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This work aims to focuse on improving the performance of the new exponentially weighted moving average (NEWMA) scheme for monitoring process dispersion. The authors use the generalized time-varying fast initial response (GFIR) to further enhance the detection ability of variability NEWMA control charts at the process startup. The performance of the proposed chart and other schemes discussed in this article are evaluated; and compared using the average run length (ARL) and standard deviation run length (SDRL) measures. It is observed that the ARL of the proposed scheme is quicker in detecting small and moderate shifts in the process dispersion than its counterparts. The real-life application of the proposed scheme is presented.

The dynamic parameter of GFIR is used to enhance the detection ability of variability NEWMA control charts. The authors apply GFIR to the control limit of variability NEWMA scheme. This further narrows the control limit, hence enabling it to swiftly detect small and moderate changes in process dispersion.

The authors present the performance comparisons by examining the ARL properties of the proposed chart and its counterparts. The performance comparison shows that the proposed chart is highly sensitive in detecting small and intermediate process shifts. The real-life application presented also supports the study’s conclusion from the simulation studies. The performance comparison of the proposed chart and its counterparts shows that the proposed scheme is efficient in detecting process abnormalities, especially at the startup.

In terms of the control limits, the proposed chart is the generalized variability NEWMA control chart in which all the previously proposed NEWMA variant schemes can be obtained. Also, the newly proposed control scheme is more efficient in detecting small or moderate persistent shifts in the process dispersion.

When a process experiences an out-of-control condition, identification of the change point is capable of leading practitioners to an effective root cause analysis. The change point addresses the time when a special cause(s) manifests itself into the process. In the statistical process monitoring when the chart signals an out-of-control condition, the change point analysis is an important step for the root cause analysis of the process. This paper attempts to propose a model approaching the artificial neural network to identify the change point of a multistage process with cascade property in the case that the process is modeled properly by a simple linear profile.

In practice, many processes can be modeled by a functional relationship rather than a single random variable or a random vector. This approach of modeling is referred to as the profile in the statistical process control literature. In this paper, two models based on multilayer perceptron (MLP) and convolutional neural network (CNN) approaches are proposed for identifying the change point of the profile of a multistage process.

The capability of the proposed models are evaluated and compared using several numerical scenarios. The numerical analysis of the proposed neural networks indicates that the two proposed models are capable of identifying the change point in different scenarios effectively. The comparative sensitivity analysis shows that the capability of the proposed convolutional network is superior compared to MLP network.

To the best of the authors' knowledge, this is the first time that: (1) A model is proposed to identify the change point of the profile of a multistage process. (2) A convolutional neural network is modeled for identifying the change point of an out-of-control condition.

Many healthcare institutions, such as hospitals, have recently implemented quality improvement initiatives such as Lean Six Sigma (LSS). However, only a few have sustained the initiatives and remained successful. One of the main reasons for the failure of LSS implementation is that managers tend to view LSS as individual projects. Managers lack a Change Management (CM) focus during the implementation. The primary purpose of this study is to document the implementation of LSS through a CM approach to improve sustainability.

Define-Measure-Analyse-Improve-Control (DMAIC) and the Awareness-Desire-Knowledge-Ability-Reinforcement (ADKAR), a popular CM approach, are combined to propose a new framework. The usefulness of the proposed framework is demonstrated using a case study in a multispeciality hospital located in southern India.

The study found that several factors are responsible for the high Length of Stay (LOS) for patients in the Emergency Department (ED). By implementing this proposed model to implement LSS and taking corrective actions, the average LOS was reduced from 267 to 158 min (a 40% reduction approximately).

The complete step-by-step approach is explained, and the LOS was considerably reduced during the pilot project. The findings will provide valuable insights for healthcare practitioners to understand the steps involved in the combined DMAIC-ADKAR model. The findings would also give healthcare practitioners the confidence to identify suitable tools and implement LSS in organisations where the practitioners work.

According to the authors' knowledge, this is the first study that synergises two models (DMAIC and ADKAR) into a single framework to implement in a hospital.

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.

To achieve changing customer demands, organizations are striving hard to embrace cutting-edge technologies facilitating a high level of customization. Industry 4.0 (I4.0) implementation aids in handling big data that could help generate customized products. Lean six sigma (LSS) depends on data analysis to execute complex problems. Hence, the present study aims to empirically examine the key operational characteristics of LSS and I4.0 integration such as principles, workforce skills, critical success factors, challenges, LSS tools, I4.0 technologies and performance measures.

To stay competitive in the market and quickly respond to market demands, industries need to go ahead with digital transformation. I4.0 enables building intelligent factories by creating smart manufacturing systems comprising machines, operators and information and communication technologies through the complete value chain. This study utilizes an online survey on Operational Excellence professionals (Lean/Six Sigma), Managers/Consultants, Managing Directors/Executive Directors, Specialists/Analysts/Engineers, CEO/COO/CIO, SVP/VP/AVP, Industry 4.0 professionals and others working in the field of I4.0 and LSS. In total, 83 respondents participated in the study.

Based on the responses received, reliability, exploratory factor analysis and non-response bias analysis were carried out to understand the biasness of the responses. Further, the top five operational characteristics were reported for LSS and I4.0 integration.

One of the limitations of the study is the sample size. Since I4.0 is a new concept and its integration with LSS is not yet explored; it was difficult to achieve a large sample size.

Organizations can utilize the study findings to realize the top principles, workforce skills, critical success factors, challenges, LSS tools, I4.0 tools and performance measures with respect to LSS and I4.0 integration. Moreover, these operational characteristics will help to assess the organization's readiness before and after the implementation of this integration.

The authors' original contribution is the empirical investigation of operational characteristics responsible for I4.0 and LSS integration.

Lean Six Sigma 4.0 has brought about a paradigm shift in customization, automation, value creation and digitalization to achieve excellence in human factors, operations and sustainable development. Despite its potential, LSS 4.0 is still in its nascent stage, with researchers striving to identify the key and relevant components of LSS in relation to Industry 4.0. The present study aims to address this knowledge gap through a literature review and subsequently provide a conceptual framework for LSS within the context of digital transformation.

In this study, the authors have conducted a thorough review of reputable articles published between 2011 and 2022, focusing on the integration of Lean Six Sigma (LSS) and Industry 4.0 (I4.0). By using appropriate keywords, the authors identified around 85 relevant articles. The main objective of this integrative literature review was to analyze and extract valuable knowledge from the existing literature on LSS and I4.0. Based on the authors’ findings, a conceptual framework was developed.

The review revealed the motivators, building blocks, tools and challenges of LSS 4.0. The conceptual framework delves into the key aspects of LSS 4.0, focusing on the dimensions of people, process and technology, as well as their subdimensions. These subdimensions serve as the building blocks for developing LSS 4.0 capabilities. The proposed framework visually represents the conceptualization and the relationships among its components.

Only a few conceptual approaches to LSS are developed that include the concepts, new roles and elements of I4.0. As a result, this research investigates the gap in current LSS models preceding I4.0 and develops a conceptual framework to provide a novel and comprehensive summary of the new concepts and components driving nascent and current LSS practices in the digital era.

This study offers practical guidance for implementing LSS in the context of I4.0, emphasizing digital transformation. The findings highlight motivators, building blocks, tools, challenges and spread of LSS 4.0 practices, and present a conceptual framework of LSS 4.0. These insights can help organizations enhance their LSS capabilities and achieve excellence in human factors, operations and sustainable development.

This study aims to make a significant contribution to the model-building efforts of researchers focusing on LSS 4.0. By offering practical guidance, the points discussed in this study help enhance the implementation efforts of practitioners and organizations in the context of I4.0, with a specific focus on digital transformation. The guidance provided takes into account the perspectives of people, processes and technology, providing valuable insights for successful integration.

Sizing system is a fundamental topic in garment fitting. The purpose of this study was to assess the fit of existing police uniforms (shirt, jacket, overcoat and trousers) and develop a sizing system for upper and lower body uniforms of Amhara policemen in Ethiopia.

In total, 35 body dimensions of 889 policemen were taken through a manual anthropometric survey following the procedures in ISO 8559:1989 after each subject was interviewed on issues related to garment fit. The anthropometric data were pre-processed, key body dimensions were identified by principal components analysis and body types were clustered by the agglomerative hierarchical clustering algorithm and verified by the XGBoost classifier in a Python programming environment. The developed size charts were validated statistically using aggregate loss and accommodation rate.

About 44% of the subjects encountered fit problems every time they own new readymade uniforms. Lengths and side seams of shirts, and lengths and waist girths of trousers are the most frequently altered garment sites. Analysis of the anthropometric measurements resulted in 13 and 15 sizes for the upper and lower bodies, respectively. Moreover, the comparison of the developed upper garment size chart with the existing size chart for a shirt showed a considerable difference. This indicates that inappropriate size charts create fit problems.

The study considers the analysis of fit problems and sizing system development in a less researched country. Moreover, the proposed data mining procedure and its application for size chart development is unique and workable.

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.

The application of lean and quality improvement methods is very common in process improvement projects at organisational levels. The purpose of this research is to assess the adoption of Lean Six Sigma™ approaches for addressing a complex process-related issue in the coal industry.

The sticky coal problem was investigated from the perspective of process-related issues. Issues were addressed using a blended Lean value stream of supply chain interfaces and waste minimisation through the Six Sigma™ DMAIC problem-solving approach, taking into consideration cross-organisational processes.

It was found that the tendency to “solve the problem” at the receiving location without communication to the upstream was, and is still, a common practice that led to the main problem of downstream issues. The application of DMAIC Six Sigma™ helped to address the broader problem. The overall operations were improved significantly, showing the reduction of sticky coal/wagon hang-up in the downstream coal handling terminal.

The Lean Six Sigma approaches were adopted using DMAIC across cross-organisational supply chain processes. However, blending Lean and Six Sigma methods needs to be empirically tested across other sectors.

The proposed methodology, using a framework of Lean Six Sigma approaches, could be used to guide practitioners in addressing similar complex and recurring issues in the manufacturing sector.

This research introduces a novel approach to process analysis, selection and contextualised improvement using a combination of Lean Six Sigma™ tools, techniques and methodologies sustained within a supply chain with certified ISO 9001 quality management systems.

The purpose of this paper is to propose a decision support framework that can be used by decision-makers to identify the most convenient disruption analysis (DA) methods for megaprojects and their stakeholders.

The framework was initially developed by conducting a comprehensive literature review to obtain extensive knowledge about disruption management and megaprojects. Focus group discussion (FGD) sessions with the participation of the construction practitioners were then organized to validate and strengthen the findings of the literature review. Consequently, 17 selection factors were identified and categorized as requirement, ability and outcome. Lastly, the most convenient DA methods for megaprojects were identified by performing integrated fuzzy analytical hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) analysis. Additionally, consistency analysis was also conducted to verify the reliability of the results.

The results revealed that the measured mile method is the most appropriate DA method for megaprojects. In case the measured mile method cannot be adopted due to various technical and contractual reasons, the decision-makers are proposed to consider program analysis, work or trade sampling, earned value analysis and control chart method, respectively. Second, the selection factors such as “Comprehensible analysis procedure,” “Existing knowledge and experience about a particular DA method,” “Ability to resolve greater number of disruption events,” “Ability to resolve complex disruption events,” “Ability to exclude factors that are not under the owner's responsibility” and “General acceptance by practitioners, courts, and arbitration, etc.” were given the top priority by the experts, highlighting the critical aspects of the DA methods.

Disruption claims in megaprojects are very critical for the contractors to compensate for the losses stemming from disruption events. Although the effective use of DA methods maximizes the accuracy and reliability of disruption claims, decision-makers can barely implement these methods adequately since past studies neglect to present extensive knowledge about the most convenient DA methods for megaprojects. Thus, developing a decision support framework for the selection of DA methods, this study is the earliest attempt that examines the mechanisms and inherent differences of DA methods. Additionally, owing to the robustness and versatility of this research approach, the research approach could be replicated also for future studies focusing on other project-based industries since disruption is also a challenging issue for many other industries.