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The purpose of this paper is to develop an innovative and quite new Six Sigma quality control (SSQC) chart for the benefit of Six Sigma practitioners. A step-by-step procedure for the construction of the chart is also given.

Under the assumption of normality, in this paper, the construction of SSQC chart is proposed in which the population mean and standard deviation are drawn from the process specification from the perspective of Six Sigma quality (SSQ). In this chart, the concept of target range is used to restrict the shift in the process within plus or minus 1.5 times of standard deviation. This control chart is useful in monitoring the process to ensure that the process is well maintained within the specification limits with minimum variation (shift).

A step-by-step procedure is given for the construction of the proposed SSQC chart. It can be easily understood and its application is also simple for Six Sigma practitioners. The proposed chart suggests for timely improvements in process mean and variation. The illustrative example shows the improved performance of the proposed new procedure.

The proposed approach assumes a normal population described by the known specification of the process/product characteristics though it may not be in all cases. This may call for a thorough study of the population before applying the chart.

The proposed SSQC chart is an innovative approach and is quite new for the practitioners. The paper assumes that the population standard deviation is known and is drawn from the specification of the process/product characteristics. The proposed chart helps in fine-tuning the process mean and bringing the process standard deviation to the satisfactory level from the perspective of SSQ.

The paper is the first of its kind. It is innovative and quite new to the Six Sigma practitioners who will find its application interesting.

This paper aims to investigate the sources of variation in aluminum profiles hot extrusion process for the purpose of process capability improvement at National Aluminum and Profiles Company (NAPCO) in Palestine. The critical-to-quality characteristics (CTQ) have been determined as base variables to measure the process capability.

The DMAIC (Define, Measure, Analyze, Improve, and Control) Lean Six Sigma (LSS) approach has been adopted to conduct this study. More specifically, historical data analysis and PARETO charts have been employed. The defects' root causes have been determined using the cause-and-effect diagram and decision matrix. A course of suggested actions has been established to help in improving extrusion process capability. Minitab-18 software was used for conducting inferential statistical analysis. A case study considering a geometry CTQ of D3 dimension of bottom sash aluminum profiles 4,332 was selected for analysis.

The results indicated a reduction in DPMO from 89,649 to 15,659, sigma level was improved from 2.84 to 3.65, process yield was improved from 91.04% to 98.43% and cost was reduced from U$75,972 to U$13,250.9 (i.e. a saving of U$62,721). Studying and improving the sigma level of the extrusion process would yield fewer defective products and consequently fewer customer complaints. A validation process which has been conducted during the year 2019 showed a consistent improvement that aligns with the first stage of improvement made on October 1, 2018.

This study focuses on only one critical quality characteristic (CTQ), namely, a geometry CTQ of D3 dimension of bottom sash aluminum profiles 4,332 produced by NAPCO was selected for analysis.

This study would be useful for researchers and practitioners to improve the process capability in aluminum profiles manufacturing industries in general and hot extrusion processes in particular.

Many previous studies on applying LSS-DMAIC methodology have been conducted in aluminum industries in developed countries. According to the literature, it is highly recommended to have more case studies of applying LSS-DMAIC in different industries in developing countries. NAPCO is the only aluminum manufacturing plant in Palestine that hotly extrudes and coats aluminum profiles. Hence, the present study is the first of its kind in NAPCO and in Palestine. Projecting the assessment of the impact of process improvement opportunities and capability analyses into monetary measures are also innovative.

Modern production facilities produce large amounts of data. The computational framework often referred to as big data analytics has greatly improved the capabilities of analyses of large data sets. Many manufacturing companies can now seize this opportunity to leverage their data to gain competitive advantages for continuous improvement. Six Sigma has been among the most popular approaches for continuous improvement. The data-driven nature of Six Sigma applied in a big data environment can provide competitive advantages. In the traditional Six Sigma implementation – define, measure, analyze, improve and control (DMAIC) problem-solving strategy where a human team defines a project ahead of data collection. This paper aims to propose a new Six Sigma approach that uses massive data generated to identify opportunities for continuous improvement projects in a manufacturing environment in addition to human input in a measure, define, analyze, improve and control (MDAIC) format.

The proposed Six Sigma strategy called MDAIC starts with data collection and process monitoring in a manufacturing environment using system-wide monitoring that standardizes continuous, attribute and profile data into comparable metrics in terms of “traffic lights.” The classifications into green, yellow and red lights are based on pre-control charts depending on how far a measurement is from its target. The proposed method monitors both process parameters and product quality data throughout a hierarchical production system over time. An attribute control chart is used to monitor system performances. As the proposed method is capable of identifying changed variables with both spatial and temporal spaces, Six Sigma teams can easily pinpoint the areas in need to initiate Six Sigma projects.

Based on a simulation study, the proposed method is capable of identifying variables that exhibit the biggest deviations from the target in the Measure step of a Six Sigma project. This provides suggestions of the candidates for the improvement section of the proposed MDAIC methodology.

This paper proposes a new approach for the identifications of projects for continuous improvement in a manufacturing environment. The proposed framework aims to monitor the entire production system that integrates all types of production variables and the product quality characteristics.

The purpose of this study is to analyze door-panel alignment defects seen in built-in ovens manufactured in one household appliances company's plant. Alignment defects in oven door panel substantially affect aesthetics of the product which is an important aspect in driving customer preference and satisfaction. Therefore, this study aimed to increase the initial 3.1 sigma level of oven-manufacturing process to at least 4 sigma level by decreasing a particular door-panel alignment defect, which constituted 67.7 percent of the overall alignment defects.

The goals were achieved through a structured Six Sigma implementation with lean element by utilizing various Six Sigma tools such as workflow, Pareto-analysis, measurement system analyses, control-charts, process capability analysis, cause-and effect-diagram and hypotheses tests. A non–value-added step was also eliminated through the lean approach.

Through Six Sigma implementation, the initial 3.1 sigma process performance level has been increased to 4.4 sigma level leading to substantial decrease in alignment defects.

In the quality management literature, not many papers directly deal with aesthetics and appearance problems of the products especially in the household appliances industry. Moreover, hypothesis testing is not frequently used in Six Sigma implementations in the literature. In addition to limited usage of hypothesis testing, very few studies conducted a thorough measurement system analysis. Considering these gaps in the Six Sigma literature, this study fills an important gap in research by implementing a detailed Six Sigma study, enhanced with hypothesis testing and a thorough measurement system analysis, on the aesthetics and appearance of the product.

This study sets out to highlight how a leading manufacturer of tooling, with its continuous improvement drive, has leveraged DMAIC Six Sigma potential to realize cost savings and improved quality on their shopfloor. The study examines one of the shopfloor chronic quality issues which deals with finding a way to reduce the amount of warp incurred in Amada A‐Station punches during the heat‐treat process.

The goals of this specific study were achieved by utilizing Six Sigma tools such as “brainstorming, process mapping, fish‐bone diagrams, histograms, and control charts”.

The analysis resulted in a number of findings and recommendations. A major contributor to the warp was the method used to fix the parts. It was found that, by using a new fixture to hang the parts, the amount of warp could be greatly reduced. The major recommendation was that the new heat‐treat fixture design be implemented as soon as a reliable fixture design has been proven. The value of implementing this new fixture design equates to roughly $10,000 per year in savings, which is due to the elimination of a secondary grinding operation and elimination of scrap parts. The company expects millions of dollars in annual savings as DMAIC Six Sigma process is rolled out to areas on the shopfloor.

The case study shows DMAIC Six Sigma process is an effective and novel approach for the machining and fabrication industries to improve the quality of their processes and products and profitability through driving down manufacturing costs.

Face the process yield rate improvements of motherboard, although general enterprises finish deployment goal of each functions by overall quality managements, through quality improvement methods, industry engineering methods, plan‐do‐check‐act (PDCA) methods and other improvement solutions, but it is only can be improved partially and unable to enhance the yield rate of product to the target. It only can takes one step ahead to enhance the process yield rate of motherboard with six sigma (6 σ) overall DMAIC process and tactics. This research aimed to use six sigma quality improvement tactics by DMAIC systematic procedure and tactics, and find the key factors that effect to the process yield rate of surface mount technology. It also identified the keys input and process and output index to satisfy customer requirements and internal process index. The results showed that the major effective factors by fishbone and process failure modes and effects analysis (PFMEA). If the index of input and output that can be quantified, the optimum parameter can be found through design of experiment to ensure that the process is stable. If the factor of input and output that cannot be quantified, we found out the effective countermeasure by Mind_Mapping, make sure whole processes can be controlled stably, to reach the high product quality and enhance the customer satisfaction.

The purpose of this paper is to explore the fundamental barriers/challenges, benefits, commonly used tools and techniques, organisational infrastructure and impact on organisational performance in three Indian manufacturing companies.

A multi-case study analysis using the exploratory case study research was adopted by the authors to obtain a deeper insight into the Six Sigma implementation within three distinctive manufacturing organisations in India. Interviews were conducted with relevant staff (Six Sigma Deployment Champions, Six Sigma Master Black Belts and Six Sigma Black Belts) in all three companies.

Some of the barriers in implementing and sustaining Six Sigma identified from the case studies include: lack of accuracy of data generated from the processes, lack of understanding of the benefits of Six Sigma in the early stages of its adoption, high-attrition rate of Six Sigma Black Belts and so on. The benefits of Six Sigma included improvement of process yield, reduction of rework and rejection, reduction of raw material inventory, improved on-time delivery, on-time availability of material for production and so on. Supplier-input-process-output-customer, cause and effect diagram, process mapping, hypothesis tests (two sample test, F-test, etc.), control charts (X-bar-R chart, individual chart, etc.), simple graphical tools such as histograms, box plots and dot plots were the most commonly used tools of Six Sigma across the companies that participated for this research. All three companies have reported that Six Sigma had a positive impact on organisational performance and moreover the study also revealed that Six Sigma had positive impact on customer satisfaction, return-on-investment, productivity and product quality.

The study was carried out in three Indian companies and therefore the findings cannot be generalised. The authors are extending the study to three more companies and the findings will be reported in the forthcoming months.

The findings of the study provide a good foundation to understand the fundamental barriers, benefits, commonly used tools and whether Six Sigma is having any impact on business performance in the Indian context. Very few empirical studies have been carried out on Six Sigma implementation in the Indian manufacturing companies and this research sets an agenda for a number of studies to follow on in the forthcoming years.

In authors’ opinion, this is possibly one of the first multi-case empirical studies on Six Sigma implementation in the Indian manufacturing companies. The results of the study can be used to benchmark with similar studies in other countries to understand the good and bad management practices of Six Sigma implementation.

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.

This paper reviews post‐occupancy evaluation (POE) research methods and discusses how these methods can be deployed within the context of a Six Sigma quality framework. The paper outlines the importance of conducting POEs to support the creation and management of optimal spaces for office workers. The authors review a variety of existing POE approaches and discuss their approach to using POEs as part of an ongoing quality framework. The results of a pilot programme using their approach, and application of these POE and quality tools, are discussed in this paper.

As opposed to general literature reviews, by narrowing down the context only around the resources related to Six Sigma tools, this study aims to offer a strong discussion about Six Sigma toolbox which has a vital role in the success of Six Sigma.

Based on a comprehensive literature research, the most used tools; classification of tools; flow of tools with respect to define, measure, analyze, improve and control (DMAIC) steps; tools as critical success factors and reasons of ineffective use of tools are reviewed. To stay focused and not to diverge from the research aim, 60 articles which are suitable to the context and flow of the discussion are selected during the construction of the study.

The study provides a detailed and integrated review of Six Sigma articles about tools. The most used tools are listed from different perspectives and resources, and the role of these tools has been discussed. After a broad review, a more practical and combined classification of Six Sigma tools is proposed. Next, the issue of using which tools during which steps of DMAIC is systematically addressed. Finally, emergence of tools as a critical success factor and the gaps in the literature related to tools of Six Sigma are pointed out.

Addressing important statistics and the facts related to the tools of Six Sigma helps new practitioners in particular to build a strategic filter to select the most proper tools throughout their projects.

This study is unique in investigating only Six Sigma toolbox and providing a literature review on this subject.