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The purpose of this paper is to further develop a method to convert (dis)satisfaction on critical attributes (critical to qualities, CTQs) in the customer satisfaction survey into performance measures that are equivalent to defect per million opportunities (DPMO).

Stepwise multiple regression analysis is applied to identify the CTQs, where the overall satisfaction is the dependent variable and the attribute‐related (dis)satisfactions (i.e. performance score minus importance score) are the independent variables. To simulate the attribute‐related (dis)satisfaction for the identified CTQs, random numbers that follow normal standard distribution are generated and returned into random numbers that have similar characteristics (properties) with the primary data. The proportion of returned random numbers below the lower six sigma limit (SL) and above the upper six SL is adjusted into dissatisfaction per million opportunities (DisPMO) and delight per million opportunities (DePMO), respectively.

Applying the logic of DPMO outside the boundary of an organisation (i.e. in the market) leads to two distinct measures, DisPMO and DePMO. These two measures can be transformed into two types/variants of sigma levels, i.e. left‐side (approximated from DisPMO) and right‐side (from DePMO), which may describe organisational effectiveness in the market from two different but complementary approaches.

DisPMO and DePMO provide a basis for assessing the effectiveness of an organisation (manufacturing/non‐manufacturing) according to six sigma methodology, where the “importance” and “performance” dimensions are considered simultaneously. Hence, the applications of DPMO (as six sigma's main performance measure) within and outside the boundary of an organisation are consistent and comparable.

The success of Six Sigma in manufacturing in the past decade has encouraged moves to explore Six Sigma applications to other domains, such as the software industry, for performance improvement. Owing to the uniqueness of software processes, there have been disagreements as to whether Six Sigma should be adopted in software design processes. In this paper, we discuss the applicability of the Six Sigma framework to software. Some myths and facts about the Six Sigma Software Program (6SSP) are discussed. We also address some common misconceptions on the potential of Six Sigma in software, as well as some actual practical challenges. A framework is suggested for practitioners and managers interested in exploiting the benefits of statistical analysis in general, and 6SSP in particular. Some ideas are also raised on what remains to be done to make 6SSP work.

The Six Sigma phenomenon has followed the TQM movement as the latest thrust for many companies seeking to improve their performance and effectiveness. The purpose of this paper is to review the basic concepts of Six Sigma, its benefits, and successful approaches for implementation. In particular, we benchmark the practices of the General Electric Company, one of the leaders and innovators in implementing the process. We conclude that keys for successful implementation include upper management support and involvement, organizational infrastructure, training, tools, and links to human resources‐based actions.

This study aims to utilize DMAIC methodology along with value stream mapping and other Lean Six Sigma tools in a major automobile light manufacturing industry to reduce defect rates and increase production capacity in their manufacturing line. The study also proposes a modified framework based on lean principles and FlexSim to identify and reduce waste in the selected industry.

A Lean Six Sigma modified framework has been deployed with DMAIC to reduce the defect rate and increase the production rate. Various tools like value stream mapping, brainstorming, Pareto charts, 5S, kanban, etc. have been used at different phases of DMAIC targeting wastes and inventory in the production line. Also, a simulation model has been utilized for the automobile light manufacturing industry to improve the machine utilization time with varying batch sizes.

The results of the study indicated a 53% reduction in defect rates. Thus, there would be an expected improvement in sigma value from 3.78 to 3.89 and a reduction in defects per million opportunities (DPMO) from 11,244 to 8,493. Additionally, simulation model using FlexSim was developed, and the optimum ordering batch size of raw material was obtained. It was also analyzed that idle time for various stations could be reduced by up to 30%.

The utilized framework helps identify defects for managers to increase production efficiency. The workers, operators and supervisors on the production line also need to be trained regularly for identifying the areas of improvement.

The modified Lean Six Sigma framework used in this study includes FlexSim simulation to make the framework robust, which has not been used with LSS tools in the literature studied. Also, the LSS finds very less application in the manufacturing domain, considering which this study tends to add value in existing literature taking a case of an automobile light manufacturing industry.

In this era of globalisation, as competition intensifies, providing quality products and services has become a competitive advantage and a need to ensure survival. The Six Sigma's problem-solving methodology DMAIC has been one of the several techniques used by organisations to improve the quality of their products and services. This paper aims to demonstrate the empirical application of Six Sigma and DMAIC to reduce product defects within a rubber gloves manufacturing organisation.

The paper follows the DMAIC methodology to systematically investigate the root cause of defects and provide a solution to reduce/eliminate them. In particular, the design of experiments, hypothesis testing and two-way analysis of variance techniques were combined to statistically determine whether two key process variables, oven's temperature and conveyor's speed, had an impact on the number of defects produced, as well as to define their optimum values needed to reduce/eliminate the defects.

The analysis from employing Six Sigma and DMAIC indicated that the oven's temperature and conveyor's speed influenced the amount of defective gloves produced. After optimising these two process variables, a reduction of about 50 per cent in the “leaking” gloves defect was achieved, which helped the organisation studied to reduce its defects per million opportunities from 195,095 to 83,750 and thus improve its sigma level from 2.4 to 2.9.

This paper can be used as a guiding reference for managers and engineers to undertake specific process improvement projects, in their organisations, similar to the one presented in this paper.

This study presents an industrial case which demonstrates how the application of Six Sigma and DMAIC can help manufacturing organisations to achieve quality improvements in their processes and thus contribute to their search for process excellence.

The purpose of this paper is to illustrate the successful application of Six Sigma at a small and medium scale plastic parts manufacturing unit. Overall operational excellence is one of the foundations of global competiveness. Indian industries are also keeping up with achieving and maintaining operational excellence through different improvement tools and methodologies. Plastic parts manufacturing industries in India are also on the move to increase their overall quality, productivity and profitability. However, it appears from the available literature that application of Six Sigma, one of the most effective breakthrough improvement strategies having direct impact on bottom line of the organization, is not being explored to its full potential, especially at plastic parts manufacturing industries in India. This study was thus undertaken at plastic products manufacturing plant to introduce Six Sigma to them by applying the same to their chronic problems and drawing improvements in quality, productivity and profitability.

This paper illustrates the real-life case study of improving quality and productivity of injection molding process by phase wiz application of define, measure, analyze, improve and control, the process improvement methodology of Six Sigma.

The critical defects, such as short molding, contamination, injection point and flash are reduced from the process leading to annual savings of INR 10.80 lacs. This is a considerable amount for a small concern in question.

Because this was the pilot project and the firm was of small and medium size, data collection was the major issue, which consequently took considerable time and efforts at define and measure phases. Injection molding is a very salient process for plastic products manufacturing. Almost one-third of plastic products are made by this process. Thus, improving quality of products made out of injection molding process is of paramount importance. The paper is an attempt to exhibit how a small-scale plastic injection molded parts manufacturing unit can put fruitful efforts to achieve competitive advantage through Six Sigma.

From the review of literature, it appears that application of Six Sigma among plastic parts manufacturing units, especially small and medium, is very rare, not in India but across the globe. This case study has opened up the direction to small- and medium-scale plastic parts manufacturing units to implement Six Sigma and to move a step forward toward achieving competitive advantage.

Various method have been used by organisations in the construction industry to improve quality, employing mainly two major techniques: management techniques such as quality control, quality assurance, total quality management; and statistical techniques such as cost of quality, customer satisfaction and the six sigma principle. The purpose of this paper is to show that it is possible to employ the six sigma principle in the field of construction management provided that sufficient information on a particular population is obtained.

Statistical properties of the hyperbolic distribution are given and quality factors such as population in range, number of defects, yield percentage and defects per million opportunities are estimated. Graphical illustrations of the hyperbolic and Gaussian distributions are also given. From that, detailed comparisons of these two distributions are numerically obtained. The impacts of these quality factors are briefly discussed to give a rough guidance to organisations in the construction industry on how to lower cost and to improve project quality by prevention. A case study on a construction project is given in which it is shown that the hyperbolic distribution is better suited to the cost data than the Gaussian distribution. Cost and quality data of all projects in the company are collected over a period of eight years. Each project may consist of a number of phases, typically spanning about three months. Each phase can be considered as a member of the project population. Quality factors of this population are estimated using the six sigma principle.

The paper finds that by using a suitable distribution, it is possible to improve quality factors such as population in range, yield percentage and number of defects per million opportunities.

This paper is of value in assessing the suitability of the hyperbolic and Gaussian distributions in modelling the population and showing that hyperbolic distribution can be more effectively used to model the cost data than the Gaussian distribution.

To provide a sound discussion on the Six Sigma methodology and see how it fits in with other quality initiatives.

A conceptual paper that takes at in‐depth look at the origins, pros and cons of Six Sigma and how it relates to some of the other quality initiatives in industry.

The immediate goal of Six Sigma is defect reduction. Reduced defects lead to yield improvement; higher yields improve customer satisfaction. Six Sigma defect reduction is intended to lead to cost reduction. It has a process focus and aims to highlight process improvement opportunities through systematic measurement. Six Sigma implementation can have negative consequences if applied in the wrong project. Six Sigma is a toolset, not a management system and is best used in conjunction with other more comprehensive quality standards such as the Baldrige Criteria for Performance Excellence or the European Quality Award.

This is a conceptual study and hence there are no hypotheses tested as in an empirical study. It does provide a good foundation for future research.

A very useful source of information and impartial advice for practitioners and researchers planning to practice the Six Sigma methodology and/or understand its pros and cons.

This paper fulfils an identified information/resources need for Six Sigma methodology. It is based on utilizing an extensive set of statistical and advanced mathematical tools, and a well‐defined methodology that produces significant results quickly. The success of this methodology within an organization has significant momentum that can only lead to fundamental organizational cultural transformation.

This paper aims to introduce some of the most important and fundamental points about Six‐Sigma and the main points that researchers, implementers and users should keep in mind in addressing Six‐Sigma to large and small companies as well as system development and/or planning for such applications. In addition to that this author proposes an algorithm for calculating the overall sigma level of a multistage system. Example problems are provided and the weighting technique is discussed.

The paper provides a general review of the Six‐Sigma and some of its extensions. The topic is followed by the introduction of a new algorithm that can be used for calculating the overall sigma level of a multistage system.

In a journey to business excellence, an organization is successful if specific goals for critical processes of the organization are identified. The quality management programs implemented can modernize and simplify activities towards the ultimate goal of the business. Regarding that, the author discusses key issues as such as leadership commitment, what Six‐Sigma would bring to the organization, how it works, Six‐Sigma business strategies, Six‐Sigma methodologies, mathematical formulation of Six‐Sigma, the use of simulation with Six‐Sigma, new Six‐Sigma, benefits of Six‐Sigma, costs of Six‐Sigma, critical success factors and the future of Six‐Sigma.

Six‐Sigma has the potential to change the quality program of an organization. When the selected quality program is Six‐Sigma, the organizational goal is to reach a sigma level of 6, or the objective of 3.4 defects per million opportunities.

In this paper, the author reviews the fundamental concept of Six‐Sigma and elaborates why it is important for small and large organizations to employ Six‐Sigma concepts into their working methodology for quality improvement. The DMAIC steps to be taken, tools to be used and the deliverables are completely discussed. The author also discusses a Six‐Sigma weighted average methodology for calculating the overall Six‐Sigma level for an organization with many working stages or processes.

The purpose of this paper is to introduce a hybrid framework (suppliers, inputs, process, output and customers+define, measure, analyze, improve and control (SIPOC+DMAIC)) aimed at improving supply chain management (SCM) process dimensions in a supply chain (SC) network.

Based upon the critical review of literature, process dimensions (average outgoing quality limit (AOQL), average outgoing quality (AOQ), process Z, defect per million opportunity) critical to SCM performance were identified. A framework consisting of three phases, i.e., design, implementation and results has been conceptualized and a case from paint industry is investigated. Implementation framework makes use of SIPOC model and Six Sigma DMAIC methodology. The goals of the study were achieved by using Six Sigma tools such as brainstorming sessions; root cause analysis, histograms, statistical tools such as control charts and process capability analysis.

Authors made an attempt to propose a conceptual framework for improving process dimensions in a SC network. It is observed from the results that selection of appropriate strategies for improving process performance based upon experiences, and use of statistical tools by cross-functional teams with an effective coordination, guarantees success. Metrics such as AOQL shows the maximum worst possible defective or defect rate for the AOQ. Process Z helps to know about sigma capability of the process.

The framework so developed is tested in a single company manufacturing batches of paint. The study has important implications for the industry since it tries to integrate SCM process dimensions which would help in successful implementation of SCM practices in firm by following the DMAIC process. The framework enables the practitioners to investigate the process and demonstrate improvements using DMAIC which makes use of statistical tools.

Although process dimensions related to SCM are critical to organization competitiveness, research so far has tended to focus on supply chain operations and reference model, balanced scorecard, total quality management, activity-based costing, just in time, etc., but in literature hardly any description of the SIPOC-DMAIC model to improve SCM process performance is provided. The use of statistics in DMAIC provides better insight into the process performance, and process control.