Assignment 40
Delp10
1.ADMAICSixSigmaApproach.pdf
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
Abstract 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 0265-671X 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: www.emeraldinsight.com/0265-671X.htm
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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-analyse- improve-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.
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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
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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
Supplier Chassis stage for prepared chassis
Prepared chassis ready for anodising
Non-etching, alkaline detergent
Aluminium
Deionised water
Equipments used for anodising
Pre-treatment
Anodising Anodised chassis
Chassis to be delivered to the powder coating
stage of production
Sealing
Supplier A
Supplier B
Supplier C
Supplier D
Input Process Output Customer
Figure 2. SIPOC diagram of anodising process
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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
0
1(4 38)
3(4 42)
5(4 56)
7(4 51)
9(4 54)
11( 431
) 13(
468 ) 15(
486 ) 17(
412 ) 19(
454 )
5
10
15
20
25
30
35
N o.
o f d
ef ec
ts
Sample number
Total defectives Crazing Black star pitting Streaking Pitting
Figure 3. Sampling data (measure phase)
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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.
0 0.005 0.01
0.015 0.02
0.025 0.03
0.035 0.04
1 3 5 7 9 11 13 15 17 19
P
Sample number
CL pi UCLi LCLi
Figure 4. P-chart
(measure phase)
0 10 20 30 40 50 60 70 80 90 100
0
10
20
30
40
50
60
70
Pitting Streaking Black star pitting Crazing
N o.
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Defect type
No. of defects Cummulative %
Figure 5. Pareto diagram (measure phase)
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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.
Environment
Humidity
Temperature of the facility
Air Contaminants
Electrolyte Tank
Filter System
Setting of Anodising Parameters
Lack of Training
Chloride level high in Deionised water
Chloride level high in Electrolyte solution
Improper Handling
Machine Measurement Manpower
Material
BLACK STAR PITTING
Figure 6. Cause and effect diagram for black star pitting defect
Environment
Machine
Measurement
Method
PITTING
Manpower
Material
Chassis Surface Cleaning Detergent
Deionised water ion component
Rinsing
Etch Staining
Filter Handling
Training
Contaminants Facility of Temperature
Acidic/Alkaline mist Anodising Parameters
Pre-treatment tank
Figure 7. Cause and effect diagram for pitting defect
Black star pitting
Electrochemical anodising
process incorrect
Water filter not functioning as
per specification
Improper handling
Facilities environment conditions not
maintained
Dust particles may be present
Acid mist may be
present in the facility
Tanks not properly cleaned
Variations in the temperature
level Contract labour
Lack of training
Employees not follow
safety instruction
There may be delay in removing chassis from the
electrolyte solution
Poor maintenance
Regular cleaning and replacement of filter candles
not done
High chloride level in tap
water
Filter settings may not correct
Electrolyte solution Concentration can
be high
Salt can deposit on
walls of tanks
Electrical parameter
settings may be incorrect
Temperature settings not
correct
Regular testing of water quality not conducted
Poor housekeeping
High level of chloride
Figure 8. Current reality tree for defect black star pitting
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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 parameters.
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
Pitting
Inefficient water filter
Poor maintenance
Filter settings incorrect
Regular cleaning and replacement
of filter candles not
done
Hard water supply in tap
Embedded debris/
marks may be present
on the surface
Base material
may have scratches
Long time gap
between pre-
treatment and rinsing
Temperature of non-etching
detergent solution/rinsing
water not correct
Tanks not properly cleaned
Scratches Gaseous oxides in
air
High acidic mist
Electrolyte solution
Concentration can be high
Anodic film specification
Temperature settings not
correct
Salt can deposit on
walls of tank
Dust particles
Poor housekeeping
Handling methods between stages of anodising process
not good
Sweat on the surface
Oil and dust
present on the surface
Poor detergent
quality
Non-etching detergent solution
concentration not as per
specification
Employees not following safety
instructions
Contract labour
Regular testing of water quality not conducted
Poor quality of Anodic surface
Etch staining during pre-treatment
Improper handling
Environment conditions not maintained
Electrochemical anodising process incorrect
Figure 9. Current reality tree for
the pitting defect
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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
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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
0 2 4 6 8
10 12 14
N o.
o f d
ef ec
ts
Sample number
Total defectives Crazing Black star pitting Streaking Pitting
1(4 67)
2(4 40)
3(4 34)
4(4 40)
5(4 46)
6(4 35)
7(4 80)
8(4 23)
9(4 97)
10( 476
) Figure 10. Sampling data (control phase)
0
0.005
0.01
0.015
0.02
0.025
1 2 3 4 5 6 7 8 9 10
P
Sample number
CL pi UCLi LCLi
Figure 11. P-chart (control phase)
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(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.
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0
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120
0 2 4 6 8
10 12 14 16 18
Pitting Streaking Black star pitting Crazing
N o.
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ts
Defect types
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Figure 12. Pareto diagram (control phase)
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Corresponding author Anshu Gupta can be contacted at: [email protected]
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