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If readers have picked up any of a number of business periodicals within the past five years, they have probably heard of Six Sigma. They have read about it or heard someone talking about the great successes that resulted from applying Six Sigma. The intent of this paper is not to teach the reader all there is to know about Six Sigma. Instead, it aims to provide a brief overview of Six Sigma (for anyone not familiar with it), explain how Ford Motor Company has approached its implementation and how it has been applied in facility management and maintenance activities.

Six Sigma has been part of our business lexicon for more than a decade. Debates on its emergence as a strategic initiative have created critics who consider it as an old wine in a new bottle. Is Six Sigma a management fad? This article presents some common myths and realities of Six Sigma business strategy. The paper provides an excellent resource for those people who would like to know whether Six Sigma is just a management fad or fact.

The paper discusses some common myths and realities of Six Sigma by critically reviewing the existing literature on Six Sigma and also provides a greater insight into the viewpoints of leading academics and practitioners.

Six Sigma is neither a fad nor just another quality initiative. It relies on factual data coupled with hard work and is a disciplined and structured problem‐solving methodology. The authors strongly argue its integration with other continuous/breakthrough improvement initiatives for sustaining the merits of Six Sigma in the twenty‐first century. The paper also elucidates the role of academia in further developing and establishing the best practices of Six Sigma management strategy. Six Sigma will evolve over time like many other initiatives – however, the key concepts, the principles of statistical thinking, tools and techniques of Six Sigma, will stay for many years, irrespective of whatever the “next big thing” will be.

In the authors' opinion, Six Sigma will continue to grow as a powerful management initiative for achieving and sustaining operational and service excellence. However, what will eventually determine whether Six Sigma is viewed by businesses as just a passing management fad or not, largely depends on the leadership and success of its execution. The authors believe that organisations developing and implementing Six Sigma should not view it as an advertising banner for promotional purposes.

The paper yields a great value to both researchers and practitioners of Six Sigma in dispelling the myths of Six Sigma, which have been quite prevalent in the business fraternity.

The purpose of this paper is to achieve cost reduction and quality improvement in SME by implementing define, measure, analyze, improve and control (DMAIC) stages of Six Sigma.

The application of DMAIC stages of Six Sigma was carried out in an SME. The cause of the wear out of the guide wheel was discerned in the define stage. In the measure stage, details from the field gathered and sigma level was determined. Using the paired comparison test and FMEA, various causes of the wear out of wheel was analyzed. The alternate material as a solution obtained was compared with present material in the improvement stage. The alternate material was implemented and ensured with proper documents, process change sheet and adoption.

With the application of easy to use tools a cost reduction of INR10,98,096 per annum and the quality improved from 2.9 to 4.4 sigma.

Easy tools were attempted in DMAIC model vs multiple and complex tools usage in large-sized organizations, which was not that much wide spread in SMEs. The study was carried out by changing the raw material of the outer layer of the guide wheel, by applying DMAIC model. A better grade rubber was used for solving the higher wear of the outer layer of the guide wheel. Significant cost reduction and quality improvement can be obtained in SMES by DMAIC model which leads to better brand image.

The coronavirus (COVID-19) pandemic has led to a surge in demand for health-care facilities, medicines, vaccines and other health-care items. The purpose of this study is to investigate different facets of integrated Green Lean Six Sigma and Industry 4.0 approach in the context of COVID-19 for better healthcare management. Integrating Green Lean Six Sigma (GLSS) and Industry 4.0 (I4.0) has the potential to meet the modern demand of health-care units and also leads to improving the quality of inpatient care with better safety, hygiene and real-time diagnoses. A systematic review has been conducted to determine the tools/techniques, challenges, application areas and potential benefits for the adoption of an integrated GLSS-I4.0 approach within health-care facilities from the perspective of COVID management. Further, a conceptual framework of integrated GLSS-I4.0 has been proposed for better COVID management.

To conduct the literature review, the authors used the preferred reporting items for systematic reviews and meta-analysis and covers relevant papers from the arrival of COVID-19. Based on the systematic understanding of the different facets of the integrated GLSS-I4.0 approach and through insights of experts (academicians and health-care personnel), a conceptual framework is proposed to combat COVID-19 for better detection, prevention and cure.

The systematic review presented here provides different avenues to comprehend the different facets of the integrated GLSS-I4.0 approach in different areas of COVID health-care management. In this study, the proposed framework reveals that the Internet of Things, big data and artificial intelligence are the major constituents of I4.0 technologies that lead to better COVID management. Moreover, integration of I4.0 with GLSS aids during different stages of the COVID management, right from diagnosis, manufacture of items and inpatient and outpatient care of the affected person.

This study provides a significant knowledge database to the practitioners by understanding different tools and techniques of an integrated approach for better COVID management. Moreover, the proposed framework aids to grab day-to-day information from the affected people and ensures reduced hospital stay with better space utilization and the creation of a healthy environment around the patient. This inclusive implementation of the proposed framework will enhance knowledge base in medical areas and provides different novel prospects to combat other medical urgencies.

To the best of the authors’ knowledge, this study is the first of its kind to review different facets of the integrated GLSS-I4.0 approach with a view of the COVID health-care perspective and provides a conceptual framework.

This paper aims to develop a conceptual framework of the implementation of the contact points (CPs) between Lean Six Sigma practices and Industry 4.0 technologies.

A systematic literature review was carried out based on two samples. A first sample containing 78 articles was analyzed through bibliometric indicators. After that, a second sample of 33 articles was analyzed in-depth according to research questions.

The conceptual framework involves 13 CPs between Lean Six Sigma (LSS) practices and I4.0 technologies (what), going through the technical requirements needed (how), categorized as information technology (IT), automation and competence requirements, to finally present the main results reported in the literature (why).

This paper presents an innovative perspective of interactions between digital technologies and LSS practices, expanding knowledge about Digital LSS. Such perspective gives emphasis to the importance of technical requirements, such as communication and connectivity protocols, network topology, machine-to-machine communication (M2M), human–machine interfaces (HMI), as well as analytical and digital skills.

The managerial implications regarding the digitalization of LSS practices address the investments required for the acquisition and maintenance of cyber-physical systems (CPS). Moreover, there is a need for the development of skills so that operators can successfully use the new technologies in a context of continuous improvement.

This paper presents a conceptual framework covering 13 CPs between LSS practices and Industry 4.0 technologies, the technical requirements and the expected results. It is hoped that this framework can assist future research and operational excellence projects towards digitalization.

This paper aims to clarify emerging aspects and trends of Six Sigma literature over 17 years, from 1992 to 2008.

The literature on Six Sigma from 417 referred journal articles in business and management disciplines, information systems and computer science, engineering, healthcare, etc. were systematically analyzed based on a scheme that consists of four distinct dimensions: publication year and journal, major themes, research type, and application sector (i.e. manufacturing vs service).

A number of key findings emerged: Six Sigma research is growing rapidly, covering various disciplines and domains with a great focus on Six Sigma tools and techniques; empirical research is dominant with more emphasis on case study approach; and the growing gap between manufacturing‐ and service‐focused articles implies the return of Six Sigma to manufacturing as its initial base. Although a large volume of literature is available on Six Sigma, the topic is still under development and offers potential opportunities for further research and applications.

The paper provides both academics and practitioners with a useful framework for pursuing rigorous Six Sigma research through explaining the chronological growth of Six Sigma, challenging themes of Six Sigma research, dominating research types and application areas in Six Sigma, and the major sources of Six Sigma information.

Manufacturing companies can competitively be recognized among the most advanced and influential companies in the world by successfully implementing Quality 4.0. However, its successful implementation poses one of the most relevant challenges to the Industry 4.0. According to recent surveys, 80%–87% of data science projects never make it to production. Regardless of the low deployment success rate, more than 75% of investors are maintaining or increasing their investments in artificial intelligence (AI). To help quality decision-makers improve the current situation, this paper aims to review Process Monitoring for Quality (PMQ), a Quality 4.0 initiative, along with its practical and managerial implications. Furthermore, a real case study is presented to demonstrate its application.

The proposed Quality 4.0 initiative improves conventional quality control methods by monitoring a process and detecting defective items in real time. Defect detection is formulated as a binary classification problem. Using the same path of Six Sigma define, measure, analyze, improve, control, Quality 4.0-based innovation is guided by Identify, Acsensorize, Discover, Learn, Predict, Redesign and Relearn (IADLPR²) – an ad hoc seven-step problem-solving approach.

The IADLPR² approach has the ability to identify and solve engineering intractable problems using AI. This is especially intriguing because numerous quality-driven manufacturing decision-makers consistently cite difficulties in developing a business vision for this technology.

From the proposed method, quality-driven decision-makers will learn how to launch a Quality 4.0 initiative, while quality-driven engineers will learn how to systematically solve intractable problems through AI.

An anthology of the own projects enables the presentation of a comprehensive Quality 4.0 initiative and reports the approach’s first case study IADLPR². Each of the steps is used to solve a real General Motors’ case study.

This article aims to focus on implementing Lean Six Sigma (LSS) in steel manufacturing to enhance productivity and quality in the galvanizing process line. In recent trends, manufacturing organizations have expressed strong interest in the LSS since they attempt to enhance its overall operations without imposing significant financial burdens.

This article used lean tools and Six Sigma's DMAIC (Define, Measure, Analyze, Improve and Control) with Yin's case study approach. This study tried to implement the LSS for the steel galvanizing process in order to reduce the number of defects using various LSS tools, including 5S, Value stream map (VSM), Pareto chart, cause and effect diagram, Design of experiments (DoE).

Results revealed a significant reduction in nonvalue-added time in the process, which led to improved productivity and Process cycle efficiency (PCE) attributed to applying lean-Kaizen techniques. By deploying the LSS, the overall PCE improved from 22% to 62%, and lead time was reduced from 1,347 min to 501 min. DoE results showed that the optimum process parameter levels decreased defects per unit steel sheet.

This research demonstrated how successful LSS implementation eliminates waste, improves process performance and accomplishes operational distinction in steel manufacturing.

Since low-cost/high-effect improvement initiatives have not been adequately presented, further research studies on adopting LSS in manufacturing sectors are needed. The cost-effective method of process improvement can be considered as an innovation.

This study aims to explore the integration of Industry 4.0 technologies with lean six sigma practices in the manufacturing sector for enhanced process improvement.

This study used a fuzzy decision-making trial and evaluation laboratory approach to identify critical Industry 4.0 technologies that can be harmonized with Lean Six Sigma methodologies for achieving improved processes in manufacturing.

The research reveals that key technologies such as modeling and simulation, artificial intelligence (AI) and machine learning, big data analytics, automation and industrial robots and smart sensors are paramount for achieving operational excellence when integrated with Lean Six Sigma.

The study is limited to the identification of pivotal Industry 4.0 technologies for Lean Six Sigma integration in manufacturing. Further studies can explore the implementation challenges and the quantifiable benefits of such integrations.

Integrating Industry 4.0 technologies with Lean Six Sigma enhances manufacturing efficiency. This approach leverages AI for predictive analysis, uses smart sensors for energy efficiency and adaptable robots for flexible production. It is vital for competitive advantage, significantly improving decision-making, reducing costs and streamlining operations in the manufacturing sector.

The integration of Industry 4.0 technologies with Lean Six Sigma in manufacturing has significant social implications. It promotes job creation in high-tech sectors, necessitating advanced skill development and continuous learning among the workforce. This shift fosters an innovative, knowledge-based economy, potentially reducing the skills gap. Additionally, it enhances workplace safety through automation, reduces hazardous tasks for workers and contributes to environmental sustainability by optimizing resource use and reducing waste in manufacturing processes.

This study offers a novel perspective on synergizing advanced Industry 4.0 technologies with established Lean Six Sigma practices for enhanced process improvement in manufacturing. The findings can guide industries in prioritizing their technological adoptions for continuous improvement.