Nursing homework help

A DMAIC Six Sigma approach to
quality improvement in the
anodising stage of the amplifier
production process
Pallavi Sharma and Suresh Chander Malik
Department of Statistics, Maharishi Dayanand University Rohtak, Rohtak, India
Anshu Gupta
School of Business, Public Policy and Social Entrepreneurship,
Ambedkar University Delhi, Kashmere Gate Campus, Delhi, India, and
P.C. Jha
Department of Operational Research, Faculty of Mathematical Sciences,
University of Delhi, New Delhi, India
Purpose – The purpose of this paper is to study the anodising process of a portable amplifier production
process to identify and eliminate the sources of variations, in order to improve the process productivity.
Design/methodology/approach – The study employs the define-measure-analyse-improve-control
(DMAIC) Six Sigma methodology. Within the DMAIC framework various tools of quality management
such as SIPOC analysis, cause and effect diagram, current reality tree, etc., are used in different stages.
Findings – High rejection rate was found to be the main problem leading to lower productivity of the
process. Four types of defects were identified as main cause of rejections in the baseline process. Pareto
analysis resulted in detection of the top defects, which were then analysed in details to find the root cause of
the problem. Further study resulted in finding improvement measures that were discussed with the
management before implementation. The process is sampled again to check the improvements, and control
measures were established.
Practical implications – The study provides a framework for implementation of DMAIC Six Sigma
methodology for a manufacturing firm. The results presented are based on the data collected from the shop
floor. Results and findings of the study were implemented for quality improvement of the process.
Originality/value – The study is based on an original research conducted with the objective of quality
improvement in the anodising process of the production process. Besides presenting an approach to DMAIC
Six Sigma methodology, an application of the current reality tree tool for root cause analysis is presented, a
tool used limitedly in the Six Sigma studies. The tool finds its uniqueness in its ability to address problems
relating multiple factors than isolated factors.
Keywords Six Sigma, DMAIC methodology, Quality improvement
Paper type Research paper
1. Introduction
The “Make in India” programme, launched in India in September 2014 focusses on growth of
the manufacturing sector in the country. Currently, manufacturing in India accounts for
16 per cent of the GDP (Shiralashetti, 2012). The programme envisions, increasing the GDP
contribution of the manufacturing sector to 25 per cent by the year 2025, create huge pool of
employment and self-employment opportunities; and thereby improving the economic
health of the country. One of the important step manufacturers are following under this
initiative is investment in methodologies and tools for process reengineering and quality
improvement. India has a large micro, small and medium sized enterprises (MSME) base
and accounts for 45 per cent industrial output ( Javalgi and Todd, 2011; Katyal and Xaviour,
2015). The major challenges faced by these units are competition from national and global
International Journal of Quality &
Reliability Management
Vol. 35 No. 9, 2018
pp. 1868-1880
© Emerald Publishing Limited
DOI 10.1108/IJQRM-08-2017-0155
Received 16 August 2017
Revised 23 November 2017
Accepted 3 December 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
players and limited availability of monetary, personnel, technological and other recourses.
In the demand-driven economy, the path of growth for the MSME units also relates to the
adoption of practices that facilitate production of reliable and quality products. Several
MSME’s have realised the situation and have started adopting quality management
practices. This research presents an application of Six Sigma define-measure-analyseimprove-control (DMAIC) approach for improving the process quality of anodising stage of
an amplifier production process. The case study presented here is based on real-life data of
an amplifier production unit.
The firm is interested in reducing the process variation and defects. In this direction a
quality improvement project is initiated. The amplifier production process adopted by the
firm is designed in seven sub-processes (stages). It is planned to execute the project in stages
considering one stage at a time for quality improvement using Six Sigma DMAIC
methodology (Hamza, 2008). The project started with quality improvement efforts applied to
the chassis preparation stage (Gupta et al., 2016). The study presents the implementation of
Six Sigma programme to the anodising stage of the production process that follows the
chassis preparation.
There are numerous approaches for quality management and improvement including
statistical quality control, Six Sigma, zero defects, total quality management, quality circle,
etc. Since inception in 1980s by Bill Smith at Motorola (Barney, 2002) for quality
improvement of manufacturing operations, Six Sigma have been successfully used for
quality improvement projects in several types of business functions such as purchasing,
finance, service, marketing, etc. Six Sigma DMAIC methodology is a combination of
statistical and managerial methods that aims at reducing the process variation (Evans and
Lindsay, 2014). The variability reduction is achieved by systematic identification of the
causes of variation and implementing corrective measures such that the process yield is
improved. Manufacturing organisations continued adopting Six Sigma as a process
improvement and defect reduction approach to waste elimination (Swarnakar and Vinodh,
2016). Following section briefly discusses the literature review related to the Six Sigma
approaches and applications.
1.1 Literature review
In the literature, several research and case studies discuss the implementation of Six Sigma
approach for quality improvement in manufacturing settings such as Banuelas et al. (2005),
Desai (2006), Kumar et al. (2007, 2011), Lee et al. (2009), Kumar and Sosnoski (2009), Gijo et al.
(2011), Thakore et al. (2014), Zhang et al. (2015) and Antony et al. (2016). The approach is
applied successfully in manufacturing settings of different scale. Banuelas et al. (2005)
discussed a case study illustrating implementation of Six Sigma to reduce waste in a coating
process. The authors used the DMAIC Six Sigma framework as a tool to uncover the causes of
unknown problems. Desai (2006) presented a roadmap for application of DMAIC Six Sigma
programme for a small-scale industry. A case study is discussed for improving the customer
delivery operations. Kumar et al. (2007) applied DMAIC Six Sigma methodology for reducing
the casting defects in an automotive engine production process. The authors discussed
management commitment and involvement, linking Six Sigma project goals with customer
requirements and business strategy, training and skill development of employees are some of
the critical success factors of a Six Sigma programme. Kumar and Sosnoski (2009) discussed
the application of Six Sigma DMAIC methodology for improving the quality and cost of shop
floor of Wilson Tool Company. The application of Six Sigma programme resulted in reducing
scrap and non-value added activities achieved using the quality management tools
brainstorming, process mapping, fishbone diagrams, histograms and control chart.
Gijo et al. (2011) demonstrated the application of Taguchi method and design of experiments
(DOE) for reducing the defects in a fine grinding process of an automotive company.
The study due to Kumar et al. (2011) discussed a framework for deployment of
Six Sigma in context of SMEs based on a survey, case studies conducted in ten SME’s
located in Scotland and England and secondary research. A five phase framework consisting of
stages – readiness for Six Sigma, prepare, initialise, institutionalise and sustain – is proposed
in the study. Zhang et al. (2015) discussed the systematic implementation of DMAIC
Six Sigma methodology for controlling the thickness variation of cold rolling stainless steel
sheet for a leading stainless steel manufacturer in China. The study uses the tools such as cause
and effect (C&E) diagram and matrix, FMEA to identify the key factors related to main defect
which are analysed using different tools such as one-way ANOVA and regression method and
DOE is conducted to optimise the process parameters. The study reported reduction of over
thinness in sheets from 40 to 5 per month, leading to reduction by 57.6 per cent in the cost
due to quality rejections per year. Similar to the studies cited above several other case studies in
the literature discusses application of Six Sigma for manufacturing firms and various tools
used in the DMAIC phases.
This study uses a combination of classical and advanced tools for DMAIC Six Sigma
implementation in the anodising stage of the amplifier production process.
2. Research methodology
This research attempts to implements DMAIC model of Six Sigma to reduce the defects
in the anodising stage of a portable amplifier. The results presented in the study are based
on the analysis conducted on the anodising process shop floor of the firm under
consideration. Before the project is executed, detailed review of literature is conducted to
study the basic framework of Six Sigma approach for MSMEs (Kumar et al., 2011) and the
various tools that can be used in each stage of the DMAIC model. DMAIC is a sequential
model consisting of five stages (define, measure, analyse, improve and control) wherein each
stage has a well-defined objective, requires inputs and using the appropriate tools output of
that stage is generated following a plan-do-check-act (PDCA) framework. Based on the
review of the literature and a brainstorming session between the members of the project
team quality management tools to be used in each stage of the study are selected. In this
study methods and tools such as SIPOC analysis, Pareto analysis, control charts, C&E
diagrams and current reality tree (CRT) are used in the different stages of the DMAIC model.
3. Case study
The implementation of the DMAIC Six Sigma quality improvement programme on
anodising process of amplifier production is presented in this section. Each phase of the
DMAIC model is discussed with data analysis, results and discussion.
3.1 Define
Define phase of DMAIC model focusses on developing the project charter (Figure 1). The
problem statement, objective, project team, timeline, execution plan, goal(s) and expected
outcomes consistent with the customer requirement and business strategy are defined in the
define phase (Antony et al., 2012). The anodising process is the second stage of the seven stage
amplifier production process. The process produces a durable, corrosion-resistant translucent
film of aluminium oxide on the surface of the base metal of the prepared amplifier chassis. It
also gives better finish on the surface. On completion of each stage of the amplifier production
process a quality check is conducted ( for details see Gupta et al., 2016). The defective pieces
are removed from the lot and then the lot moves for further processing. The management
wants to improve the productivity of the production process. As discussed earlier, the project
is executed in stages targeting the defect reduction of one stage at a time. The project team
included quality head, representatives of purchasing, production (shop floor), stores, quality
and maintenance team, and design engineer of the firm along with the researchers. In the
initial brainstorming session among the team members, higher rejection rate is identified as
the main problem leading to lower productivity of the process.
The objective of the study is set to identify the causes of defects in the anodising process
and recommend corrective measures for process improvement. The current process
performance is used to establish the numerical goals in terms of target sigma level (see
measure phase). The project execution started with structured recording of the key
suppliers, inputs, process mapping, defining output(s), customers and identifying elements
critical to quality using SIPOC analysis. This followed computing the baseline process
performance (sigma level) based on sampling and identifying the top defects using Pareto
analysis. The potential causes of top defect are analysed using C&E diagrams. The CRT
tool is used for root cause analysis. The team with the help of design engineers, and further
observations and analysis gave recommendations for process improvement. The
suggestions accepted by the management were implemented and the process was
sampled again to measure the process performance post improvement and control.
In order to improve the quality of a process it is imperative to understand the process
design, key process elements, inputs, outputs, defects occurring in the process and their
causes. The SIPOC analysis is a tool, that is, used to define and document a process
including its supplier as well as customer(s) (Yeung, 2009). Figure 2 shows the SIPOC
diagram for the anodising process.
The process of anodising chassis surface is a three step process – pre-treatment,
anodising and sealing. Rinsing of the surface with deionised water is carried between
Project title: Defect reduction in Anodising process
Project objective and goal: Reduce the Black star pitting and Pitting defects in the
process by 50%
Project location: An Amplifier Production facility in Delhi NCR, India
Rational for project selection:
Pitting and BSP defects accounts for 69.45% defects
Expected benefits: Reduction in rejections from the process output
Methodology: DMAIC Six Sigma approach
Project timeline: Four months
Project team:
Quality head
Quality control inspector
Research advisor and associates
Floor operators
Managers from production, purchasing, store and maintenance
Process engineer
Baseline process Performance:
Black star pitting (BSP) defects 37.13%
Pitting defects 32.33%
Others 30.53%
Figure 1.
Project charter
Chassis stage for
prepared chassis
Prepared chassis
ready for anodising
alkaline detergent
Deionised water
Equipments used
for anodising
Chassis to be
delivered to the
powder coating
stage of
Supplier A
Supplier B
Supplier C
Supplier D
Input Process Output Customer
Figure 2.
SIPOC diagram of
anodising process
these stages. The pre-treatment process prepares the surface for electrochemical anodising.
Any kind of debris, residual oil and corrosion on the surface of chassis are removed by
cleaning in non-etching, alkaline detergent heated to approximately 63 degree Celsius. On
the surface of the pre-treated chassis a coating of aluminium oxide is formed using an
aluminium substrate by the electrochemical conversion process. The porous aluminium
oxide layer formed in the electrochemical conversion is sealed by boiling chassis in
deionised water in the last stage of the process. The whole process is carried in a controlled
environment. Rest of the key elements of the process are listed in Figure 2.
The rejected pieces available in the inventory were inspected and four types of defects,
namely – pitting, streaking, black star pitting (BSP) and crazing, are observed. For detailed
explanation of these defects the reader can refer to Qamar et al. (2004). The study is
completed in four months.
3.2 Measure
In the measure phase, the current sigma level of the process is measured, categorising the
items as defective and non-defective based on the four types of defects discussed above. Due
to absence of the any past record of process rejections, sampling is conducted to determine
the current sigma level of the process and status of process control. Pareto analysis is
conducted to prioritise the defects to be controlled in the study (Montgomery, 2007).
Data collection. Anodising process is observed for 20 days and 100 per cent inspection of
the process output is conducted. Process specifications are followed to inspect the items for
pitting, streaking, BSP and crazing defects. In total, 8,795 units are observed in 20 days
sampling that resulted into rejection of 149 units. The sampling data are recorded using
check sheets (represented graphically in Figure 3).
The short-term sigma level of the baseline process is estimated to be 3.62 (16,941.44 parts per
million). To ascertain whether current process is in control or not, p-charts for the attribute data
are drawn (Figure 4). The p-chart shows the anodising process is in control with average
fraction defective value 0.0169. Further Pareto analysis is conducted and Pareto diagram is
drawn (Figure 5). BSP and pitting together accounted to 69.45 per cent of total defectives (BSP
37.12 per cent and pitting 32.33 per cent) while rest of the 30.55 per cent of the defectives were
due to streaking and crazing defects. In a meeting of the project team with the higher
management it is decided that the study will focus on identifying the root causes of the
defects – BSP and pitting only, which form the top causes of variations in the process. BSP is
characterised as star shaped black coloured pits on the surface of the anodic film while pitting
No. of defects
Sample number
Total defectives Crazing
Black star pitting Streaking
Figure 3.
Sampling data
(measure phase)
defects are tiny white or light grey corrosion marks that usually originate before electrochemical
anodising. In line with the previous study of the chassis preparation stage the goal is set to
reduce the percentage defective due to these two types of defects by 50 per cent.
3.3 Analyse
C&E analysis (Hagemeyer et al., 2006) is an important tool of quality management for
identifying the potential causes of the defects in a process. The tool classifies the potential
causes under all or some of the generic causes – methods, machines, manpower, material,
measurement, maintenance and environment. Once the potential causes are identified
further analysis is conducted to deduce the root causes. Several tools are discussed in
literature (Doggett, 2003; Andersen and Fagerhaug, 2006) to analyse the potential causes of
non-conformities for root cause(s) of the problem(s). In this study, the CRT (Doggett, 2005)
tool is used for root cause analysis. As the potential causes could be related and
interdependent, CRT finds its uniqueness in its ability to address problems relating multiple
factors than isolated factors. It also links the undesirable effects with the core problem and
thus also helps the practitioners to develop solutions to the core problems.
The project team conducted brainstorming sessions along with process suppliers and
engineers, and closely observed the shop floor to draw the C&E diagrams and CRT maps.
The detailed C&E diagrams are shown in Figures 6 and 7.
Potential causes identified in C&E analysis are studied in details to develop CRT maps.
The CRT diagrams are shown in Figures 8 and 9. In the CRT maps possible root causes
related to each effect are identified for both types of defects (highlighted in light grey colour
boxes). The boxes highlighted in dark grey colour shows the root causes which are not
under the control of the production facility.
1 3 5 7 9 11 13 15 17 19
Sample number
Figure 4.
(measure phase)
Pitting Streaking Black star pitting Crazing
No. of defects
Defect type
No. of defects Cummulative %
Figure 5.
Pareto diagram
(measure phase)
3.4 Improve
The improve phase of the DMAIC model aims to find the solutions that can be implemented in
the current process to eliminate the root causes of non-conformities (undesirable effects). Though
BSP and pitting are two different types of defects, while developing the CRT maps some
common causes were diagnosed for both. Quality of the deionised water used in the process in
different steps, setting of anodising parameters, handling and facility conditions are some of the
common causes that may lead to defects in the finished product if quality is not maintained.
Temperature of the facility
Air Contaminants
Electrolyte Tank
Filter System
Setting of Anodising
Lack of Training
Chloride level high in
Deionised water
Chloride level high in
Electrolyte solution
Improper Handling
Machine Measurement Manpower
Figure 6.
Cause and effect
diagram for black star
pitting defect
Chassis Surface
Cleaning Detergent
Deionised water
ion component
Etch Staining
Filter Handling
Facility of Temperature
Acidic/Alkaline mist
Figure 7.
Cause and effect
diagram for
pitting defect
Black star
process incorrect
Water filter not
functioning as
per specification
Facilities environment
conditions not
may be
Acid mist
may be
present in
the facility
Tanks not
Variations in
the temperature
Lack of
not follow
There may be
delay in removing
chassis from the
electrolyte solution
Regular cleaning
and replacement
of filter candles
not done
High chloride
level in tap
Filter settings
may not
Electrolyte solution
Concentration can
be high
Salt can
deposit on
walls of tanks
settings may
be incorrect
settings not
Regular testing
of water quality
not conducted
High level of
Figure 8.
Current reality
tree for defect
black star pitting
From the literature (Zhu et al., 2011) and discussions it was observed that the BSP defect
occurs due to excess chlorine content in the electrolytic solution. While pitting defect occurs
due to the increase in the level of acids or alkalis in the process stages or atmosphere mainly
during the pre-treatment and rinsing. CRT maps enabled the identification of all possible
causes that brings these undesirable effects. Each of the identified cause is studied in detail
by observing the process, discussions with the shop floor operators and engineers, checking
the standards and established operating procedures to find the gaps, and testing of process
Poor quality of deionised water used at various points of anodising (pre-treatment,
rinsing and electrolyte solution) is found to be an important cause for both types of defects.
Supply of deionised water is obtained from a filtration system installed in house, for which
maintenance is outsourced. On testing the filter water quality, it was found that the ion
content of the water is at higher level than required. Further analysis revealed that hard tap
water with varying level of hardness is fed into the filter which is the root cause for poor
water quality. As the input supply is not in control of the facility, it was suggested that it
could be controlled by increasing the maintenance frequency. It is also suggested that the
input water supply should be tested every time maintenance activity is carried and the filter
settings should be adjusted accordingly.
The output quality of anodising process depends greatly on the process handling. Shop
floor operators must follow the specified standards and safety instructions. The process is
sensitive to the time lag between various steps. Transition delays were observed on the shop
floor. Improper handling was also found to be a leading cause for increasing the acid/alkali’s
content in the process. On discussing the handling issue with shop floor managers it was
found that root problem prevailing in management of operations are contractual employees
who are hired on yearly contract terms. They lack in experience, ownership and are not
skilled in their job. Due to lack of proper training programmes and procedures in the firm, it
is also difficult to train them. The issue was further discussed with the higher management
and suggestions were made to recruit some permanent employees on the shop floor and
develop comprehensive training programmes.
CRT maps also indicated that the inadequate anodising parameter setting may also
interfere with the output quality. The anodising process standards are verified and not
significant variations were found from the established standards. Rather lack of proper
training and contractual employees are accessed as the root cause for this effect also. The
operators some time delay the rinsing of surface after pre-treatment leading to pitting
defects. On the other hand, delay in removal of chassis from electrolytic solution after
electrochemical anodising process lead to BSP defects. Operators also neglect to follow the
Inefficient water
cleaning and
of filter
candles not
in tap
marks may
be present
on the
may have
Long time
and rinsing
of non-etching
water not
Tanks not
Scratches Gaseous
oxides in
can be high
Anodic film
settings not
Salt can
deposit on
walls of
Handling methods
between stages of
anodising process
not good
on the
Oil and
present on
the surface Poor
not as per
Employees not
following safety
Regular testing
of water quality
not conducted
Poor quality of
Anodic surface
Etch staining during
Environment conditions
not maintained
Electrochemical anodising
process incorrect
Figure 9.
Current reality tree for
the pitting defect
safety instructions like wearing gloves. Sweat of the operator’s hands and improper
cleaning of different tanks were also found to be source of non-conformities. Training and
motivation of employees to follow the process instructions along with recruiting few
permanent employees on the shop floor could help in improving the process handling and
eliminating both types of defects.
Facility conditions were also observed in an attempt to device the improvement strategy.
Anodising is an exothermic process and appropriate facility temperature (between 20 and
23°C) is an important determinant of output quality. Though housekeeping is able to control
the dust in the facility, variation in the atmospheric temperature was found to be a root
cause, again for both types of defects. Exposure of the anodic surface to high temperature
between transitions of surface from one stage to another is source pitting defects. It also
leads to increase in acid mist in the facility and temperature of the electrolytic solution.
Chloride level in the solution rises due to increase in temperature leading to BSP defects. The
temperature in the facility must be closely monitored and controlled. Suggestions were made to
install low-pressure oil-free regenerative blower(s) in the facility to control the temperature.
The team studied the quality of the supplies of the process; the team identified the
scope of improving the quality of the non-etching alkaline detergent. The management was
suggested to procure better quality detergent that can give better results in the
pre-treatment stage. Dispersants and chelants (Painter et al., 1994) could be added to the
detergent solution to prevent re-deposition of dissolved dust and act on rust on the raw
surface, respectively. Doing this can reduce the pitting defects in the finished products.
Based on the root cause analysis and further investigation of the root causes, measures
were established for the process improvement and discussed with the higher management.
The team discussed the feasibility, implementation cost and time requirements with the
management. The management decided that the improvement measures that are easily
implementable within the project timeline and available recourses will be made in the first
phase. It was recommended that further analysis should be conducted on the other
measures to determine the time, cost and feasibility of the potential improvements in the
second stage of the study before implementation.
Following improvements were made in the process:
(1) Regular testing of tap water quality and adjustment of filter settings according to
the incoming and required output water quality is started.
(2) Water filter maintenance frequency is increased to 10 days from the earlier 20 days.
(3) Short training sessions were organised for the shop floor operators.
(4) Process instruction guides were prepared to help operators follow the established
standard procedures. Specific instructions were displayed near to the operation area,
to keep the workers informed.
(5) One blower was installed in the facility to maintain the atmospheric temperature.
(6) Pre-treatment process is improved by adding a dispersant and chelant in the
detergent solution.
It was resolved that after making the above changes the new level of process performance will
be established, then second phase of the study will be conducted to further analyse the
remaining recommendations. For example, the study could determine the alternative varieties of
pre-treatment detergent that may be used in the process based on the impact on cost and quality.
3.5 Control
The measures suggested above were implemented on the system in order to improve the
process performance and eliminate the root causes of BSP and pitting defects. The real
challenge for sustainable process improvement lies in long-term sustainability of the
improvement efforts, continues monitoring and controlling the process activities.
Contractual employees were found to be a root cause for both types of defects. Though
training of contractual employees could bring short-term results, it is important for the firm
to devise procedure for permanent hiring of few skilled shop floor operators. They were also
suggested to develop continues training programmes for its employees. With
recommendations of the research team the management also decided to explore better
filtration system as deionised water is an important supply required continuously during
the process.
On implementing the improvement measures, the process was closely monitored for
15 days to bring to stable operations. After 15 days of operation, the process is sampled
again for 10 days with 100 per cent sampling of finished product. The data recorded on
check sheets are represented graphically in Figure 10, p-chart for fraction defective and
the Pareto analysis are shown in Figures 11 and 12, respectively. The p-chart shows the
process is in control. The short-term sigma level of the improved process is calculated to
be 3.91 as compared to 3.62 of the base-level performance. The average rate of
non-conformance including all four types of defects reduced to 0.0079 from 0.0169 in the
base-level process. The combined percentage defective due to pitting and BSP defects
is calculated to be 36.11 per cent in the improved process, which is reduced by
48.01 per cent as compared to the fraction defective calculated in the measure phase
No. of defects
Sample number
Total defectives Crazing
Black star pitting Streaking
Figure 10.
Sampling data
(control phase)
1 2 3 4 5 6 7 8 9 10
Sample number
Figure 11.
P-chart (control phase)
(before quality improvement). The process must be monitored and controlled
continuously. For keeping a check on the process quality repeated samples should be
taken over regular periods. Whenever the control chart signals processes running out of
control immediate actions are required to be taken to sustain the process performance.
4. Conclusions, limitations and future scope of work
This study discusses analysis and results of a Six Sigma project for improving the
productivity of anodising stage of an amplifier production process. The DMAIC Six Sigma
methodology is adopted to investigate the causes of non-conformities and conceive the
improvement measures. Pitting and BSP were found to be the two main types of defects in
the process. From the twofold analysis using C&E diagrams and CRT, it was identified
that the contractual employees, hard water supply in taps, temperature variations in the
facility’s atmosphere and the poor quality of non-etching alkaline detergent used in
pre-treatment step are root causes of non-conformities in this process. The feasible
improvement measures are devised and implemented to eliminate the root causes. The
study resulted in improving the sigma level of the anodising process to 3.91 compared to
base sigma level 3.62 in the short term. Hiring of permanent employees and development
of comprehensive training programmes could be important steps towards the sustainable
quality improvement efforts for this process. The firm has started considering the
remaining suggestions and are likely to be realized in future based on the second phase of
the study. The findings of the study are limited to eliminate only two of defects that were
leading to 69.46 per cent of defects. Future study could also consider the two defects still
remaining to be analysed for further improvement of the process. Future study will focus
on next stage of the production process and will also explore other statistical and
managerial quality improvement tools.
Andersen, B. and Fagerhaug, T. (2006), Root Cause Analysis: Simplified Tools and Techniques, ASQ
Quality Press, Milwaukee.
Antony, J., Gijo, E.V., Kumar, V. and Ghadge, A. (2016), “A multiple case study analysis of six sigma
practices in Indian manufacturing companies”, International Journal of Quality & Reliability
Management, Vol. 33 No. 8, pp. 1138-1149.
Antony, J., Singh Bhuller, A., Kumar, M., Mendibil, K. and Montgomery, D.C. (2012), “Application of six
sigma DMAIC methodology in a transactional environment”, International Journal of Quality &
Reliability Management, Vol. 29 No. 1, pp. 31-53.
Banuelas, R., Antony, J. and Brace, M. (2005), “An application of six sigma to reduce waste”, Quality
and Reliability Engineering International, Vol. 21 No. 6, pp. 553-570.
Pitting Streaking Black star pitting Crazing
No. of defects
Defect types
No. of defects Cummulative %
Figure 12.
Pareto diagram
(control phase)
Barney, M. (2002), “Motorola’s second generation”, Six Sigma Forum Magazine, Vol. 1 No. 3, pp. 13-16.
Desai, D.A. (2006), “Improving customer delivery commitments the six sigma way: case study of an
Indian small scale industry”, International Journal of Six Sigma and Competitive Advantage,
Vol. 2 No. 1, pp. 23-47.
Doggett, A.M. (2003), “Solving problems: a statistical comparison of three root cause analysis tools”,
Journal of Industrial Technology, Vol 20 No. 2, pp. 1-9.
Doggett, A.M. (2005), “Root cause analysis: a framework for tool selection”, The Quality Management
Journal, Vol. 12 No. 4, pp. 34-45.
Evans, J.R. and Lindsay, W.M. (2014), An Introduction to Six Sigma and Process Improvement, Cengage
Learning, Stamford.
Gijo, E.V., Scaria, J. and Antony, J. (2011), “Application of six sigma methodology to reduce defects
of a grinding process”, Quality and Reliability Engineering International, Vol. 27 No. 8,
pp. 1221-1234.
Gupta, A., Sharma, P., Malik, S.C., Agarwal, N. and Jha, P.C. (2016), “Productivity improvement
in the chassis preparation stage of the amplifier production process: a DMAIC Six Sigma
methodology”, International Journal of Reliability, Quality and Safety Engineering, Vol. 23
No. 6, p. 1640012.
Hagemeyer, C., Gershenson, J.K. and Johnson, D.M. (2006), “Classification and application of
problem solving quality tools: a manufacturing case study”, The TQM Magazine, Vol. 18 No. 5,
pp. 455-483.
Hamza, S.E.A. (2008), “Design process improvement through the DMAIC six sigma approach: a case
study from the Middle East”, International Journal of Six Sigma and Competitive Advantage,
Vol. 4 No. 1, pp. 35-47.
Javalgi, R.R.G. and Todd, P.R. (2011), “Entrepreneurial orientation, management commitment, and
human capital: the internationalization of SMEs in India”, Journal of Business Research, Vol. 64
No. 9, pp. 1004-1010.
Katyal, M.A. and Xaviour, M.B. (2015), “A study on MSMEs’-role in propelling economic development
of India & a discussion on current HR issues in MSMEs’ in India”, International Journal of
Scientific and Research Publications, Vol. 5 No. 2, pp. 77-89.
Kumar, M., Antony, J. and Tiwari, M.K. (2011), “Six Sigma implementation framework for SMEs – a
roadmap to manage and sustain the change”, International Journal of Production Research,
Vol. 49 No. 18, pp. 5449-5467.
Kumar, M., Antony, J., Antony, F.J. and Madu, C.N. (2007), “Winning customer loyalty in an automotive
company through six sigma: a case study”, Quality and Reliability Engineering International,
Vol. 23 No. 7, pp. 849-866.
Kumar, S. and Sosnoski, M. (2009), “Using DMAIC six sigma to systematically improve shopfloor
production quality and costs”, International Journal of Productivity and Performance
Management, Vol. 58 No. 3, pp. 254-273.
Lee, K.L., Wei, C.C. and Lee, H.H. (2009), “Reducing exposed copper on annular rings in a PCB factory
through implementation of a six sigma project”, Total Quality Management, Vol. 20 No. 8,
pp. 863-876.
Montgomery, D.C. (2007), Introduction to Statistical Quality Control, John Wiley & Sons, Jefferson City.
Painter, J.D., Marshall, J.L. and Laurent, J.C.S. and The Procter & Gamble Company (1994),
“Nonphosphated automatic dishwashing compositions with oxygen bleach systems and process
for their preparation”, US patent 5, 292, 446, March 8.
Qamar, S.Z., Arif, A.F.M. and Sheikh, A.K. (2004), “Analysis of product defects in a typical aluminum
extrusion facility”, Materials and Manufacturing Processes, Vol. 19 No. 3, pp. 391-405.
Shiralashetti, A.S. (2012), “Prospects and problems of MSMEs in India – a study”, International Journal
of in Multidisciplinary and Academic Research, Vol. 1 No. 2, pp. 1-7.
Swarnakar, V. and Vinodh, S. (2016), “Deploying Lean Six Sigma framework in an automotive
component manufacturing organization”, International Journal of Lean Six Sigma, Vol. 7 No. 3,
pp. 267-293.
Thakore, R., Dave, R., Parsana, T. and Solanki, A. (2014), “A review: six sigma implementation practice
in manufacturing industries”, International Journal of Engineering Research and Applications,
Vol. 4 No. 11, pp. 63-69.
Yeung, S.M.C. (2009), “Using six sigma – SIPOC for customer satisfaction”, International Journal of Six
Sigma and Competitive Advantage, Vol. 5 No. 4, pp. 312-324.
Zhang, M., Wang, W., Goh, T.N. and He, Z. (2015), “Comprehensive six sigma application: a case study”,
Production Planning & Control, Vol. 26 No. 3, pp. 219-234.
Zhu, H., Couper, M.J. and Dahle, A.K. (2011), “Etching effects and the formation of streaking defects on
Al extrusions”, Aluminium International Today, Vol. 23 No. 3, p. 31.
Corresponding author
Anshu Gupta can be contacted at:
For instructions on how to order reprints of this article, please visit our website:
Or contact us for further details:
Reproduced with permission of copyright owner. Further
reproduction prohibited without permission.


15% off for this assignment.

Our Prices Start at $11.99. As Our First Client, Use Coupon Code GET15 to claim 15% Discount This Month!!

Why US?

100% Confidentiality

Information about customers is confidential and never disclosed to third parties.

Timely Delivery

No missed deadlines – 97% of assignments are completed in time.

Original Writing

We complete all papers from scratch. You can get a plagiarism report.

Money Back

If you are convinced that our writer has not followed your requirements, feel free to ask for a refund.