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The purpose of this paper is to demonstrate the vital linkage between six sigma and statistical thinking. The paper also explains the key characteristics required for statistical thinking and some of the common barriers in the implementation of the key principles of statistical thinking.

The objectives of the paper have been achieved in several ways. The paper provides the key principles of statistical thinking and then discusses the possible reasons for lack of statistical thinking in modern organizations. The paper then illustrates the linkage between the statistical principles and six sigma. The tools and techniques of six sigma used within statistical thinking are also highlighted in the paper.

The key findings of this work include the relationship between the two key powerful methodologies: six sigma and statistical thinking, reasons for lack of applications of statistical thinking in organizations, the future role of managers and engineers in companies with regard to statistical thinking era and the commonalities in the application of tools and techniques between these two methodologies.

The paper needs more justification through surveys and case examples and this will be the future step of this study. In fact, one of the co‐authors is currently conducting a survey in the UK organizations to investigate the relationship between statistical thinking and six sigma.

The paper is very practical in nature and it does yield a great value to those people who are currently embarking on six sigma program, especially at senior manager and executive levels.

Very little is published in the field of statistical thinking in the UK academic world. In fact, there is a cognitive gap in this field and this paper certainly forms a good platform for further research that will enable to bridge the gap.

Data science is the study of the generalizable extraction of knowledge from data. It includes a variety of components and develops on methods and concepts from many domains, containing mathematics, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization and data warehousing aiming to extract value from data. The purpose of this paper is to provide an overview of open source (OS) data science tools, proposing a classification scheme that can be used to study OS data science software.

The proposed classification scheme is based on general characteristics, project activity, operational characteristics and data mining characteristics. The authors then use the proposed scheme to examine 70 identified Open Source Software. From this the authors provide insight about the current status of OS data science tools and reveal the state-of-the-art tools.

The features of 70 OS tools are recorded based on the criteria of the four group characteristics, general characteristics, project activity, operational characteristics and data mining characteristics. Interesting results came from the analysis of these features and are recorded here.

The contribution of this survey is development of a new classification scheme for examination and study of OS data science tools. In parallel, this study provides an overview of existing OS data science tools.

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.

The attainment of superior quality and reliability in a manufactured product depends upon the existence of a framework integrating an organization’s capabilities in management, technology and information utilization. With respect to information utilization, statistical tools are particularly essential for optimizing product and process performance. This paper outlines the functions of these tools and examines the steps in which they are adopted by non‐statisticians in industry. A “seven S” approach is explained, highlighting a strategy for the effective deployment of statistical quality engineering.

The purpose of this paper is to find the proper statistical distribution function, which can cover the failure time of a single machine or a group of machines. To this end, an innovative program is written in an Excel software, capable of assessing at least six statistical distribution functions. This research study intends to show the advantages of applying statistical distribution functions in an integrated model format to create or increase productive reliability machines. Productive reliability is a simultaneous combination of efficiency and effectiveness in reliability.

The method of theoretical research methodology comprises data collection tools, reference books and articles in addition to exploiting written reports of the Iranian Center for Defence’s Standards. The practical research method includes deploying and assessing the proposed model for a selected machine (in this case a computerized numerical control machine).

A comprehensive program in an Excel software having the capability of assessing at least six statistical distribution functions was developed to find the most efficient option for covering the failure times of each machine in the shortest time with the highest precision. This is regarded as the most important achievement of the present study. Furthermore, the advantages of applying the developed model are discussed and a large group of which have direct influences on the productivity of equipment reliability.

The originality of the research was ascertained by managers and experts working in maintenance issues at the different levels of the Defense Industries Organization.

The purpose of this article is to demonstrate the application of the tools and techniques of Quality by Design (QbD) approach in an Indian pharmaceutical drug product manufacturing company and to understand the challenges, managerial implications and lessons learned while implementing this initiative.

This work adopts the Action Research methodology for impurity reduction in a drug product manufacturing company in India by using the tools and techniques of QbD approach. Various QbD tools like Design of experiments, process capability evaluation and control charts were effectively utilized for the study.

As a part of QbD implementation in the organization, a specific drug product was identified and the impurity level in the product was studied. Significant variables impacting the impurity were identified and the optimum levels for the significant variables were identified through design of experiments. The solutions were implemented and the impurity levels were reduced significantly.

Even though the article is based on a single case study related to tools and techniques of QbD in a single organization in India, the identified problem is a generic product quality related issue for any pharmaceutical drug product manufacturing company. Hence the findings of this research are applicable to pharmaceutical drug manufacturing industry in general.

This article illustrates the systematic usage of various tools and techniques of QbD methodology in a pharmaceutical drug product manufacturing company. The usage of Design of Experiments for process optimization and application of other tools and techniques are ready references for the practitioners and novice users in the field.

The Design For Six Sigma (DFSS) methodology is one of the most important to achieving excellence in an organization’s product development process. This paper aims to propose a roadmap for product development based on the DFSS for the consumer durables manufacturing industries. The proposed roadmap presents a systematic approach to the phases of the product development process, integrating the statistical techniques and quality tools that should be used in each phase.

This study presents a detailed roadmap for product development, which was built by identifying gaps in the DFSS methods, based on previous studies on the subject. In this step, the opportunities are provided in all phases from creation to discontinuation of the product in the market. In addition, the roadmap presented was validated by team of stakeholders in the product development process of different industrial companies.

The proposed roadmap for the product development process based on six sigma design suggests a visual tool with sequential steps and techniques that allow you to follow the evolution of the development process from idea conception until the product is discontinued in the market. Identifying the priorities of organizations, especially the consumer, regarding the quality and reliability of the product.

The roadmap seeks to facilitate an understanding of the important stages of the product development process and to provide an approach to improving and optimizing the product before the manufacturing process step through the principles of DFSS methodology. This research provides a guide step by step to apply statistical techniques and quality tools in the product development process to achieve high quality and six sigma level in the manufacturing process.

The proposed roadmap of this research combines design for sigma and product development concepts, covering a wide spectrum of relevant activities that include the product development process, the application of statistical techniques and the design of high-quality durable consumer goods to match manufacturing technologies.

Traditional statistical tools are subject to certain constraints when they are applied to quality control in industries where the number of faults per working day is limited. An effective quality monitoring and analyzing tool is therefore needed to meet the specific requirements of these industrial sectors. Proposes a so‐called “Cause‐classified Control Chart”, based on fieldwork in the Nanchang Telecommunications Office of China. Trial results from several posts and telecommunications offices in China in recent years have positively shown that the Cause‐Classified Control Chart is an effective tool for quality enhancement in these specific industrial sectors.

The purpose of this paper is to present the application of a procedure for the quality control of stainless steel tubes produced for automotive exhaust systems from a leading company in the steel sector, based on the Delphi method in accordance with the ISO/TS 16949:2009 and the ISO 9000:2008. Using Delphi methodology, it was possible to identify the main problems in the production lines object of the study, the main defects and their causes. Statistical methods were used to monitor process compliance and capacity. The panel of experts involved in Delphi method was able to identify causes of non‐compliance and suggest corrective actions.

The quality procedure implemented involves the application of the Delphi method and the ISO/TS 16949:2009 standard in conjunction with ISO 9000:2008 to the production line of welded tubes for exhaust systems. The statistical methods used to monitor the process were mainly control charts. Capability index, Cp and Cpk, were used to measure the process attitude to produce compliant outputs. Dimensional data were acquired by non‐destructive testing on diameters and X‐R charts were used to graphically represent the process state of control. Destructive tests were performed to monitor the welding quality and P‐chart were used to assess the proportion of nonconforming units.

In this work, a procedure was developed in order to characterize the production process of TXM tubes realized in the line 31 of the leader company plant. The use of Delphi methodology, in order to incorporate experts opinions in the quality control of stainless steel tubes, was one of the main points of this work. The panel of experts worked together to identify process issues, define their causes and propose corrective actions. The paper provides an overview about the quality approach of one of the world's largest companies in the production of steel and shows also how the statistical tools are used in order to manage process behavior.

The value of this paper is to illustrate an innovative approach to a real life quality problem; it demonstrates how the application of qualitative and quantitative quality instruments in accordance with technical specification can help in increasing and maintaining product compliance and in optimizing the management of resources.

Many of the benefits of Six Sigma (SS) programs stem from the proper use of tools within team‐based improvement projects. However, teams often struggle with selecting and integrating the best tools to use in their projects, compromising not only the project completion but also the solution implementation and sustainability over time. The purpose of this paper is to present a systematic approach to articulate SS tools based on team learning, in order to facilitate tool selection and alignment, team collaboration, solution ownership, and overall solution quality and sustainability.

The action research approach was adopted to explore the link between team learning practices and tool application that combined participation in and reflection upon a successful SS project from the electronic manufacturing industry.

SS tools can be more effectively aligned and integrated with team learning practices. Team discussions and dialogues on SS tool outcomes are pivotal to the development of practical interpretations, allowing the team to create new knowledge, change mental models regarding pre‐conceived solutions, and enable team members to collectively select and interpret tools in a coherent way, leading to the buy‐in of the final solution and the SS program as a whole.

The paper yields value to practitioners and researchers interested in successful SS project execution and program sustainability, by providing a team‐learning approach for effective tool integration. In addition, this paper highlights the need for additional research on team learning within SS programs.