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The purpose of the study is to examine how risk factors contribute to the occurrence of defects in a process. By analyzing these risk factors in relation to process quality, the study aims to help organizations prioritize their resources and efforts toward addressing the most significant risks. These challenges, integrated with the emerging concept of Quality 4.0, necessitate a comprehensive risk assessment technique.

Fuzzy logic integrated with an analytic network process is used in the process failure mode and effects analysis for conducting risk identification and assessment under uncertainty. Through a mathematical model, the linkage of risk with Six Sigma is established and, finally, a value–risk matrix is developed for illustrating and analysing risk impact on process quality.

A case study on fused filament fabrication demonstrates the proposed methodology’s applicability. The results show its effectiveness in assessing risk factors’ impact on Six Sigma metrics: defects per million opportunities/sigma level.

By integrating qualitative assessments and leveraging available data, this approach enables a more comprehensive understanding of risks and their utilization for an organization’s quality improvement initiatives.

This approach establishes a risk-centric Six Sigma assessment method in accordance with the requirement of ISO 9001:2015 and in the context of Quality 4.0.

This paper aims to highlight how the six sigma methods helped the medical equipment manufacturing company in finding and analysing the root causes that lead to the reduction in production rate, rejection rates, quality and other major causes that lead to the reduction in productivity of the blood bags manufacturing unit.

Given the critical nature of blood bag manufacturing Six Sigma was chosen as the primary methodology for this research since Six Sigma’s data-driven approach provides a structured framework to identify, analyse and rectify inefficiencies in the production processes. This study proposes the Six Sigma DMAIC (D-Define, M-Measure, A-Analyse, I-Improve, C-Control) encompassing rigorous problem definition, precise measurement, thorough analysis, improvement and vigilant control mechanisms for effectively attaining predetermined objectives.

The paper demonstrates how the Six Sigma principles were executed in a blood bag manufacturing unit. After a detailed and thorough data analysis, it was found that a total of 40 critical-to-quality factors under the five drivers such as Machine, Components, Inspection and Testing, People and Workspace were influential factors affecting the manufacturing of blood bags. From the study, it is identified that the drivers such as inspection and testing, components and machines contribute significantly to increasing productivity.

The paper offers valuable strategic insights into implementing Six Sigma methodologies within the specific context of a blood bag manufacturing unit. The Six Sigma tools and techniques used by the project team to solve issues within the blood bag manufacturing unit can be used for similar healthcare organizations to successfully deploy Six Sigma. The insights from this research might not be directly applicable to other manufacturing facilities or industries but can be used as a guiding reference for researchers and managers.

The current state of scholarly literature indicates a significant absence in the examination of Six Sigma methodologies designed specifically to improve production output in healthcare equipment manufacturing. This paper highlights the application of Six Sigma principles to enhance efficiency in the specific context of blood bag manufacturing.

New product development (NPD) is necessary for business sustenance and customer satisfaction. Six Sigma and Design for Lean Six Sigma (DLSS) efficiently employ the repetitive stages for NPD, leading to quality performance and profitability. This study aims to map the quality performance through NPD attributes through the Lean methodology.

The data on NPD were collected from 267 respondents from manufacturing companies to map the relationship between Six Sigma and DLSS for NPD. Confirmatory factor analysis was employed to confirm model fit, while structural equation modeling was employed to analyze the empirical data for framework testing. The study included nine variables and fourteen hypotheses identified from the literature.

The statistical results of this study show that NPD attributes such as innovation, marketing, organization, customer, product and technology positively influence the Lean Six Sigma structured improvement process (LSSSIP) and DLSS. Moreover, integrating these attributes in Lean planning enhance quality performance. This empirical investigation's findings indicate that ten of the 14 hypotheses were supported, giving the study a strong foundation.

The data collection was limited to northern India; therefore, the results may not be generalizable to other areas of the world.

NPD involves handling technical issues and factors such as cost, operational bottlenecks, economic changes, competitors' strategy and company policy. This study helps understand the various NPD parameters and their relationship to Lean, which enables an effective NPD implementation strategy.

The current philosophy of NPD calls for a concurrent engineering approach; therefore, the entire organization must be part of this process. This study uses the holistic framework by optimizing NPD with Lean Six Sigma (LSS) principles. The study is unique in that, to date, research does not integrate NPD attributes with the objectives of LSS to develop an efficient NPD implementation strategy.

The purpose of this study is to present the systematic literature review (SLR) on Lean Six Sigma (LSS) by exploring the state of the art on growth of literature on LSS within the manufacturing sector, critical factors to implement LSS, the role of LSS in the manufacturing sector from an implementation and sustainability viewpoint and Industry 4.0 viewpoints while highlighting the research gaps.

An SLR of 2,876 published articles extracted from Scopus, WoS, Emerald Insight, IEEE Xplore, Taylor & Francis, Springer and Inderscience databases was carried out following the protocol of systematic review. In total, 154 articles published in different journals over the past 10 years were selected for quantitative and qualitative analysis which revealed a number of research gaps.

The findings of the SLR revealed the growth of literature on LSS within the manufacturing sector. The review also highlighted the most cited critical success factors, critical failure factors, performance indicators and associated tools and techniques applied during LSS implementation. The review also focused on studies related to LSS and sustainability viewpoint and LSS and Industry 4.0 viewpoints.

The findings of this SLR can help senior managers, practitioners and researchers to understand the current developments and future requirements to adopt LSS in manufacturing sectors from sustainability and Industry 4.0 viewpoints.

Academic publications in the context of the role of LSS in various research streams are sparse, and to the best of the authors’ knowledge, this paper is one of the first SLRs which explore current developments and future requirements to implement LSS from sustainability and Industry 4.0 perspective.

This paper explores Lean Six Sigma principles and the DMAIC (define, measure, analyze, improve, control) methodology to propose a new Lean Six Sigma 4.0 (LSS 4.0) framework for employee occupational exams and address the real-world issue of high-variability exams that may arise.

This study uses mixed methods, combining qualitative and quantitative data collection. A detailed case study assesses the impact of LSS interventions on the exam management process and tests the applicability of the proposed LSS 4.0 framework for employee occupational exams.

The results reveal that changing the health service supplier in the explored organization caused a substantial raise in occupational exams, leading to increased costs. By using syntactic interoperability, lean, six sigma and DMAIC approaches, improvements were identified, addressing process deviations and information requirements. Implementing corrective actions improved the exam process, reducing the number of exams and associated expenses.

It is important to acknowledge certain limitations, such as the specific context of the case study and the exclusion of certain exam categories.

The practical implications of this research are substantial, providing organizations with valuable managerial insights into improving efficiency, reducing costs and ensuring regulatory compliance while managing occupational exams.

This study fills a research gap by applying LSS 4.0 to occupational exam management, offering a practical framework for organizations. It contributes to the existing knowledge base by addressing a relatively novel context and providing a detailed roadmap for process optimization.

With the growing need for employees to be innovative, public-sector organizations are investing in employee training. This study aims to examine the effects of a combined Lean Six Sigma and innovation training, using action learning, on public-sector employees’ creative role identity and innovative work behavior.

The authors studied a public service agency in Singapore in which a five-day Lean Innovation Training was implemented, using a combination of Lean Six Sigma and Creative Problem-Solving tools, with a simulation on day one and subsequent team-based project coaching, spread over six months. The authors administered pre- and postintervention surveys among all the employees, and initiated group interviews and observations before, during and after the intervention.

Creative role identity and innovative work behavior had significantly improved six months after the intervention, enabled through senior management’s transformational leadership. The training induced managers to role-model innovative work behaviors while cocreating, with their employees, a renewal of their agency’s core processes. The three completed improvement projects contributed to an innovative work culture and reduced service turnaround time.

Starting with a role-playing simulation on the first day, during which leaders and followers swapped roles, the action-learning type training taught all the organizational members to use various Lean Six Sigma and Creative Problem-Solving tools. This nimble Lean Innovation Training, and subsequent team-based project coaching, exemplifies how advancing the staff’s creative role identity can have a positive impact.

The study aims to identify Green Lean Six Sigma (GLSS) barriers in the context of Higher Education Institutions (HEIs) and prioritize them for executing the GLSS approach.

A systematic literature review (SLR) was used to identify a total of 14 barriers, which were then verified for greater relevance by the professional judgments of industrial personnel. Moreover, many removal measures strategies are also recommended in this study. Furthermore, this work also utilizes Gray Relational Analysis (GRA) to prioritize the identified GLSS barriers.

The study reveals that training and education, continuous assessment of SDG, organizational culture, resources and skills to facilitate implementation, and assessment of satisfaction and welfare of the employee are the most significant barriers to implementing this approach.

The present study provides an impetus for practitioners and managers to embrace the GLSS strategy through a wide-ranging understanding and exploring these barriers. In this case, the outcomes of this research, and in particular the GRA technique presented by this work, can be used by managers and professionals to rank the GLSS barriers and take appropriate action to eliminate them.

The ranking of GLSS barriers gives top officials of HEIs a very clear view to effectively and efficiently implementing GLSS initiatives. The outcomes also show training and education, sustainable development goals and organizational culture as critical barriers. The findings of this study provide an impetus for managers, policymakers and consultants to embrace the GLSS strategy through a wide-ranging understanding and exploring these barriers.

The GLSS barriers in HEIs may significantly affect the society. HEIs can lessen their environmental effect by using GLSS practices, which can support sustainability initiatives and foster social responsibility. Taking steps to reduce environmental effect can benefit society as a whole. GLSS techniques in HEIs can also result in increased operational effectiveness and cost savings, which can free up resources to be employed in other areas, like boosting student services and improving educational programs. However, failing to implement GLSS procedures in HEIs could have societal repercussions as well. As a result, it is critical for HEIs to identify and remove GLSS barriers in order to advance sustainability, social responsibility and operational effectiveness.

GLSS is a comprehensive methodology that facilitates the optimum utilization of resources, reduces waste and provides the pathway for sustainable development so, the novelty of this study stands in the inclusion of its barriers and HEIs to prioritize them for effective implementation.

This project aims to optimise a secondary agricultural company’s reporting and data lifecycle by providing self-help business intelligence at an optimal price point for all business users.

A design for Lean Six Sigma approach utilising the define, measure analyse, design and verify methodology was utilised to design a new reporting and data product lifecycle.

The study found that this approach allowed a very structured delivery of a complex program. The various tools used assisted greatly in delivering results while balancing the needs of the team.

This study demonstrates how improving data analysis and enhanced intelligence reporting in agribusinesses enable better decision making and thus improves efficiencies so that the agribusiness can leverage the learnings.

Improving data analysis increases efficiency and reduces agrifood food wastage thus improving sustainability and environmental impacts.

This paper proposes creating a standardised approach to deploying Six Sigma methodology to correct both the data provisioning lifecycle and the subsequent business intelligence reporting lifecycle. It is the first study to look at process optimisation across the agricultural industry’s entire data and business intelligence lifecycle.

Quality management practitioners have yet to cease the potential of digitalisation. Furthermore, there is a lack of tools such as frameworks guiding practitioners in the digital transformation of their organisations. The purpose of this study is to provide a framework to guide quality practitioners with the implementation of digitalisation in their existing practices.

A review of literature assessed how quality management and digitalisation have been integrated. Findings from the literature review highlighted the success of the integration of Lean manufacturing with digitalisation. A comprehensive list of Lean Six Sigma tools were then reviewed in terms of their effectiveness and relevance for the hybrid digitisation approach to process improvement (HyDAPI) framework.

The implementation of the proposed HyDAPI framework in an industrial case study led to increased efficiency, reduction of waste, standardised work, mistake proofing and the ability to root cause non-conformance products.

The activities and tools in the HyDAPI framework are not inclusive of all techniques from Lean Six Sigma.

The HyDAPI framework is a flexible guide for quality practitioners to digitalise key information from manufacturing processes. The framework allows organisations to select the appropriate tools as needed. This is required because of the varying and complex nature of organisation processes and the challenge of adapting to the continually evolving Industry 4.0.

This research proposes the HyDAPI framework as a flexible and adaptable approach for quality management practitioners to implement digitalisation. This was developed because of the gap in research regarding the lack of procedures guiding organisations in their digital transition to Industry 4.0.

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.