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1 – 10 of over 4000Narottam Yadav, Mathiyazhagan Kaliyan, Tarik Saikouk, Susobhan Goswami and Ömer Faruk Görçün
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Practical implications
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.
Originality/value
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.
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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…
Abstract
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.
Elisa Gonzalez Santacruz, David Romero, Julieta Noguez and Thorsten Wuest
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…
Abstract
Purpose
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.”
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Rouhollah Khakpour, Ahmad Ebrahimi and Seyed-Mohammad Seyed-Hosseini
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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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…
Abstract
Purpose
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.
Design/methodology/approach
The paper systematically categorizes the published literature and then analyzes and reviews it methodically.
Findings
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.
Practical implications
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.
Originality/value
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.
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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…
Abstract
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.
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Bianca Caiazzo, Teresa Murino, Alberto Petrillo, Gianluca Piccirillo and Stefania Santini
This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status…
Abstract
Purpose
This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.
Design/methodology/approach
The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.
Findings
The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.
Practical implications
Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.
Originality/value
The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.
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Since works-in-process (WIPs) are highly vulnerable to defects because of the variety and complexity of manufacturing processes, the purpose of this paper is to describe how to…
Abstract
Purpose
Since works-in-process (WIPs) are highly vulnerable to defects because of the variety and complexity of manufacturing processes, the purpose of this paper is to describe how to utilize existing analytics techniques to reduce defects, improve production processes, and reduce the cost of operations.
Design/methodology/approach
Three alternatives for diagnosing causes of defects and variations in the production process are presented in order to answer the following research question: “What are the most important factors to be included in prognostic analysis to prevent defects?”
Findings
The key findings for the proposed alternatives help explain the characteristics of defects that have a great impact on manufacturing yield and the quality of products. Consequently, any corrective action and preventive maintenance addressing the common causes of defects and variations in the process can be regularly evaluated and monitored.
Research limitations/implications
Although the focus of this study is on improving shop-floor operations by reducing defects, further experimentation with business analytics in other areas such as machine utilization and maintenance, process control, and safety evaluation remains to be done.
Practical implications
This study has been validated with several scenarios in a manufacturing company, and the results demonstrate the practical validity of the approach, which is equally applicable to other manufacturing sub-sectors.
Originality/value
This study is different from the others by providing alternatives for diagnosing the root causes of defects. Control charts, costs of defects, and clustering-based defect prediction scores are utilized to reduce defects. Additionally, the key contribution of this study is to demonstrate different methods for understanding WIP behaviors and identifying any irregularities in the production process.
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Elisa Verna, Gianfranco Genta and Maurizio Galetto
The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality…
Abstract
Purpose
The purpose of this paper is to investigate and quantify the impact of product complexity, including architectural complexity, on operator learning, productivity and quality performance in both assembly and disassembly operations. This topic has not been extensively investigated in previous research.
Design/methodology/approach
An extensive experimental campaign involving 84 operators was conducted to repeatedly assemble and disassemble six different products of varying complexity to construct productivity and quality learning curves. Data from the experiment were analysed using statistical methods.
Findings
The human learning factor of productivity increases superlinearly with the increasing architectural complexity of products, i.e. from centralised to distributed architectures, both in assembly and disassembly, regardless of the level of overall product complexity. On the other hand, the human learning factor of quality performance decreases superlinearly as the architectural complexity of products increases. The intrinsic characteristics of product architecture are the reasons for this difference in learning factor.
Practical implications
The results of the study suggest that considering product complexity, particularly architectural complexity, in the design and planning of manufacturing processes can optimise operator learning, productivity and quality performance, and inform decisions about improving manufacturing operations.
Originality/value
While previous research has focussed on the effects of complexity on process time and defect generation, this study is amongst the first to investigate and quantify the effects of product complexity, including architectural complexity, on operator learning using an extensive experimental campaign.
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A.Z. Keller and A. Kazazi
Examines Just‐in‐Time (JIT) from its evolution as a Japaneseconcept through to a review of its philosophy and implementation. Citesseveral techniques of implementation. Includes a…
Abstract
Examines Just‐in‐Time (JIT) from its evolution as a Japanese concept through to a review of its philosophy and implementation. Cites several techniques of implementation. Includes a review of the early work of various researchers and practitioners. Concludes that JIT is a very effective manufacturing philosophy which is universal in nature encompassing all aspects of manufacturing. Suggests a few deficiencies in current literature.
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