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The purpose of this paper is to propose a new approach in which cost‐based process weights are used to determine a unique weighted‐defects per million opportunity (DPMO) and its corresponding overall sigma level in order to classify an organization as either “world‐class,” “industry average” or “non‐competitive.”

In order to achieve this objective, the proposed approach uses both internal and external performances of the products and processes in terms of costs involved to determine cost‐based process weights. These weights are then incorporated into the respective DPMOs for computing weighted‐DPMOs. Finally, a unique weighted‐DPMO and its corresponding sigma level are found.

The proposed method is a new one and it involves various costs for determining process weights. The findings reveal that the weight‐based overall sigma level is more realistic than the one that is calculated without weights. Further, the results of this study could provide interesting feedback to six‐sigma practitioners, as they are particular about DPMOs and return on investments in project implementations.

The results of this paper are based on the weights of respective processes and their products that are calculated using various cost aspects. Determining such weights by means of any other process and product factors incorporating the effects of various marketing activities, if any, could extend its generality and fulfil the gap.

The proposed method is simple to implement and the required data can be collected without any additional commitments. Also, it is more generic so that it can be adapted by organizations of any nature. This paper recommends change in the practice from simply using the DPMOs with equal importance to using the weight‐based DPMOs for evaluating overall sigma level (performance) of an organization.

The proposed approach would have a high value among six‐sigma quality practitioners and researchers as it provides a new and more realistic measure for overall performance of an organization during the evaluation process.

This paper seeks to present a way of estimating DisPMO, DePMO, left‐side and right‐side Sigma levels (as the “mutations” of DPMO and Sigma level when applied on customer satisfaction measurements), where all critical attributes (CTQs) contain data sets that are non‐normally distributed.

The calculation of DisPMO, DePMO, left‐side and right‐side Sigma levels is based on dynamic‐multiple CTQs without the need for assuming 1.5 Sigma shift from the mean. Using step‐wise multiple regression, CTQs are then the attributes that significantly influence overall customer satisfaction. This further developed method no longer takes normality assumption for granted, which means that, prior to calculating DisPMO, DePMO, left‐side and right‐side Sigma levels, the data should be proven as being normally distributed. To fulfil the assumption of normality, the primary data are being “replicated” by first generating random numbers that follow normal standard distribution and then adjusting (re‐calculating) these random numbers with the mean, standard deviation, and the skewness of the primary data. Simulation technique is then applied to generate a larger amount of secondary data as the basis for estimating DisPMO, DePMO, left‐side and right‐side Sigma levels.

The application of the method in a Swedish house‐building construction project suggests that: the use of multiple CTQs may reduce the risk for under‐/overestimation of Sigma levels, and DisPMO and DePMO are each other's “mirror” and both of them should be considered when calculating Sigma levels. The calculated Sigma levels suggest that the developer's performance is still quite far below Six Sigma level of performance.

Using the replica of the primary data as a way of approaching normality may be regarded as the main contribution of the paper in addressing one of the challenges in Six Sigma theory.

The purpose of this paper is to assess and benchmark Six Sigma strategies in services sector, namely, the telecom field, by establishing tables of fallouts of non-conforming services and their associated costs along with a custom data envelopment model for benchmarking the different strategic alternatives.

Under normality assumption, process fallout in Six Sigma is around 0.002/3.4 part per million for a centered/shifted process. By introducing Six Sigma to applications in services sector, normality assumption may no longer be valid; hence, fallouts of non-normal attributes are computed for different one-sided quality levels. The associated costs of strategy deployment, fallout and transaction completion are all considered. Data envelopment analysis model is also established to benchmark the Six Sigma strategic plans. The strategies are detailed down to processes and to quality characteristics which constitute the decision-making units. The efficiency of each service unit is computed using both CCR and super efficiency models.

The amount of efforts/costs needed to reduce the variation in a service may differ according to the targeted quality level. For the same Six Sigma quality level, services demonstrate different performance/efficiencies and hence different returns. In some scenarios, moderate quality levels could present high efficiencies as compared to services of higher levels. It was also found that the required improvement is less in the case of Log-normal as compared to normal distributions at some quality levels. This observation is also noted across the presented distributions of this study (Normal, Log-normal, Exponential, Gamma and Weibull).

The deployment of Six Sigma in services is mostly found in time-related concepts such as timeliness of billing, lifetimes in reliability engineering, queueing theory, healthcare and telecommunication.

The paper contributes to the existing research by presenting an assessment model of Six Sigma strategies in services of non-normal distributions. Strategies of different quality levels present diverse efficiencies; hence, higher quality levels may not be the best alternatives in terms of the returns on investment. The computed fallout rates of the different distributions can serve as palm lines for further deployment of Six Sigma in services. Besides, the combination of optimization and Six Sigma analysis provides additional benchmarking tool of strategic plans in both manufacturing and services sector.

The purpose of this paper is to propose an approach to evaluate “on time” performance for a class of Indian railways (IR). This approach is build-up on the theme of six sigma level computation for continues data. The arrival data obtained from a class of IR called “Rajdhani Express” exhibited a unique characteristic: neither did the data followed a distribution nor could it be transformed to fit into a distribution. In this work, the authors present an approach to evaluate on time performance of IR, given such “unruly” data.

An attempt is made to develop a lucid approach, given an unruly data. Initially, the authors plot a histogram using Scott’s method. Later, the authors use Taguchi’s quality loss function to assign weights to each of the bins in histogram. Weights to each of the bins are assigned based on the predefined rules. Finally, sigma level is computed by using weighted defect per million opportunities (DPMO) approach. In this paper, the authors discuss the proposed algorithm, an illustrative example with an emphasis on a class of IR.

Given the unique characteristic (unruly data) of arrival data of IR, the proposed methodology helps in quantifying it is on time performance. This method extends the conventional DPMO approach of computing the sigma level. The proposed method is validated using various data sets of different distributions. Further, this approach can be generalized and applied to data set of any distribution. Since distribution of the data is not the pre-requisite, the proposed approach can be applied to compare (and benchmark) data sets of different distributions. This methodology, thus, paves path to develop newer approaches in quantifying and benchmarking the sigma levels.

In this manuscript, the authors present a case of Rajdhani Express trains, a class of IR. A practicing manager can use this approach to compare the performance of various classes of railways and benchmark their performance. Such an approach, with suitable modifications (if any) can be applied to evaluate performance in various service industries.

Usually, if the data are unruly, the sigma level is computed by using ad-hoc methods that may provide compromising solutions and/or inaccurate results. The developed methodology proposes a unique approach to quantify sigma level, given such an unruly nature of the data. This approach thus fills the long needed gap in addressing such situations. This approach can be applied in various similar situations. A case of IR is presented.

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.

Six sigma is a way to measure the probability of manufacturing a product or creating a service with zero defects. Presents a case study to illustrate how the concept of zero defects, measured by six sigma, can be applied to customer satisfaction measurement and to examine the impact of customer expectations on the company’s strategies for improving satisfaction. The information presented is based on actual studies conducted for a high‐tech manufacturing company in the USA during 1991 and 1992. The performance and expectations values and some of the attributes have been altered for reasons of confidentiality.

Six Sigma within small and medium‐sized enterprises (SMEs) is rapidly emerging as the new wave of change in Six Sigma. The purpose of this paper is to review the implications of applying Six Sigma methodology over the SMEs, taking a specific case of a bicycle chain manufacturing unit. The study could be a paradigm initiative towards high quality products and services at low cost for every SME.

Based on the literature, this paper proposes a process flow chart to present a one‐shot picture of the Six Sigma application in a bicycle chain manufacturing unit which falls in an SME environment. The methodology adopted is DMAIC methodology of Six Sigma, which had been mostly successful so far in large‐scale industries. The methodology has been applied to reduce the bush rejection rate (bush is an important component of a cycle chain) by reducing defects inherent in the processes. The statistical techniques such as two sample t‐test and process capability analysis have been used to establish the process capability before and after the Six Sigma application.

This paper is an attempt to justify the highly useful role of quality management techniques like Six Sigma for SMEs which are normally presumed to be in the domain of large industries. In this paper, Six Sigma methodology has been applied to a small unit manufacturing bicycles chains with dwindling productivity levels. After applying Six Sigma it was found that the chain manufacturing firm can increase its profit by controlling high rejection rate of cycle chain bush. Application of Six Sigma project recommendation brought up the process sigma level to 5.46 from 1.40 by reduction in bush diameter variation in the process of bicycle chain bush manufacturing. This increase in sigma level is equivalent to monetary saving of Rs 0.288 million per annum, which is a noteworthy figure for an industry of such level. The success of this study paves the way to further extend the Six Sigma application to more such industries working in the SME environment.

This paper provides documented evidence of Six Sigma implementation in a bicycle chain manufacturing unit which has been taken as representative of a small and medium‐size industry. The study will yield a great value to academics, consultants, researchers and practitioners of Six Sigma.

The aim of this study is to evaluate the quality of services provided by the Regional Information Center for Science and Technology (RICeST) employing the Six Sigma methodology.

The studied population in this research consists of the users of RICeST. To start with, 120 users of RICeST have been chosen as the samples in a simple random way. Next, in order to collect the data, the LibQUAL+ Questionnaire has been used. Having applied this questionnaire, the gap between the customers' expectations and perceptions has been identified and analyzed. Moreover, the given data are analyzed and their mean and standard deviations are calculated by “SPSS”. Then, in each of the evaluated cases, the calculated standard deviation is compared with the sigma level in the output table in order to specify the qualitative efficiency of services. Accordingly, the error rate in each of the provided services has been determined.

Findings revealed that staff at RICeST could meet the users' minimum expectations of this center in all cases except for the option “the courteous staff”; therefore, they have allocated the efficiency of 50 percent which is equal to 500,000 errors per million. In other words, the average rate seems to be true in all options about the staff in this center. The efficiency of 69.20 percent obtained from the users' view about the quality of existing resources of this center, represents their relative satisfaction with the information resources in RICeST. But it is just in the option “the presence of a web site in the center” that the level of users' expectations is equal to the actual level and the achieved efficiency of 99.97 percent. Finally, it has been found that the quality of library space at the actual level was lower than the maximum level of users' expectation, but higher than the level of users' minimum expectations. The efficiency of 69.20 percent about the physical space of RICeSt indicates 308,000 errors per million. Consequently, it is far from achieving the desired level or the maximum users' expectations.

The increasing expectations of users in libraries and information centers have caused that these types of centers need modern strategies and management techniques in order to comply with these increasing changes and requirements. Evaluating the service quality in the library means to assess the effectiveness of services provided. The effect of evaluating the service quality is not limited to this fact, in which the services are provided only for users, but it is also focusing on the users' application level and compliance with their information needs and satisfaction of the quantity and quality of services.

The purpose of this paper is to study the relationship between reported sigma levels and actual failure rates (FRs) of gamma-distributed processes. The added complexity of the non-normality behavior of the gamma distribution is analyzed for the case of the cycle time (CT) of a real procurement process from the oil and gas industry. Then, recommendations and guidelines for the application of Six Sigma methodology for the case study are proposed.

Sensitivity analysis is conducted to study the relationship between gamma distribution parameters and FRs considering different quality levels. Then, adjustments for implementing Six Sigma programs for gamma processes are proposed. These adjustments consist of first determining the appropriate probability distribution, the standard CT and the due date, followed by setting performance zones and improvement strategies on target gamma parameters that yield the minimal FR.

For gamma-distributed processes, simply reporting the sigma level is not sufficient to capture the main characteristics of the process. These characteristics include process FR, mean setting, shape, spread and amount of variation reduction (i.e. improvement effort) required. That is why caution must be exercised when dealing with one-sided non-normal quality characteristics such as CT.

To the authors’ knowledge, this is the first time that the Six Sigma performance has been evaluated for gamma processes to analyze the link between Six Sigma FRs and gamma distribution parameters leading to the development of a modified Six Sigma methodology for non-normal processes.

This paper, a case study, aims to illustrate the application of Six Sigma in a small-scale ceramic manufacturing industry. The purpose of this paper is to demonstrate the empirical application of DMAIC methodology to reduce failure rate at high voltage (HV) testing of one of the most critical products, insulator.

The case study is based on primary data collected from a real-life situation prevailing in the industry. The case study methodology adopted here is at one small-scale unit wherein the authors have applied DMAIC methodology and observed and recorded the improvement results, especially, reduction in failure rate at HV testing of insulator and, thus, increase in Sigma level.

The results found after implementation of the solutions are very significant. The rejection percentage has been reduced from 0.5 to 0.1 percent and consequently the Sigma level has been improved from 4.4 to 5.0.

This success story can be a guiding roadmap for other such industries to successfully implement Six Sigma to improve quality, productivity and profitability.

This case study will serve as one of the resource bases for the industries which have till not implemented Six Sigma and benefited from the same.

Improved quality and productivity leads to better economy. This case will help industries to serve the society with better economy with improved quality and productivity.

Though ceramic industries in India are having enormous potential for growth, majority of them, especially, small and medium industries are either not aware of or not implementing Six Sigma to reap its multidimensional benefits of improving quality, productivity and profitability. This study highlights the benefits reaped by small-scale ceramic manufacturing industry opening up the avenues for its application at other such organizations.