Quality control: Applying six sigma in tania water factory

Chapter 1

Introduction and Background

1.1           Introduction

Lean Six Sigma has been the most common business strategy for delivering continuous improvement in the manufacturing and public sectors in recent years. Any firm in the world should strive for continuous development in order to attain quality and operational excellence while also improving performance.

  1. Quality Control Definition 

 Quality control is a system that ensures that a desired level of quality is maintained by providing feedback on product/service characteristics and taking corrective action when those characteristics stray from a set of criteria. Acceptance sampling programs, statistical process control, and off-line quality control are three subcategories of this wide area.

1.3   Six Sigma the Problem Is That Spaces in The Factory

In the late 1980s, Motorola was the first to design Six Sigma. The concept was created by Bill Smith, a quality engineer, with the goal of decreasing errors through enhancing the performance of quality and measurement systems. Mistake rates were tolerated by Motorola systems, resulting in excessive scrap, rework, redundant testing, and, in many cases, customer dissatisfaction.

Six Sigma was created with the goal of detecting and eliminating any process variation. When variation is removed, process results may be anticipated reliably – every time. Designing the system so that these accurately predicted results fit within the acceptable performance zone from the customer’s perspective eliminates process errors.

Motorola engineers, on the other hand, went a step farther. They were well aware that many process improvements fail because the root cause of the problem is not addressed. Furthermore, the changes they made would be temporary, as the operators would eventually revert to their former behaviors. Six Sigma was divided into five phases to address these issues.

Figure 1.3 DAMIC

What Are the Five Phases of Six Sigma?

  1. Define
    The constraints of the examined process, as well as the customer’s expectations or expected performance for that process, are set in this step. This is to ensure that a change does not detract from the customer experience, but rather enhances it.
  2. Measure
    In this phase, the existing performance of the process, product, or service is measured to determine what is actually happening, especially from the customer’s perspective. This ensures that the analysis and solution are based on real-world evidence rather than speculative or anecdotal information.
  3. Analyze.

This phase evaluates the process, product, or service using the measured data to find the source or sources of the variation that is creating the problem. This is to ensure that the genuine root cause(s) is discovered rather than just a symptom.

  1. Improve
    Before creating and testing a solution set of changes, this phase evaluates prospective process, product, or service alterations. This ensures that the solution delivers the desired result with the least amount of variation possible.
  2. Control
    To ensure that the solution is fully implemented and performance does not deteriorate, changes are implemented, supporting systems are updated, and the process, product, or service is put under control – usually statistical process control.Six Sigma can be used to any process, product, or service that has a stated performance target and quantifiable attributes because it is mostly data-driven.

Similarities and distinctions

Despite their differences, Lean and Six Sigma work well together. Because of their commonalities, they get along swimmingly. The distinctions ensure that the analytical tools and solution options needed to improve the process, product, or service are available. Because of their commonalities, both types of analysis can be applied to the same process, product, or service at the same time.

  1.  Lean six sigma

Lean Six Sigma is a methodology for solving problems, reducing waste and inefficiency, and improving working conditions in order to better respond to client requests.

It is a popular and powerful methodology for improving your company’s operations that blends Lean and Six Sigma tools, methodologies, and principles.

For enterprises all over the world, Lean Six Sigma’s team-oriented approach has proved success in maximizing efficiency and substantially increasing profitability.

“What exactly is Lean Six Sigma?” you might wonder. Continue reading to learn more about what it is, why it matters, and how to do it correctly.

Figure 1.4 Lean six sigma

There are three key elements to Lean Six Sigma.

Tools and techniques: A set of tools and analytical procedures for locating and addressing issues.

Process and methodology : A sequence of processes that organizes the utilization of problem-solving instruments to ensure that the genuine underlying causes are found and a solution is applied completely.

Mindset and culture: Data and processes are used to fulfill operational performance goals and This way of thinking will improve over time.

These three elements complement one another. They will not be used effectively unless there is a process for applying analytical techniques and an attitude of constant improvement. An improvement process will not provide the desired results without the tools and procedures that define the activity of the process phases, as well as a culture that insists on a systemic data-based approach to issue solutions.

Finally, a culture that strives to improve continuously would be frustrated if there are no tools and techniques for analysis and no process or methodology to organize and focus improvement activities.

Fortunately, all three layers are covered by the Lean Six Sigma method to business improvement.

Five principles of lean manufacturing

  1. Value 

The consumer, not the individuals making or delivering the product or service, determines the importance of what the customer finds vital in a product or service.

  1. Value Stream
    The set of business activities and steps involved in generating and delivering products and services to clients; rather than looking at each step independently, it focuses on the connections between them.
  2. Flow
    The degree to which operations that provide value to the consumer move smoothly and uninterruptedly along the value stream, rather than waste and inefficiency hindering it.
  3. Pull
    The extent to which the value stream exclusively processes things and services for which there is a client demand, rather than developing something in the hopes that it will be bought.
  4. Perfection
    Continuous evaluation of value stream performance is used to identify and improve the value created and supplied to customers, rather than rejecting changes that improve the process of creating and delivering customer value.

Similarities of Lean and Six Sigma

• Both rely on a value definition that is based on the customer experience. The customer is supreme (or queen).

• Both employ a process flow mapping approach to understand the process. Even if the analysis is focused on a product or service, the development and delivery of that product or service involves a process.

• In both circumstances, data is used to determine current performance as well as the implications of future performance. The data collected during a Lean Six Sigma project is often utilized to assist both Lean and Six Sigma analysis. The usage of data assists in the discovery of the true underlying cause.

• Both are implemented by a small cross-functional team through improvement projects. The length of the project and the size of the team will be determined by the scope and scale of the process, product, or service that has to be improved.

• Both have gone beyond the factory floor and are now used for all internal and external tasks and operations. Manufacturing, consumer goods, government, education, and non-profit organizations are among the industries that employ them.

Both waste and variance are often reduced as a result of improvements based on either strategy. Getting rid of inefficient processes and activities (muda) eliminates sources of variance, which eliminates lost process capacity and procedures involved with accommodating it (mura and muri).

There are, however, some differences between the two systems. These distinctions do not create a conflict; instead, they enable various routes to the same end. Which sets of tools are most applicable in a Lean Six Sigma project should be determined by the nature of the defect, as defined by customer value, and the existing status of the process, product, or service. Often, the final answer is a combination of Lean and Six Sigma improvements.

What is the difference between Lean and Six Sigma?

• Different approaches to problem solving: Lean emphasizes waste (Muda, Mura, Muri), whereas Six Sigma emphasizes variation, or any divergence from the target performance.

• Various methodologies — For both analysis and solution creation, Lean predominantly uses visual techniques, which are backed up by data analysis. Six Sigma largely uses statistical approaches, which are reinforced by data visualization, for analysis and solution design. This gives birth to the notion that Lean is easier than Six Sigma since Lean’s visual analysis is straightforward to grasp, but many people are intimidated by Six Sigma’s numerical analysis. Today’s statistical assistance tools make both sorts of analysis simple.

• Various types of solution documentation – a new value stream map is used to record the Lean solution, which leads to changes in workflows and frequent modifications in work instructions at many of the process steps. The Six Sigma solution includes changes to setup procedures as well as a control strategy for monitoring and responding to variation. It will also affect job instructions, and it will almost always result in adjustments to measurement methods or systems.

Because the two systems are so similar in so many ways, combining them into one paradigm and reaping the benefits of their synergy was simple. Lean Six Sigma, in its current form, eliminates the majority of the drawbacks of prior failed efforts.

1.5 Tania water factory

Tania Bottled Water Company was established in 2003 by ambitious Saudi youth and continued to grow until it reached today the ranks of the largest companies in the Middle East in the field of water. Tania has several branches in the Kingdom to ensure that its products are presented to its customers easily and smoothly. In Tania, quality is not only a way of working or a level to follow, but it is a belief within Tania and the basis of every process it performs, as it has obtained the most important international and local quality certificates. Tania’s culture is distinctive and its vision aspires to a healthy reality, whether in its operations or the environment around it.

Figure 1.5 Tania water factory

1.5.1     Tania Factory Products

200 ml, which is one of the smallest sizes of bottles that Tania company provides to the customer.

Figure 1.5.1 Tania water products

330 ml, which is one of the medium sizes that the factory manufactures and produces

Figure 1.5.1 Tania water products

600 ml, which is one of the largest sizes produced by the factory

Figure 1.5.1 Tania water products

Chapter 2

Literature Survey

2.1 literature Survey

In a water-bottling company, six sigma methodology was used to improve efficiency and reduce scrap.

Department of Mechanical Engineering, Postgraduate program (MSc) in Advanced Industrial & Manufacturing Systems Technological and Educational Institute of Piraeus, P. Ralli & Thivon Avenue 250, Aigaleo 12224, Athens, Greece & Kingston University, London, England E.S. Telis 1, C. Tsonis 2, G. Besseris 3 and C. Stergiou 4

2.2 Define Phase

The project’s start date had been set for the beginning of 2009. Prior to the project’s start date, the team received two weeks of intensive training in Six Sigma methodology and DMAIC problem-solving techniques. The project manager had received green belt training from outsourcing black belts. The project leader had also trained the project team members. Members of the team come from various departments within the company, including factory, including engineering, quality assurance, production, and finance. The first step involves selecting a project for improvement and defining a project charter that includes the six basic elements of purpose, importance, scope, deliverables, measures, and resources. “Increase line efficiency of the 0.5L-bottle line from 78 to 84 percent and reduce bottle and preform losses from 0.5 percent to 0.2 percent,” the project stated. Customer specifications specified which project flaws were acceptable. Efficiency gains of more than 84 percent would be considered a success in this project, while total losses would be reduced by less than 0.2 percent. The project charter for the define phase is shown in Figure 1 below.

Figures 2.2 Define Phase

2.3 Measure Phase

To collect the project’s important baseline data, a data collection strategy was prepared, which included information about the data that needed to be measured, the data type, how the data was measured, and how the data was recorded. The next step was to plot the available data across time in order to better understand process variance and uncover trends that would necessitate additional data analysis. As a starting point, the information acquired for the project efficiency, bottles, and preforms The scrap material was from the previous production year, 2008, and it covered the full year. Figure 2 shows the time ordered data for line efficiency for the 0.5L bottle product. Almost all of the data points are close to the mean, with the majority of fluctuating runs behaving normally and only a few runs exceeding the process control limitations. This pattern shows that special cause variation is likely to be small and easy to spot, and that once discovered and eliminated, the process should become statistically stable. The unique cause points were fixed when they occurred in production mode, according to additional study.

Figures 2.3 Process measure phase

The Pareto chart of efficiency losses depicts the contribution of each line machine based on available time losses per machine. According to Pareto analysis, shrink wrappers have the biggest losses in terms of time availability, followed by blower and labeler machines. Further inquiry was decided in order to figure out why the three machines are the principal cause of the line’s efficiency loss. A fresh Pareto analysis (Figure 4) is developed for each of the three machines in order to determine the major reasons for the machines’ increased proportion of efficiency losses. Labelers and shrink wrappers are plagued by micro-stoppages and mechanical issues, while the blower machine is to blame.

Figures 2.3 Pareto Analysis for line time availability losses

Figures 2.3 Pareto chart for shrink wrapper machine availability

2.4 Analyze Phase

 The project team was able to recognize and clarify which potential causes for micro stoppages and mechanical problems as they were mentioned in the previous phase by using cause-and-effect diagrams in DMAIC. Micro stoppages in the labeler machine were caused by: • Bottle defects caused by the blower machine • Label defects • Glue temperature • Feeding line high pressure Labeler machine mechanical issues are linked to the labeling function. Micro stoppages in shrink wrapper machines were caused by: • The bottle-feeding lines’ pressure • Bottles from the labeler that are incorrectly labeled Mechanical issues with shrink wrapper machines can occur as a result of• The bottle forwarding mechanism is operational. Blockage of bottles in the air conveyor caused micro stoppages in the blower machine, affecting the number of preforms and bottle losses. Lack of proper machinery maintenance resulted in mechanical issues. Bottle losses in the filler machine were caused by: • Bottle defects from the blower machine • Changeover adjustments for machines The DMAIC team needed to create a plan for cause verification in order to finish the Analyze phase of the project. The potential causes that would be evaluated, their impact, and the type of data that would be required to verify the effect were all included in this plan. A cause verification plan was created for each of the line’s four machines. The potential causes for blower machine would be evaluated as follows: • Scheduled replacement of air conveyor trucks • Weekly maintenance plan The data required to verify the effect was the same as that collected during the Measure phase of the project, and it concerned the frequency of blower stoppages as well as the number of bottle and preform losses in the machine. The potential causes that would be evaluated in the labeler machine were: • Machine maintenance plan • Bottle pressure in the machine’s infeed • Glue temperature • Cleaning by machine • High-quality labels The following were the main effects of the potential causes in the labeler machine:• Additional blockages in the machine’s inlet • Additional unlabeled bottle losses The data collected for the verification process was the same as for the Measure phase of the project and related to the labeler machine’s stoppage frequency. Potential reasons for evaluation of shrink wrapper machines included: • Shrink wrapper maintenance schedule • Bottle separator function adjustment • Bottle pressure in the infeed conveyor belts • Maintenance of the machine The following were the main effects of the causes: • Additional blockages in the machine’s inlet • Additional stoppages during the shift The stoppage frequency in the shrink wrapper machine was also data that needed to be collected for the verification. The following were possible verification causes in the filler machine: • The quality of the bottles produced by the blower machine • Machine changeover adjustments • Conveyor screw function The following were the main effects of the potential causes: • Additional blockages in the machine’s inlet • Additional stoppages during the shift Data was gathered to assess the frequency of filler machine stoppages and the associated bottle scrap. The team decided to divide mechanical problems that stood out in the Measure phase into separate subcategories in order to verify the potential causes. Attending to micro-stoppages was handled in the same way. The Ichart of total bottle-and-preforms production line losses and the I-chart for the corresponding efficiency are shown in figures 5 and 6. Figures 7 and 8 show Pareto charts that show the main mechanical issues and micro-stoppages as they occurred. A lot of useful information about the process can be gathered using the previous I-charts and Pareto charts created in the Analyze phase of the DMAIC project.

Figuer 2.4 I-chart of total losses of production line

Figuer 2.4 Efficiency by the end of Analyze phase

The line’s total bottle and preform losses were around 0.47 percent, with the filler, blower, and labeler being the main contributors to the losses. Blower and filler machines suffer the most losses, while labeler machine losses appear to be well managed.

Figure 2.4 Main mechanical stoppage in labeler machine

Figure 2.4 Micro stoppage rf labeler machine

This fact motivates the DMAIC team to focus on long-term solutions for blower and filler losses during the Improve phase of the project. Another important finding is that the mechanical and micro-stoppage issues discovered during the Measure phase of the project can now be addressed using the potential causes identified during the Analyze phase. This could imply that those micro stoppages are a result of the main mechanical issues, but their role in causing stoppages may be minor

2.5 Improve Phase

The team noted that efficiency tended to increase once the enhancement work was completed, eventually reaching 82.34 percent. This efficiency boost was validated as a success, indicating that the team’s solutions were on track (Figure 9). The box plot diagram in figure 10 shows the difference between the old and new efficiency numbers, as well as a 95 percent confidence range. Losses were considerably reduced at the targeted levels when the solutions were implemented. Figure 12 illustrates a box plot of total losses with a 95% confidence interval that indicates the difference in loss performance between old and new values. Finally, we estimated the new process sigma level to determine the DMAIC project’s success in terms of loss and efficiency. We compared it to the sigma level obtained at the start of the project during the Measure phase.

Figure 2.5 I chart of efficiency after improvements

Figure 2.5 Box plot of efficiency

We plotted the sigma estimation for efficiency after the last enhancements in the Improve phase. Eleven of the 45 process orders were judged faulty due to inefficiency of less than 78 percent. This yields a 75.56 percent yield, which translates to a sigma level of 2.19, which is up from 1.64 previously.

Figure 2.5 New sigma level for efficiency

In terms of losses, figure 14 shows that ten of the 45 completed process orders were defective because they performed better than the assigned target of 0.2 percent. This results in a yield of 77.78 percent, corresponding to a sigma level of 2.26 when compared to a baseline of 1.6.

Figure 2.5 New sigma level for total losses of the production line

2.6 Control Phase

The DMAIC approach concludes with the control phase. The goal of the control phase is to make sure that the problem-solving actions have stabilized production concerns and that the new methods can be improved even more. In addition, during the control phase, useful lessons can be learned and future project plans can be defined. In DMAIC methodology, the control phase has two main goals: • Continuing to improve the process • Sharing what you’ve learned The steps that were followed to complete this project were as follows: • Ensure that everyone follows the new process in accordance with the verified solutions (by developing and documenting standard practices). • Improvements were sustained in a process that had not yet been reached (daily schedule check meeting).• Show the sponsor that everyone involved in the process is using the new methods to minimize or eliminate the issues discovered during the project’s measurement phase. Figures 9 and 11 clearly show an increase in confidence that improvements have been made in the right direction. 4. Final thoughts To conclude this research, it is important to note that the six sigma methodology, with its data-driven approach and structured approach to problem solving, produces positive results not only in terms of improving a process and reaping economic benefits, but also in terms of instilling high confidence and recognition in the project’s participants. The following are the main takeaways from this case study• DMAIC team members were heavily involved • In-depth analysis and digging for the true root causes • Floor staff proposed interesting solutions • All line employees should be trained in DMAIC methodology and lean practices • More time should be dedicated to DMAIC projects • A Black Belt should be trained in one person from management. The champion black belt and master black belt, for example, are motivated to actually enforce and align the company’s attitudes toward recognizing and solving the true fundamental problems because of the way process data is used to show real problems and tested solutions with positive results.

Furthermore, teaching employees to function as a cohesive team is a valuable management method for dealing with operational inefficiencies. People must be convinced that a new technique has been applied, and the most effective way to persuade them is for the new method to create actual and measurable outcomes in their everyday job. This is something that six-sigma methodology may supply when properly implemented, and it may be one of the key reasons for the approach’s success and recognition, aside from the tools it employs.

Chapter 3

Problem Definition and Project Objective

3.1 Problem Definition

The problems are the low performance of water treatment plants, and it happens that the quality and purity of the water produced are less than the acceptable limit.

3.2 Project Objective

Our objective in this project is to understand the process of water production and analyze the data. By analyzing the data/process of the water production in tania water factory our main goal is to search for any errors and defects in the process and see how we can apply the methodology of six sigma.

3.3 Project Scope

The Scope of this work is to implement the six sigma tools in the improvement of water purity by moving step by step, to decrease the defect and errors in the factory, by the end of this project we expect to increase the quality of the purity of water production to eliminate all errors and defects. The same methodology can be applied in other water factories.  

3.4 Expertise of the team members

Every member of the team has the degree of knowledge required to complete this project successfully. The students have taken the appropriate course (IEG 303 Quality Control), and the project topic is of interest to everyone in the team.

Chapter 4

Project Methodology

4.1 Define

One of the biggest problems facing factories is the low performance of water treatment plants and because of this, the quality and purity of the produced water is less than the acceptable limit. To maintain product quality, attention should be paid to treatment devices and their development.  

Figures 4.1 Define

4.2 Measure

To measure the level of service, send a message to assess the level of service and put a questionnaire to the customer through an after-sales questionnaire in order to measure the level of quality provided to customers.

Figures 4.2 Form

4.3 Analyze

• Extraction of raw groundwater from the aquifer.

• Drawing samples of raw groundwater from the ground well to conduct chemical   and microbiological tests.

• Preliminary sterilization of groundwater using primary chemicals “sulfuric acid + chlorine”.

• Storing raw groundwater in underground reservoirs.

• Taking samples from groundwater in the ground reservoir to conduct chemical and microbiological tests.

• Distribution of groundwater to desalination units for groundwater treatment.

• Purification and filtration of groundwater using sand filters and 5-micron linear filters.

•  Prepare groundwater with anti-sedimentation and metabisulfite.

• Adding final chemicals to drinking water, including “sodium fluoride, sodium hydroxide, chlorine.”

• Taking samples of drinking water produced from desalination units to conduct chemical and microbiological tests.

• Collecting drinking water produced from desalination units in the producing water tank.

• Taking samples of the drinking water produced from the ground tank of the produced water to conduct chemical and microbiological tests.

• Extracting the produced drinking water from the produced water tank and passing it to the final treatment stage through purification and filtration with active carbon filters.

• Taking samples of drinking water purified and filtered with carbon to conduct chemical and microbiological tests.

• Purification of carbon-filtered production water on the final linear filters with a purification diameter of less than 1 micron.

• Taking samples from purified and filtered drinking water with final thread filters to conduct chemical and microbiological tests.

• Sterilization of pure treated drinking water by UV sterilization devices.

• Taking samples from purified and sterilized drinking water with UV rays to conduct chemical and microbiological tests.

• Final sterilization of sterilized pure drinking water by mixing with O3 ozone gas in 316-degree stainless steel mixing tanks.

• Taking samples of drinking water purified and sterilized permanently with ozone O3 to conduct chemical and microbiological tests.

• Starting to distribute the treated water finally to the lines and filling machines for drinking water products:

• Lines and machines for filling the product of Tania bottles, a volume of 5 gallons = 19 liters, taking samples from the machine and from the final product for testing.

• Lines and machines for filling the products of Tania bottles and PET zippers, sizes 200, 330, 600, 1500 ml, taking samples from the machines and from the final products for laboratory tests.

• Lines and machines for filling Tania cups and PET zippers, sizes 150 and 250 ml, taking samples from the machines and from the final products for laboratory tests.

• Lines and machines for filling Tania and PET bottles products with volumes of 1.89, 3.8, and 5 liters, taking samples from the machines and from the final products for laboratory tests.

• Recording the timing and dates of production and the numbering of packing machines for each of the final products in vials or cups and cartons.

• Packing cardboard products and keeping them in final production warehouses.

• Withdrawing samples of the final products from the final production warehouse of various types to conduct chemical and microbiological laboratory tests.

• Loading the final products on the transport trailers for the main and subsidiary warehouses for sales.

• Withdrawing samples of the final products from the main and subsidiary warehouses of various types to conduct chemical and biological laboratory tests.

• Withdrawing samples of the final products from grocers and distributors of various types to conduct chemical and microbiological laboratory tests.

4.4 Improve


This phase evaluates potential modifications to the process, product, or service, and then designs and tests a solution set of improvements. This ensures that the solution has the desired effect and that variation is minimized or avoided.

Figure 4.4 Tania Factory  

4.5 Control


The changes are made, the supporting systems are updated, and the process, product, or service is put under control – usually statistical process control – to verify the solution is completely implemented and to detect if performance begins to deteriorate.

4.6 Project Plan

1413121110987654321                     Week Task            
              Selecting groups and choosing instructor
              topic/ brainstorming about lean 6 sigma
              Contacting factories and Collecting data and discuss the factory issues
              choosing the method that will be applied/ Preparing the project proposal    

4.7 Reference

  1. George ML. Lean Six Sigma: Combining Six Sigma Quality with Lean Speed. New York: McGraw-Hill; 2002
  • Grant, Eugene L., and Richard S. Leavenworth, Statistical Quality Control, Seventh Edition, New York: McGraw-Hill Publishing Co., 1996.
  • George, Michael L., John Mary, David T. Rowlands, and Mark Price, The Lean Six Sigma Pocket Toolbook: A Quick Reference Guide to 100 Tools for Improving Quality and Speed, New York: McGraw-Hill, 2004. ISBN 0071441190.
  • Pyzdek, Thomas, The Six Sigma Handbook 3rd Edition, New York: McGraw Hill, 2014.
  • A Quick Reference Guide to 100 Tools for Improving Quality and Speed, New York: McGraw-Hill, 2004. ISBN 0071441190.
  • V. Gupta, R. Jain, M. L. Meena, G. S. Dangayach, “Six-sigma application in tiremanufacturing company: a case study,” J Ind Eng Int, vol. 14, pp. 511–520, 2018.
  • J. Antony, “Design of Experiments and its Role within Six Sigma,” 2nd ed., pp.201–208, Design of Experiments for Engineers and Scientists, 2014.
  • G. V. S. S. Sharma, P Rao Srinivasa, B Babu Surendra, “Process capability improvement through DMAIC for aluminium alloy wheel machining,” J Ind Eng Int, vol. 14, pp 213–226, 2018.
  1. Eckes, G. (2000), “The six sigma revolution”, John Wiley and Sons, New York,NY
  1. Linderman, K., Schroeder, R.G., Zaheer, S. and Choo, A.S. (2003), “Six sigma: A goal-theoretic perspective”, Journal of Operations Management, Vol. 21, pp. 193-203
  1. McClusky, R. (2000), “The rise, fall and revival of Six Sigma quality”, Quality Focus, Vol. 4, No. 2, pg. 6 
  1. Pande, P. and Holpp, L. (2002), “What is lean six sigma?”, McCraw Hill, New York
  1.  Raisinghani, M.S., Ette, H., Pierce, R., Cannon, G. and Daripaly, P. (2005), “Six Sigma: concepts, tools, and applications”, Industrial Management and Data Systems, Vol.105, No. 3/4, pg. 491
  1.  Sandholm, L. and Sorqvist, L. (2002) “12 requirements for Six Sigma success”, ASQ Six Sigma Forum Magazine;Vol.2, No.1, pg. 17
  1.  Snee, R.D. and Hoerl, R.W. (2003), “Leading six sigma-A step by step guide based on experience with GE and other six sigma companies”, Prentice Hall, pp.279.
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