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The purpose of the work presented in this paper is to capture the current state of Six Sigma as well as to document the current practices of Six Sigma through a systematic literature review so as to extend and update the previous work of Brady and Allen.

The approach to this paper is to answer the questions such as “what is Six Sigma?”, “what are the applications of the Six Sigma?”, “what are the main enablers and barriers to its application?” and “what are the emerging trends?” These questions are used to guide the search of papers from various publication databases even if it is expected that existing literature might not be sufficiently developed to translate each question directly into a finding. The literature is then analysed and the major emerging themes are presented.

Seven key findings (topics on which the views of the authors converged) and two issues (topics on which authors had differing views) have been established. These include the interpretation of Six Sigma, tools and techniques, implementation of Six Sigma, benefits, adoption, enablers and links to other disciplines.

The systematic literature review approach used in this paper allows emerging trends and issues in Six Sigma to be highlighted in a structured and thematic manner, enabling the future work to progress as Six Sigma continues to develop and evolve. The findings also open up new opportunities to apply Six Sigma in the fields that are not widely explored before for instance sustainability and product‐service systems.

While Six Sigma metrics have been studied by researchers in detail for normal distribution-based data, in this paper, we have attempted to study the Six Sigma metrics for two-parameter Weibull distribution that is useful in many life test data analyses.

In the theory of Six Sigma, most of the processes are assumed normal and Six Sigma metrics are determined for such a process of interest. In reliability studies non-normal distributions are more appropriate for life tests. In this paper, a theoretical procedure is developed for determining Six Sigma metrics when the underlying process follows two-parameter Weibull distribution. Numerical evaluations are also considered to study the proposed method.

In this paper, by matching the probabilities under different normal process-based sigma quality levels (SQLs), we first determined the Six Sigma specification limits (Lower and Upper Six Sigma Limits- LSSL and USSL) for the two-parameter Weibull distribution by setting different values for the shape parameter and the scaling parameter. Then, the lower SQL (LSQL) and upper SQL (USQL) values are obtained for the Weibull distribution with centered and shifted cases. We presented numerical results for Six Sigma metrics of Weibull distribution with different parameter settings. We also simulated a set of 1,000 values from this Weibull distribution for both centered and shifted cases to evaluate the Six Sigma performance metrics. It is found that the SQLs under two-parameter Weibull distribution are slightly lesser than those when the process is assumed normal.

The theoretical approach proposed for determining Six Sigma metrics for Weibull distribution is new to the Six Sigma Quality practitioners who commonly deal with normal process or normal approximation to non-normal processes. The procedure developed here is, in fact, used to first determine LSSL and USSL followed by which LSQL and USQL are obtained. This in turn has helped to compute the Six Sigma metrics such as defects per million opportunities (DPMOs) and the parts that are extremely good per million opportunities (EGPMOs) under two-parameter Weibull distribution for lower-the-better (LTB) and higher-the-better (HTB) quality characteristics. We believe that this approach is quite new to the practitioners, and it is not only useful to the practitioners but will also serve to motivate the researchers to do more work in this field of research.

This study aims to present an overview of leading research trends in the lean six sigma domain published in the International Journal of Lean Six Sigma (IJLSS) since its inception.

The study analyses articles published between 2010 and 2019 in IJLSS using the bibliometric technique. The results of data analysis identify the most prolific authors, their affiliation, citation trends and highly cited articles from the journal. Further, a graphical analysis involving bibliographic coupling and co-citation analysis of the corpus enriches the investigation.

The results of the bibliometric analysis suggest that the number of IJLSS’s publications and citations grew markedly over time (from 4 citations in 2010 to nearly 1,324 in 2019). The organizational diversity and collaboration among authors publishing in IJLSS are trending upwards. Case study and focus group are the two most common research designs in publications. In the study, three major themes emerged: implementation of lean on business, integration of lean and six sigma and the effects of lean six sigma on businesses.

The study finding informs and educates practitioners and scholars about various qualitative research tools, applications and methods of implementing lean six sigma tools in different industry sectors.

The study uses bibliometric analysis to propose a novel categorization of research published in IJLSS and to report on the utilization of various lean tools in the journal. The study provides guidance for new future research besides offering a thorough introspection of the lean and six sigma domains.

The purpose of this paper is to use a practical case study approach to demonstrate the power to use lean Six Sigma (LSS) to improve service process in a higher education institution (HEI). The paper also illustrated the barriers and challenges met and lessons learnt for the LSS adoption in this HEI.

Prior to the study, extensive literature review was conducted to understand various aspects of LSS in HE industry. The authors use a single descriptive case study as methodology to explain how DMAIC was applied within a HEI environment.

In this LSS case study, the team found HEI service process contains a large human behavior component, which dramatically increases the unpredictability of the entire service delivery process and increases the complexity of the process and the ability of the improvement team to identify the root cause. This case study demonstrates the numerous challenges will occur in working with the intangible factors that are both hard to recognize, quantify and rarely tracked by organization.

During the research, the pre-award service process was studied, data were recorded and various statistical tool and techniques were used to discover and resolve the root cause. The lessons learnt of the LSS adoption in this service process in HEI and the problems encountered were all recorded in this study, which will be helpful for future research in HEI industry.

From the literature review, LSS has been widely adopted in manufacturing industry, increased adoption in service, but there has been limited academic research about the implementation in nonprofit, service sectors, particular to higher education industry. The major benefit of implementation LSS in both manufacturing and service is considerable improvement to the bottom line. However, in this HEI case study, it has opened up the direction to implement LSS to better serve your customer as ultimate mission instead of financial gains.

The aim of this paper is to deploy Six Sigma (SS) methodology for addressing the customer complaints pertaining to Chemical-X in an Indian chemical company.

The study followed a structured Define, Measure, Analyze, Improve, Control (DMAIC) approach to address the customer complaints. The complaints have been classified into different categories along with a project charter in the define phase. In the measure phase, measurement system analysis (MSA) and supplier, input, process, output and control (SIPOC) have been applied. In the analyze and improve phase, why–why analysis, process capability study, how–how analysis, Gage repeatability and reproducibility and Taguchi design have been applied to optimize the manufacturing process parameters for Chemical-X. Lastly, in the control phase, validation of 20 batches has been piloted to validate the optimized parameters.

The findings of this study highlight the optimization and prioritization of the process parameters. It shows that humidity has the least impact on the manufacturing of Chemical-X, whereas shift type has the maximum impact. The experimental output indicates that the 1st Shift, the holding time after grinding should be twenty-four hours, and the temperature after grinding should be 40 °C to reduce the customer complaints concerning lumps formation in Chemical-X.

The study is performed for a single product (Chemical-X). It has focused only from the manufacturing process view and not from the transportation, suppliers and downstream supply chain view.

A systematic and data-driven approach of the SS methodology ensured that the customer complaints due to lumps formation reduced from 5% (approx.) to 1% (approx.) which resulted in the cost saving of INR 4 million (approx.) annually.

Micro-enterprises have a vital role in the development of a nation by creating employment, encouraging entrepreneurship, balancing social income and educating the workforce. Yet, micro-enterprises face significant operational challenges such as low productivity, high production costs and long changeover times. These challenges are often overlooked by researchers and practitioners but have a drastic impact on micro-enterprises' operational performance. With over 95% of the economy consisting of micro-enterprises, it is vital to improve operational performance and competitiveness of the micro-enterprises.

Considering the data availability and practical challenges of gathering data from micro-enterprises, an action research methodology was selected as a suitable research method. Following the initial diagnostic visits, planned interventions were prepared and results were observed to gather data and draw conclusions.

Findings suggest that commonly adapted performance improvement initiatives by large enterprises cannot be directly applied in micro-enterprises to enhance operational performance. Micro-enterprises lack the critical resources and company culture to easily adapt these initiatives. On the other hand, when these performance improvement initiatives are filtered according to specific needs of micro-enterprises, continuous improvement initiatives were often found to be effective in improving operational performance. Specifically, 5S, single-minute exchange of dies (SMED), suggestion schemes, layout improvements, management coaching, visual management, empowerment were found to be useful and suitable to address command and control culture, low productivity, unorganised workspace, undelegated authority, low skilled employees and unwillingness to take responsibility problems.

Further research is required to investigate the specific factors that affect the adaptation of continuous improvement initiatives such as culture and industry type.

This research extends and contributes the current literature on continuous improvement initiatives by revealing how performance improvement initiatives need to be filtered according to the specific needs of micro-enterprises, and how these initiatives can be used to address specific problems to improve operational efficiency. It provides a conceptual framework to guide the decision-making process on operational performance improvement in micro-enterprises.

The purpose of this paper is to demonstrate the systematic application of Six Sigma tools for identification and reduction of cost of poor quality (COPQ). The study examines one of the chronic problems of failure of cooling fan assembly at repair division of a company dealing in helicopter components.

The case adopted Six Sigma Define-Measure-Analyze-Improve-Control (DMAIC) methodology to achieve the goal of reduction in COPQ.

After completing the Define, Measure and Analyze phase, it was found that use of extreme tolerances and cross-fitment of bearings are the root cause of cooling fan assembly failure. The major recommendations made during the Improve phase were to design a bearing matching software for improving the cross-fitment of bearings and to procure a hydraulic jig with electronic jig instead of manual jig. The value of implementing these recommended solutions equate to a saving of INR 34 lacs per annum. Since it was a chronic problem, the company expects this to be a recurring saving.

This specific case exhibits the successful application of Six Sigma DMAIC methodology in repair and maintenance for driving down the cost of failure and improved processes.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

In a packaging olives manufacturing system, the drained weight of the product plays a decisive role in customer’s satisfaction as well as in financial saving for the organization. The purpose of this study is to minimize the variation of the drained weight of olives in the production system to avoid the negative consequences.

The research develops a practical implementation step-by-step of Six Sigma define, measure, analyze, improve and control (DMAIC) in reducing the variation of the drained weight of olives.

Data analysis was used at various phases of the project to identify the root causes of rejection and rework. As a result of the necessary interventions and actions to optimize the manufacturing process, the standard deviation of drained weight was significantly reduced by 51.02%, with a 99.97% decrease in the number of parts per million defectives. Thus, the yield of the production process was improved by 8.24%. The estimated annual savings from this project were US$ 228,000 resulting from reduced rejection and rework.

This research may be used in packaging olives production systems as a tool for managers and engineers planning to increase productivity and efficiency while also improving product quality. The study also provided the organization with helpful actions that will be used to guide future Six Sigma operations management on the system. Thus, practical guidelines and solutions are provided.

In this project, for the first time, the Six Sigma methodology has been applied to solve a real-world problem in the packaging olives manufacturing system and to show that the DMAIC approach may assist to improve the efficiency of their operations and hence contribute to their quest toward continuous improvement.

With increasing choice from a range of programs, improvement project selection within broader supply chain context and resource constraints has become a major research challenge. The purpose of this paper is to investigate the different criteria for selecting Six-Sigma (SS) projects based on previous studies. The study is supported by two grounded theories: resource-based view and institutional norms. The criteria include: first, business drivers for improvement and the common performance metrics deployed; second, the organization’s stakeholders needs; and third, process owner’s needs.

To determine the relative importance of influencing factors, opinions were collected from 30 experienced practitioners including SS champions/master black-belts, company directors, consultants, and process owners through a series of interviews in small, medium, and large organizations including multi-national organizations. The evaluation of criteria is based on analytical hierarchy process.

The results show that impact on customer, financial impacts, and impact on operational goals are the most significant factors in selecting SS improvement project.

This study is a first attempt to determine the relative weight among SS project selection criteria, which help the practitioner to allocate their limited resources in implementing SS project.