avoiding the repetition of the same waste). The final stage of the original DMAIC cycle is
represented by it, but at the same time, a guideline is served by it to ensure that the organization's
objectives and goals are not deviated from (Adnan, 2010).
Literature review
The literature has widely reported the application of lean thinking and Six Sigma tools,
from the service industry to manufacturing and processing.
The six-sigma methodology has been successfully applied in foundry projects, with a focus
on customer complaints, an increase in process capability, a reduction in the defect rate, and a
reduction in customer complaints (Kumar, 2007).
Six-sigma methodology has been successfully implemented in the design of a fixture that
would reduce warp during the heating process of a heat treatment, achieving a new die design that
not only eliminates costs due to rework but also increases the efficiency of the process, presenting
significant savings (Kumar. 2009; Hernandez, 2025).
DMAIC applications are not limited to large companies with complex processes where
based on an Ishikawa, the main causes of defects were clearly identified by a CNC machining
company (Hiregoudar, 2011), leading to a reduction in manufacturing costs, an improvement in
the process, and the development of a training plan, as well as the implementation of a 5'S model
in small and medium-sized companies in India. It is worth noting that an increase in productivity
in medium-sized companies has been through the application of continuous improvement tools
such as the 5'S (Hernandez, 2025).
Researchers have emphasized the importance of integrating 4.0 technologies with the
DMAIC methodology for automated processes, where Machine Learning (ML) technology serves
as a tool to predict the weight of components based on prior statistical information, allowing for
the prediction and control of the amount of scrap generated in the process, as well as more precise
control of parameters (Martinez, 2025; Krauß, 2023).
Case Study: Background
This research project began with a meeting between the project team, senior management,
and production managers to analyze the main problems related to the interlining manufacturing
process. During the meeting, current procedures were reviewed, and a preliminary diagnosis of
production conditions was conducted. As a result, several common nonconformities were
identified that directly affect the quality of the final product, including stains, tears, and wrinkles
in the interlinings.
Control
The effectiveness of the measures established in the improvement plan is sought to be
monitored by the control phase while the future state of the system is simultaneously monitored
(avoiding the repetition of the same waste). The final stage of the original DMAIC cycle is
represented by it, but at the same time, a guideline is served by it to ensure that the organization's
objectives and goals are not deviated from (Adnan, 2010).
Literature review
The literature has widely reported the application of lean thinking and Six Sigma tools,
from the service industry to manufacturing and processing.
The six-sigma methodology has been successfully applied in foundry projects, with a focus
on customer complaints, an increase in process capability, a reduction in the defect rate, and a
reduction in customer complaints (Kumar, 2007).
Six-sigma methodology has been successfully implemented in the design of a fixture that
would reduce warp during the heating process of a heat treatment, achieving a new die design that
not only eliminates costs due to rework but also increases the efficiency of the process, presenting
significant savings (Kumar. 2009; Hernandez, 2025).
DMAIC applications are not limited to large companies with complex processes where
based on an Ishikawa, the main causes of defects were clearly identified by a CNC machining
company (Hiregoudar, 2011), leading to a reduction in manufacturing costs, an improvement in
the process, and the development of a training plan, as well as the implementation of a 5'S model
in small and medium-sized companies in India. It is worth noting that an increase in productivity
in medium-sized companies has been through the application of continuous improvement tools
such as the 5'S (Hernandez, 2025).
Researchers have emphasized the importance of integrating 4.0 technologies with the
DMAIC methodology for automated processes, where Machine Learning (ML) technology serves
as a tool to predict the weight of components based on prior statistical information, allowing for
the prediction and control of the amount of scrap generated in the process, as well as more precise
control of parameters (Martinez, 2025; Krauß, 2023).
Case Study: Background
This research project began with a meeting between the project team, senior management,
and production managers to analyze the main problems related to the interlining manufacturing
process. During the meeting, current procedures were reviewed, and a preliminary diagnosis of
production conditions was conducted. As a result, several common nonconformities were
identified that directly affect the quality of the final product, including stains, tears, and wrinkles
in the interlinings.
Vol. 12/ Núm. 4 2025 pág. 3664
These defects not only increase costs through rework and waste but also affect customer
perception and compromise product functionality. The team prioritized a detailed analysis of the
causes of these problems and proposed corrective and preventive actions to improve process
efficiency and ensure compliance with quality standards. Management expressed its willingness
to collaborate with the necessary resources, and key project stakeholders took specific
responsibilities, committing to work under a continuous improvement approach aligned with the
Six Sigma methodology.
Unlike previous studies, this research focuses on interlining manufacturing, a scarcely
documented textile sub-process which increases novelty due to main authors emphasizing the use
of lean tools in mostly automotive and aerospace industry. The objective of this research is to
improve the sigma level of the interlining manufacturing process through the DMAIC
methodology.
Interlinings, as intermediate products used primarily in the manufacture of collars, cuffs,
and hems, must have specific technical standards met, such as strength, adhesiveness, and
uniformity. The final appearance of garments and their functionality can be compromised by the
presence of defects such as stains, creases, or tears, generating rework, waste, and loss of customer
confidence (Montgomery, 2020).
METHODOLOGY
The first stage involved directly observing the production process, which included the
warping, gumming, weaving, dyeing, and napping stages. The team used verification sheets to
record the number and type of defects they identified in the interlinings produced during several
work shifts. The researchers randomly selected production batches for the sample to ensure
representative data.
For data analysis, we used an inspection sheet to record and classify the most frequently
observed defects (stains, creases, tears, etc.). Using an attribute control chart (p-chart), we
evaluated the proportion of defective products in 10 different batches, corresponding to 5
workdays with two shifts per day. We selected a batch size of 100 items by simple random
sampling.
The team then organized and analyzed the information using a Pareto chart to visualize
which defects accounted for the highest proportion of nonconformities. Following the 80/20
principle, they also determined the process's sigma level based on each defect. Once they
quantified the defects, they used an Ishikawa diagram to identify their root causes, classifying
them into categories such as materials, methods, machinery, labor, and environment, and
ultimately establishing an action plan.
These defects not only increase costs through rework and waste but also affect customer
perception and compromise product functionality. The team prioritized a detailed analysis of the
causes of these problems and proposed corrective and preventive actions to improve process
efficiency and ensure compliance with quality standards. Management expressed its willingness
to collaborate with the necessary resources, and key project stakeholders took specific
responsibilities, committing to work under a continuous improvement approach aligned with the
Six Sigma methodology.
Unlike previous studies, this research focuses on interlining manufacturing, a scarcely
documented textile sub-process which increases novelty due to main authors emphasizing the use
of lean tools in mostly automotive and aerospace industry. The objective of this research is to
improve the sigma level of the interlining manufacturing process through the DMAIC
methodology.
Interlinings, as intermediate products used primarily in the manufacture of collars, cuffs,
and hems, must have specific technical standards met, such as strength, adhesiveness, and
uniformity. The final appearance of garments and their functionality can be compromised by the
presence of defects such as stains, creases, or tears, generating rework, waste, and loss of customer
confidence (Montgomery, 2020).
METHODOLOGY
The first stage involved directly observing the production process, which included the
warping, gumming, weaving, dyeing, and napping stages. The team used verification sheets to
record the number and type of defects they identified in the interlinings produced during several
work shifts. The researchers randomly selected production batches for the sample to ensure
representative data.
For data analysis, we used an inspection sheet to record and classify the most frequently
observed defects (stains, creases, tears, etc.). Using an attribute control chart (p-chart), we
evaluated the proportion of defective products in 10 different batches, corresponding to 5
workdays with two shifts per day. We selected a batch size of 100 items by simple random
sampling.
The team then organized and analyzed the information using a Pareto chart to visualize
which defects accounted for the highest proportion of nonconformities. Following the 80/20
principle, they also determined the process's sigma level based on each defect. Once they
quantified the defects, they used an Ishikawa diagram to identify their root causes, classifying
them into categories such as materials, methods, machinery, labor, and environment, and
ultimately establishing an action plan.
Vol. 12/ Núm. 4 2025 pág. 3665
RESULTS AND DISCUSSION
Project Charter (Define)
The results obtained in this study demonstrate that the application of the DMAIC Six Sigma
methodology is an effective approach for identifying, prioritizing, and reducing defects in the
interlining manufacturing process. The analysis of 1,000 linear meters of interlinings revealed a
defect rate of 10%, which is considerably higher than the scrap levels of approximately 5%
reported in similar textile processes (Jiménez-Delgado, 2023; Mughal, 2021; González, 2023)
This finding justified the need for a structured improvement project.
In the kick-off meeting of this six-sigma project, the team established a Project charter that
defined and delimited the object of study. This tool serves as the basis for defining any
improvement project. (Prashar, 2014; Srivastava, 2021) present the information corresponding to
the definition phase in table 1.
Table 1
DMAIC Project Charter
Project title Minimize defects in interlinings.
Business Case A considerable number of errors were
found during the fabric production
process. A representative sample of
1,000 meters of interlinings was used
to quantify these to establish an
approximate production batch.
Goal Increase the process sigma level to 4.5
for each defect.
Metrics
(CTQ´s)
Primary Metric (sigma level)
Cost of quality ($)
Project Scope Management, project team,
production department.
Checklist (Measure)
During the analysis carried out at the plant, the team inspected 10 production batches
corresponding to one week of work, with a total of 1,000 linear meters of interlining inspected.
The sample size was selected based on industrial inspection standards and resource availability,
ensuring representativeness while minimizing production disruption. The team recorded the
defects found in Table 2 using the verification sheet.
RESULTS AND DISCUSSION
Project Charter (Define)
The results obtained in this study demonstrate that the application of the DMAIC Six Sigma
methodology is an effective approach for identifying, prioritizing, and reducing defects in the
interlining manufacturing process. The analysis of 1,000 linear meters of interlinings revealed a
defect rate of 10%, which is considerably higher than the scrap levels of approximately 5%
reported in similar textile processes (Jiménez-Delgado, 2023; Mughal, 2021; González, 2023)
This finding justified the need for a structured improvement project.
In the kick-off meeting of this six-sigma project, the team established a Project charter that
defined and delimited the object of study. This tool serves as the basis for defining any
improvement project. (Prashar, 2014; Srivastava, 2021) present the information corresponding to
the definition phase in table 1.
Table 1
DMAIC Project Charter
Project title Minimize defects in interlinings.
Business Case A considerable number of errors were
found during the fabric production
process. A representative sample of
1,000 meters of interlinings was used
to quantify these to establish an
approximate production batch.
Goal Increase the process sigma level to 4.5
for each defect.
Metrics
(CTQ´s)
Primary Metric (sigma level)
Cost of quality ($)
Project Scope Management, project team,
production department.
Checklist (Measure)
During the analysis carried out at the plant, the team inspected 10 production batches
corresponding to one week of work, with a total of 1,000 linear meters of interlining inspected.
The sample size was selected based on industrial inspection standards and resource availability,
ensuring representativeness while minimizing production disruption. The team recorded the
defects found in Table 2 using the verification sheet.
Vol. 12/ Núm. 4 2025 pág. 3666
Table 2
Interlining presented defects
Defect type Frequency
Stains 37
Creases 25
Tears 18
Lack of
adhesiveness
10
Textile pollution 7
Frays 3
Total 100
The table 2 clearly shows that most defects corresponded to stains, folds, and breaks with
37, 25, and 18 defects respectively, while the lack of adhesiveness occurred a total of 10 times;
finally, the exposure of textile contamination and fraying ranked as the least observed defects, so
clear evidence exists of what type of defects the present improvement project should focus on.
Researchers have reported scrap levels of 5% in the literature for different stages of a textile
process (Jiménez-Delgado, 2023) so that when they observe a total of 100 defects in 1000 meters
of interlinings, they find that the value of 10% is considerably high compared to what other
improvement projects have reported (Mughal, 2021; González, 2023).
Sigma Level (Measure)
Sigma level was calculated based on DPMO using standard Six Sigma conversion tables.
According to what is shown in Table 2, the defects and their frequency were quantified based on
the representative sample, and the sigma level of the process was also determined. As shown in
Table 2, the sigma level for defects was reported as 3.3, 3.5, 3.6, 3.8, 3.7 and 4.3 for stains, folds,
tears, lack of adhesiveness, textile contamination and fraying respectively. The management goal
is set at 4.5, with no value being recorded above the established goal.
Table 3
Sigma Level of the Process
Defect type Sigma Level Sigma Level
Goal
Stains 3.3 4.5
Tears 3.6 4.5
Lack of
adhesiveness
3.8 4.5
Textile pollution 3.7 4.5
Frays 4.3 4.5
Table 2
Interlining presented defects
Defect type Frequency
Stains 37
Creases 25
Tears 18
Lack of
adhesiveness
10
Textile pollution 7
Frays 3
Total 100
The table 2 clearly shows that most defects corresponded to stains, folds, and breaks with
37, 25, and 18 defects respectively, while the lack of adhesiveness occurred a total of 10 times;
finally, the exposure of textile contamination and fraying ranked as the least observed defects, so
clear evidence exists of what type of defects the present improvement project should focus on.
Researchers have reported scrap levels of 5% in the literature for different stages of a textile
process (Jiménez-Delgado, 2023) so that when they observe a total of 100 defects in 1000 meters
of interlinings, they find that the value of 10% is considerably high compared to what other
improvement projects have reported (Mughal, 2021; González, 2023).
Sigma Level (Measure)
Sigma level was calculated based on DPMO using standard Six Sigma conversion tables.
According to what is shown in Table 2, the defects and their frequency were quantified based on
the representative sample, and the sigma level of the process was also determined. As shown in
Table 2, the sigma level for defects was reported as 3.3, 3.5, 3.6, 3.8, 3.7 and 4.3 for stains, folds,
tears, lack of adhesiveness, textile contamination and fraying respectively. The management goal
is set at 4.5, with no value being recorded above the established goal.
Table 3
Sigma Level of the Process
Defect type Sigma Level Sigma Level
Goal
Stains 3.3 4.5
Tears 3.6 4.5
Lack of
adhesiveness
3.8 4.5
Textile pollution 3.7 4.5
Frays 4.3 4.5
Vol. 12/ Núm. 4 2025 pág. 3667
The importance of using the sigma level of a process as a key performance indicator to
measure the current state of the system and to be able to monitor the effect of improvement actions
on process optimization was highlighted previously (Patel, 2023). For the present research, it is
stated that the sigma level is at least above 3 for each of the defects, with which it is aimed that
this level is increased by a maximum of 1.5.
Pareto Diagram (Measure)
According to optimize the organization´s time and resources the team created a Pareto
diagram corresponding to all detected defects. It clearly shows that the three most common defects
are strains, creases and tears, representing 80% of the total defects, complying with the 80/20
principle. This indicates that by focusing on eliminating these three defects, the team could reduce
the number of defects by 80%. The investigation did not prioritize three defects that accounted
for 20% due to their low percentages, which were individually 10%, 7%, and 3%. The Pareto
analysis showed that stains, creases, and tears accounted for approximately 80% of total defects,
confirming the applicability of the 80/20 principle in this process. Similar patterns have been
reported in previous textile related Six Sigma studies, where a small number of defect types were
responsible for most nonconformities (Kumar. 2009; Hiregoudar, 2011), By focusing
improvement efforts on these critical defects, the project optimized the use of organizational
resources and maximized the potential impact of corrective actions.
Figure 1
Pareto Chart of Defects
Control Chart-P (Measure)
The use of p control charts allowed for the identification of abnormal variability in two
production batches, indicating the presence of special causes affecting process stability. This
result is consistent with findings reported by other researchers, who highlight the effectiveness of
attribute control charts in detecting process instability within Six Sigma projects [1–8]. The
identification of out-of-control points provided valuable information for directing root cause
The importance of using the sigma level of a process as a key performance indicator to
measure the current state of the system and to be able to monitor the effect of improvement actions
on process optimization was highlighted previously (Patel, 2023). For the present research, it is
stated that the sigma level is at least above 3 for each of the defects, with which it is aimed that
this level is increased by a maximum of 1.5.
Pareto Diagram (Measure)
According to optimize the organization´s time and resources the team created a Pareto
diagram corresponding to all detected defects. It clearly shows that the three most common defects
are strains, creases and tears, representing 80% of the total defects, complying with the 80/20
principle. This indicates that by focusing on eliminating these three defects, the team could reduce
the number of defects by 80%. The investigation did not prioritize three defects that accounted
for 20% due to their low percentages, which were individually 10%, 7%, and 3%. The Pareto
analysis showed that stains, creases, and tears accounted for approximately 80% of total defects,
confirming the applicability of the 80/20 principle in this process. Similar patterns have been
reported in previous textile related Six Sigma studies, where a small number of defect types were
responsible for most nonconformities (Kumar. 2009; Hiregoudar, 2011), By focusing
improvement efforts on these critical defects, the project optimized the use of organizational
resources and maximized the potential impact of corrective actions.
Figure 1
Pareto Chart of Defects
Control Chart-P (Measure)
The use of p control charts allowed for the identification of abnormal variability in two
production batches, indicating the presence of special causes affecting process stability. This
result is consistent with findings reported by other researchers, who highlight the effectiveness of
attribute control charts in detecting process instability within Six Sigma projects [1–8]. The
identification of out-of-control points provided valuable information for directing root cause
Vol. 12/ Núm. 4 2025 pág. 3668
analysis efforts. As shown in figure 2, which corresponds to a p control chart, we can see the
proportion of defective units per production batch. Each point corresponds to the percentage of
defects found in a batch of 100 randomly inspected units. The center line (CL) shows the process
mean, while the upper (UL) and lower (UL) lines represent the statistically calculated control
limits.
Based on the p chart, two batches out of 10 (20%) are out of control; batches 3 and 7 are
above the LCL, 0.14 and 0.12 respectively, indicating abnormal variability at those points in the
process. The goal is to verify whether this system’s behavior is due to a special cause. A
preliminary analysis suggests that the causes could be machine failures or human error, which
should be investigated to prevent recurrences. Researchers have reported that using attribute
control charts in Six Sigma projects is effective for measuring and, if that approach fails, taking
process control measures (Evans, 2011; Hayajneh, 2013; Rifqi, 2021; Ahmed, 2019; Smith,
2011).
Figure 2
Initial Control Chart-P of defects
Ishikawa diagram (Analyze)
Root cause analysis using the Ishikawa diagram revealed that the main sources of defects
were related to materials, methods, machinery, and personnel. In particular, inconsistent raw
material quality, inadequate cleaning procedures, insufficient training, and poor environmental
conditions were identified as critical contributors to staining defects. These results align with
previous studies in textile manufacturing, which emphasize the influence of material handling,
equipment cleanliness, and operator training on product quality (Nedra, 2019; Kumar, 2007).
Figure 3 shows the Ishikawa diagram, developed to analyze the root causes of the most common
defect: staining. Possible causes were identified, grouped into six classic categories: materials,
methods, labor, machinery, environment, and measurement.
analysis efforts. As shown in figure 2, which corresponds to a p control chart, we can see the
proportion of defective units per production batch. Each point corresponds to the percentage of
defects found in a batch of 100 randomly inspected units. The center line (CL) shows the process
mean, while the upper (UL) and lower (UL) lines represent the statistically calculated control
limits.
Based on the p chart, two batches out of 10 (20%) are out of control; batches 3 and 7 are
above the LCL, 0.14 and 0.12 respectively, indicating abnormal variability at those points in the
process. The goal is to verify whether this system’s behavior is due to a special cause. A
preliminary analysis suggests that the causes could be machine failures or human error, which
should be investigated to prevent recurrences. Researchers have reported that using attribute
control charts in Six Sigma projects is effective for measuring and, if that approach fails, taking
process control measures (Evans, 2011; Hayajneh, 2013; Rifqi, 2021; Ahmed, 2019; Smith,
2011).
Figure 2
Initial Control Chart-P of defects
Ishikawa diagram (Analyze)
Root cause analysis using the Ishikawa diagram revealed that the main sources of defects
were related to materials, methods, machinery, and personnel. In particular, inconsistent raw
material quality, inadequate cleaning procedures, insufficient training, and poor environmental
conditions were identified as critical contributors to staining defects. These results align with
previous studies in textile manufacturing, which emphasize the influence of material handling,
equipment cleanliness, and operator training on product quality (Nedra, 2019; Kumar, 2007).
Figure 3 shows the Ishikawa diagram, developed to analyze the root causes of the most common
defect: staining. Possible causes were identified, grouped into six classic categories: materials,
methods, labor, machinery, environment, and measurement.
Vol. 12/ Núm. 4 2025 pág. 3669
In the materials category, inconsistent quality in polyester batches and oil residue on the
reels were considered; in the methods category, lack of cleaning between batches and improper
adjustment of drying times; in the labor category, lack of training in the handling of the gumming
system; in the machinery category, rollers with accumulated residue were detected; in the methods
category, poor cleaning between batches; in the environment category, excess moisture in the
gumming area; and in the measurement category, the lack of standardized controls during
inspection (Kurma, 2021).
Figure 3
Fishbone Diagram
Action Plan (Improve)
For the action plan, management, along with those responsible for the project and
production, developed a series of recommendations based on containment, correction, and
prevention actions. Table 4 mentions these actions.
Table 4
Improvement actions for the problems detected
# Problem Action
1 Measure: Lack of
standardized visual
controls
Management, in conjunction with the production department, will
develop a series of visual aids and controls to assist workers in their
daily tasks. They will constantly monitor the system's status to make
improvements if necessary.
2 Material: Inconsistent
quality on polyester
batches
The team developed a procedure that details the quality
characteristics that the material must meet.
In the materials category, inconsistent quality in polyester batches and oil residue on the
reels were considered; in the methods category, lack of cleaning between batches and improper
adjustment of drying times; in the labor category, lack of training in the handling of the gumming
system; in the machinery category, rollers with accumulated residue were detected; in the methods
category, poor cleaning between batches; in the environment category, excess moisture in the
gumming area; and in the measurement category, the lack of standardized controls during
inspection (Kurma, 2021).
Figure 3
Fishbone Diagram
Action Plan (Improve)
For the action plan, management, along with those responsible for the project and
production, developed a series of recommendations based on containment, correction, and
prevention actions. Table 4 mentions these actions.
Table 4
Improvement actions for the problems detected
# Problem Action
1 Measure: Lack of
standardized visual
controls
Management, in conjunction with the production department, will
develop a series of visual aids and controls to assist workers in their
daily tasks. They will constantly monitor the system's status to make
improvements if necessary.
2 Material: Inconsistent
quality on polyester
batches
The team developed a procedure that details the quality
characteristics that the material must meet.
Vol. 12/ Núm. 4 2025 pág. 3670
3 Material: Oil residue on
reels
To eliminate dirt caused by oils and/or residues, an awareness
campaign on industrial hygiene was carried out.
4 Personnel: Poor machine
cleaning training
The maintenance department, in conjunction with quality, developed
training in autonomous maintenance.
5 Personnel: Frequent staff
turnover
The future plan proposes that the HR department conduct an analysis
of the reasons for high staff turnover.
6 Environment: High
humidity in the gumming
area
Researchers will conduct a subsequent study to establish corrective
actions.
7 Methods: Improperly set
drying times
The methods department conducted a study to determine the ideal
drying time.
8 Methods: Lack of
cleaning between batches
Maintenance and Production established a cleaning procedure as part
of autonomous maintenance.
9 Machines: Rollers with
accumulated residue
As with cause number 8, both problems were corrected by the
cleaning procedure.
1
0
Machines: Leaking steam
valves
A preventive maintenance plan was developed to address this
situation.
Control Chart-P (Control)
Once the improvement actions were implemented, a P control chart was created again to
monitor the system status, as shown in Figure 4. The process variation was decreased because of
the implementation of the improvement actions. P charts have been successfully used to measure
process variation and to have a detailed control of the control of changes in a process (Jimenez,
2023; Patel, 2023),
Figure 4
Improve Control Chart-P of defects
3 Material: Oil residue on
reels
To eliminate dirt caused by oils and/or residues, an awareness
campaign on industrial hygiene was carried out.
4 Personnel: Poor machine
cleaning training
The maintenance department, in conjunction with quality, developed
training in autonomous maintenance.
5 Personnel: Frequent staff
turnover
The future plan proposes that the HR department conduct an analysis
of the reasons for high staff turnover.
6 Environment: High
humidity in the gumming
area
Researchers will conduct a subsequent study to establish corrective
actions.
7 Methods: Improperly set
drying times
The methods department conducted a study to determine the ideal
drying time.
8 Methods: Lack of
cleaning between batches
Maintenance and Production established a cleaning procedure as part
of autonomous maintenance.
9 Machines: Rollers with
accumulated residue
As with cause number 8, both problems were corrected by the
cleaning procedure.
1
0
Machines: Leaking steam
valves
A preventive maintenance plan was developed to address this
situation.
Control Chart-P (Control)
Once the improvement actions were implemented, a P control chart was created again to
monitor the system status, as shown in Figure 4. The process variation was decreased because of
the implementation of the improvement actions. P charts have been successfully used to measure
process variation and to have a detailed control of the control of changes in a process (Jimenez,
2023; Patel, 2023),
Figure 4
Improve Control Chart-P of defects
Vol. 12/ Núm. 4 2025 pág. 3671
Sigma Level (Control)
As a result of the implementation of improvement actions, the process sigma level index
was increased from 3.3, 3.5, 3.6, 3.8, 3.7, and 4.3 to 3.5, 3.7, 3.9, 4.0, 4.2, and 4.5, respectively.
Increases in quality and productivity in various areas have been led to by the implementation of
DMAIC, resulting in the sigma level of the processes being increased.
Table 4
Sigma Level of the Process
Defect type Sigma level
before
Sigma level
after
Stains 3.3 3.5
Creases 3.5 3.7
Tears 3.6 3.9
Lack of
adhesiveness
3.8 4.0
Textile
pollution
3.7 4.2
After implementing the proposed corrective and preventive actions, improvements were
observed in the sigma levels of all defect categories. The sigma level increased by up to 0.6 in
some cases, demonstrating the positive impact of the DMAIC methodology on process
performance. However, the target sigma level of 4.5 was achieved only for one defect type (frays),
indicating that while significant progress was made, further improvement cycles are required.
Similar limitations have been reported in other DMAIC-based case studies, where incremental
improvements are achieved through successive PDCA cycles rather than a single intervention
(Krauß , 2023; Patel, 2023). Overall, the results confirm that integrating DMAIC with continuous
monitoring tools such as PDCA and control charts provides a robust framework for quality
improvement in textile manufacturing processes, particularly in intermediate products such as
interlinings.
Although the DMAIC methodology proved effective in reducing defect rates and
increasing sigma levels, the results indicate that achieving the target sigma level for all defect
categories within a single improvement cycle is challenging in interlining manufacturing
processes. Defects associated with raw material variability and environmental conditions showed
slower improvement rates, suggesting the need for sustained corrective actions and stronger
process controls. These findings emphasize that DMAIC should be complemented with
continuous improvement mechanisms such as PDCA cycles, preventive maintenance programs,
and operator training to ensure long-term process stability. Therefore, quality improvement in
Sigma Level (Control)
As a result of the implementation of improvement actions, the process sigma level index
was increased from 3.3, 3.5, 3.6, 3.8, 3.7, and 4.3 to 3.5, 3.7, 3.9, 4.0, 4.2, and 4.5, respectively.
Increases in quality and productivity in various areas have been led to by the implementation of
DMAIC, resulting in the sigma level of the processes being increased.
Table 4
Sigma Level of the Process
Defect type Sigma level
before
Sigma level
after
Stains 3.3 3.5
Creases 3.5 3.7
Tears 3.6 3.9
Lack of
adhesiveness
3.8 4.0
Textile
pollution
3.7 4.2
After implementing the proposed corrective and preventive actions, improvements were
observed in the sigma levels of all defect categories. The sigma level increased by up to 0.6 in
some cases, demonstrating the positive impact of the DMAIC methodology on process
performance. However, the target sigma level of 4.5 was achieved only for one defect type (frays),
indicating that while significant progress was made, further improvement cycles are required.
Similar limitations have been reported in other DMAIC-based case studies, where incremental
improvements are achieved through successive PDCA cycles rather than a single intervention
(Krauß , 2023; Patel, 2023). Overall, the results confirm that integrating DMAIC with continuous
monitoring tools such as PDCA and control charts provides a robust framework for quality
improvement in textile manufacturing processes, particularly in intermediate products such as
interlinings.
Although the DMAIC methodology proved effective in reducing defect rates and
increasing sigma levels, the results indicate that achieving the target sigma level for all defect
categories within a single improvement cycle is challenging in interlining manufacturing
processes. Defects associated with raw material variability and environmental conditions showed
slower improvement rates, suggesting the need for sustained corrective actions and stronger
process controls. These findings emphasize that DMAIC should be complemented with
continuous improvement mechanisms such as PDCA cycles, preventive maintenance programs,
and operator training to ensure long-term process stability. Therefore, quality improvement in
Vol. 12/ Núm. 4 2025 pág. 3672
intermediate textile processes should be approached as an iterative and systematic effort rather
than a one-time intervention.
CONCLUSIONS
This study applied the DMAIC Six Sigma methodology to improve quality in an interlining
manufacturing process, enabling the systematic identification, analysis, and reduction of the most
frequent defects affecting the final product. Through the use of statistical and quality tools, critical
defects were prioritized, and their root causes were identified, allowing for the development of
targeted corrective and preventive actions.
The implementation of the improvement actions led to a measurable increase in the process
sigma level for all defect categories. Although the target sigma level of 4.5 was achieved only for
one defect type, the overall improvement demonstrates the effectiveness of the DMAIC
methodology as a structured approach for quality improvement in textile manufacturing
processes.
It is important to emphasize that continuous monitoring of the implemented actions is
required to ensure their long-term effectiveness. The integration of DMAIC with the PDCA cycle
provides a continuous improvement framework that allows organizations to evaluate
performance, detect deviations, and implement additional corrective actions as needed.
Future research should focus on conducting successive PDCA cycles to further increase
the sigma level of the remaining defects, as well as on integrating advanced tools such as digital
monitoring systems or Industry 4.0 technologies to enhance process control and predictive
capabilities in interlining manufacturing.
Acknowledgements
The authors A.B.R. and K.P.M.O. would like to thank Mtro. Luis Ricardo Martínez
Pacheco for his teachings, wisdom, and patience. They also extend their gratitude to the interlining
company that opened its doors for the development of this work and the collection of information.
intermediate textile processes should be approached as an iterative and systematic effort rather
than a one-time intervention.
CONCLUSIONS
This study applied the DMAIC Six Sigma methodology to improve quality in an interlining
manufacturing process, enabling the systematic identification, analysis, and reduction of the most
frequent defects affecting the final product. Through the use of statistical and quality tools, critical
defects were prioritized, and their root causes were identified, allowing for the development of
targeted corrective and preventive actions.
The implementation of the improvement actions led to a measurable increase in the process
sigma level for all defect categories. Although the target sigma level of 4.5 was achieved only for
one defect type, the overall improvement demonstrates the effectiveness of the DMAIC
methodology as a structured approach for quality improvement in textile manufacturing
processes.
It is important to emphasize that continuous monitoring of the implemented actions is
required to ensure their long-term effectiveness. The integration of DMAIC with the PDCA cycle
provides a continuous improvement framework that allows organizations to evaluate
performance, detect deviations, and implement additional corrective actions as needed.
Future research should focus on conducting successive PDCA cycles to further increase
the sigma level of the remaining defects, as well as on integrating advanced tools such as digital
monitoring systems or Industry 4.0 technologies to enhance process control and predictive
capabilities in interlining manufacturing.
Acknowledgements
The authors A.B.R. and K.P.M.O. would like to thank Mtro. Luis Ricardo Martínez
Pacheco for his teachings, wisdom, and patience. They also extend their gratitude to the interlining
company that opened its doors for the development of this work and the collection of information.
Vol. 12/ Núm. 4 2025 pág. 3673
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Vol. 12/ Núm. 4 2025 pág. 3675
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and its holistic assessment. International Journal of Quality Engineering and Technology,
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manufacturing engineering, 43(1), 476-483.
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quality and improving process output by adopting a Six Sigma DMAIC tool: a case study.
International Journal of Six Sigma and Competitive Advantage, 13(1-3), 195-216.
Patel, M. T., & Desai, D. (2023). Implementation of Six Sigma in the small-scale ceramic industry
and its holistic assessment. International Journal of Quality Engineering and Technology,
9(2), 110-144.
Prashar, A. (2014). Adoption of Six Sigma DMAIC to reduce cost of poor quality. International
Journal of Productivity and Performance Management, 63(1), 103-126.
Rahman, A., Shaju, S. U. C., Sarkar, S. K., Hashem, M. Z., Hasan, S. K., & Islam, U. (2018).
Application of six sigma using define measure analyze improve control (DMAIC)
methodology in garment sector. Independent Journal of Management & Production, 9(3),
810-826.
Rifqi, H., Zamma, A., Souda, S. B., & Hansali, M. (2021). Lean manufacturing implementation
through DMAIC approach: A case study in the automotive industry. Quality Innovation
Prosperity, 25(2), 54-77.
S. Kumar, M. Sosnoski, Using DMAIC six Sigma to systematically improve shopfloor production
quality and costs. Int. J. Productivity Perform. Manage. 58(3), 254–273 (2009).
https://doi.or g/10.1108/17410400910938850.
Sin, A. B., Zailani, S., Iranmanesh, M., & Ramayah, T. (2015). Structural equation modelling on
knowledge creation in Six Sigma DMAIC project and its impact on organizational
performance. International Journal of Production Economics, 168, 105-117.
Smith, C., Wood, S., & Beauvais, B. (2011). Thinking lean: implementing DMAIC methods to
improve efficiency within a cystic fibrosis clinic. The Journal for Healthcare Quality
(JHQ), 33(2), 37-46.
Sokovic, M., Pavletic, D., & Pipan, K. K. (2010). Quality improvement methodologies–PDCA
cycle, RADAR matrix, DMAIC and DFSS. Journal of achievements in materials and
manufacturing engineering, 43(1), 476-483.
Srivastava, P., Alrasheedi, M., Kaushik, P., & Sharma, S. (2021). Reducing the cost of poor
quality and improving process output by adopting a Six Sigma DMAIC tool: a case study.
International Journal of Six Sigma and Competitive Advantage, 13(1-3), 195-216.