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A zero‐defect quality philosophy must exist throughout any organisation before it can successfully implement computer‐integrated manufacturing (CIM). Once established, zero‐defect quality pays for itself by mailing a company a stronger, more vigorous competitor.

This paper recommends a method entitled “SMED 4.0” as a development of conventional single minute exchange of die (SMED) to avoid defect occurrence during production and improve sustainability, besides reducing setup time.

The method builds upon an extensive literature review and in-depth explorative research in SMED and zero defect manufacturing (ZDM). SMED 4.0 incorporates an evolutionary stage that employs predict-prevent strategies using Industry 4.0 technologies including the Internet of Things (IoT) and machine learning (ML) algorithms.

It presents the applicability of the proposed approach in (1) identifying the triple bottom line (TBL) criteria, which are affected by defects; (2) predicting the time of defect occurrence if any; (3) preventing defective products by performing online setting on machines during production as needed; (4) maintaining the desired quality of the product during the production and (5) improving TBL sustainability in manufacturing processes.

The extended view of SMED 4.0 in this research, as well as its analytical approach, helps practitioners develop their SMED approaches in a more holistic way. The practical application of SMED 4.0 is illustrated by implementing it in a real-life manufacturing case.

The present paper proposes a framework for zero-defect manufacturing in Indian industries. Due to the current competitive market, there is a strong need to achieve zero defects from the customer's perspective. A survey questionnaire is analyzed based on the responses and a structured framework is drafted to implement zero defect manufacturing in the Indian industry.

To analyze zero-defect in Indian industries, a literature review and a survey questionnaire constituted a framework. This framework is independent of the type of process and product.

The findings of this study are based on a total of 925 responses received through survey questionnaires by different mediums. The framework has been tested in different manufacturing organizations to achieve zero-defect through the continuous improvement approach.

The study results aim to achieve zero-defect, help to improve customer satisfaction, reduce waste and rework in the manufacturing process. This framework is also used as a problem-solving approach to implement Six Sigma in the Indian industries.

Zero defect manufacturing is growing in India and globally. This framework helps to implement zero defect manufacturing in Indian industries. It is an essential tool to capture the voice of the customer.

Impact of the digitalization on the production and service sector is a highly popular topic in these days and especially, new business models receive increasingly more attention. Under the light of digitalization, the Fourth Industrial Revolution, so-called Industry 4.0, and its impacts on all kinds of process is a promising topic in the academia and also beneficial for the practitioners. Since there are arguments from scholars that Industry 4.0 has an important and shaping effect on marketing, the concept of 7P's in marketing should be incorporated in Industry 4.0 elements. From this point of view, this study focuses on developing the understanding of 7P's based on contemporary perspectives of Industry 4.0.

In order to do that, different criteria related to integration of Industry 4.0 and marketing practices under each marketing-mix element are presented and one of the multi-criteria decision-making (MCDM) methods, the best–worst method (BWM) is used to prioritize the criteria for future implications.

Results indicated that product, process and physical evidence are the most affected marketing-mix factors by considering Industry 4.0. managerial implications which were also presented based on the numerical results.

An in-depth analysis of literature review related to Industry 4.0 revealed that changing and developing technologies are examined in detail mostly around a production perspective. In order to fulfill the gap in the knowledge, this study focuses on the examination of impacts of Industry 4.0 on the marketing-mix strategy. To the best of the authors’ knowledge, this is the first study which merges Industry 4.0 with the marketing mix.

The purpose of this paper is to aim at identifying the relationships between Industry 4.0 (I4.0) technologies and Lean Manufacturing (LM) practices.

A systematic literature review was conducted, in which 93 studies were analyzed according to their content and contextualization level.

In total, 9 I4.0 technologies and 14 LM practices were identified and categorized according to different levels of both value stream application and synergy. From the 126 pairwise relationships, 24 were classified as being of high synergy (e.g. relationship between Cyber-Physical Systems and value stream mapping), revealing the existence of a positive interaction between LPs and I4.0 technologies toward the achievement of a higher operational performance. Further, three future research opportunities were suggested: to validate the proposed synergies among LPs and I4.0 technologies; to distinguish the effects of relationships on all levels of flow; and to examine the effect of such relationships on operational performance.

With the emergence of the Fourth Industrial Revolution, the specificities on the relationship between LM and I4.0 still need further investigation. This paper provides a better understanding of existing literature related to I4.0, LM, and their relationship, as well as pointing out research gaps to encourage future works.

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.

There is increasing interest in the connection between Industry 4.0 (I4.0) and operational excellence approaches; however, studies on the integration between Six Sigma (SS) and I4.0 have been absent from the literature. Integration with I4.0 technologies can maximize the positive effects of SS. The purpose of this study is to understand what types of relationships exist between SS and I4.0 and with I4.0's technologies, as well as the benefits derived from this integration and future directions for this field of study.

A Systematic Literature Review (SLR) was carried out to analyze studies about connections between I4.0 technologies and SS. SLR analyzed 59 articles from 2013 to 2021 extracted from the Web of Science and Scopus databases, including documents from journals and conferences.

The SLR identified relationships between SS and several I4.0 technologies, the most cited and with the greatest possibilities of relationships being Big Data/Big Data Analytics (BDA) and Internet of Things (IoT). Three main types of relationships were identified: (1) support of I4.0 technologies to SS; (2) assistance from the SS to the introduction of I4.0 technologies, and, to a lesser extent; (3) incompatibilities between SS and I4.0 technologies. The benefits are mainly related to availability of large data sets and real-time information, enabling better decision-making in less time.

In addition, the study can help managers to understand the integration relationships, which may encourage companies to adopt SS/Lean Six Sigma (LSS) in conjunction with I4.0 technologies. The results also drew attention to the incompatibilities between SS and I4.0 to anticipate potential barriers to implementation.

The study focuses on three previously unexplored subjects: the connection between SS and I4.0, the existing relationships with different technologies and the benefits resulting from the relationships. In addition, the study compiled and structured different types of relationships for SS and I4.0 and I4.0's technologies, identifying patterns and presenting evidence on how these relationships occur. Finally, exposes current trends and possible research directions.

Manufacturing industry is under pressure to improve productivity and reducing costs through minimization of wastage of resources. This paper aims to present case study of an automobile component manufacturing company to implement lean manufacturing through resource flexibility and also demonstrate the various areas of future scope for improving lean manufacturing.

The case study has been conducted using the flexible system methodology framework. For measuring resource (labor and machine) flexibility and lean manufacturing, various parameters contributing towards labor flexibility, machine flexibility and lean manufacturing are identified. To determine their relative weights, analytical hierarchy process has been employed. A specially designed questionnaire is used to collect the information during case study on different aspects of resource flexibility and lean manufacturing. SAP-LAP analysis has also been carried out, to look into the ways the company is building up resource flexibility and lean manufacturing.

Although all parameters of labor flexibility contribute towards overall labor flexibility but ability of workers to work on different machines has the maximum impact of 35.16 percent. Ability of machines to perform diverse set of operations has maximum contribution of 40.38 percent towards machine flexibility. Similarly elimination of waste is 35.15 percent responsible for lean manufacturing implementation. There is also a huge scope to achieve higher degree of lean manufacturing by implementing zero defects, changing attitude towards change and installing flexible machines. It is inferred that 76.2 percent of lean manufacturing is endorsed by resource flexibility.

The present study includes only labor and machines as the elements of resource flexibility. Other resources may also be included to compute overall resource flexibility.

The present study provides guidelines to assess the status of resource flexibility and lean manufacturing. According to conclusions, feeble areas in the manufacturing system can be identified and a suitable course of action might be planned for the improvement. Hopefully this study will help the firm's management to identify the problems to manage resource flexibility and implement an effective lean manufacturing.

In this work, the theoretical perspective has been used not only to update the original instrument, but also to study the subject from a perspective beyond that usually associated with resource flexibility and lean manufacturing.

The purpose of this paper is to review the literature on Total Productive Maintenance (TPM) and to present an overview of TPM implementation practices adopted by the manufacturing organizations. It also seeks to highlight appropriate enablers and success factors for eliminating barriers in successful TPM implementation.

The paper systematically categorizes the published literature and then analyzes and reviews it methodically.

The paper reveals the important issues in Total Productive Maintenance ranging from maintenance techniques, framework of TPM, overall equipment effectiveness (OEE), TPM implementation practices, barriers and success factors in TPM implementation, etc. The contributions of strategic TPM programmes towards improving manufacturing competencies of the organizations have also been highlighted here.

The literature on classification of Total Productive Maintenance has so far been very limited. The paper reviews a large number of papers in this field and presents the overview of various TPM implementation practices demonstrated by manufacturing organizations globally. It also highlights the approaches suggested by various researchers and practitioners and critically evaluates the reasons behind failure of TPM programmes in the organizations. Further, the enablers and success factors for TPM implementation have also been highlighted for ensuring smooth and effective TPM implementation in the organizations.

The paper contains a comprehensive listing of publications on the field in question and their classification according to various attributes. It will be useful to researchers, maintenance professionals and others concerned with maintenance to understand the significance of TPM.

Outlines the concept of poka‐yoke (as developed by Shigeo Shingo) as a quality methodology, and contrasts it with statistical process control. Highlights the inherent simplicity and the breadth of coverage, and the way it can be used to underpin a policy of zero defect manufacturing.