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Competitive pressures force companies to seek solutions to eliminate wastes while improving product quality. Lean Six Sigma (LSS) has been considered one of the most effective approaches for business transformation. This article aims to present an empirical case study where LSS and Define, Measure–Analyze–Improve–Control (DMAIC) methodologies are applied to reduce defects in a car parts manufacturer.

The study follows the DMAIC methodology. Design of experiments and hypothesis testing were applied in a single case study.

The main defects and the main factors that cause defective parts were indicated for die-casting and machining processes. Solutions implemented reduced the defect incidence from a chronically high level to an acceptable one. The sigma level rose from 3.4 s to 4 s sustainably.

The study is limited to a single case study, without intention of generalizing the results to other types of industries.

This paper can be a useful guide of how to use DMAIC Six Sigma approach to defect reduction and can be applied in many sectors.

This study offers the knowledge on how to apply the Six Sigma DMAIC methodology, reducing the dependence on specialization courses.

This study describes in detail the process used in a structured improvement exercise including sigma-level calculation, factorial experiments and hypothesis tests – a set of techniques still poorly combined in the literature.

The aim of this paper is to identify some specificities of production planning and control (PPC) activities in the one-of-a-kind-production (OKP) process through an extensive literature review. Relevant aspects related to systems and PPC activities in the context of OKP environment are discussed, and six opportunities for future research are highlighted.

The following research is based on a review of 53 articles published in peer-reviewed journals over the past three decades. After an initial descriptive analysis based on bibliometric indicators, a cluster analysis of 15 most cited articles was carried out using multivariate data analysis techniques and in-depth analysis.

The results reveal some specificities inherent to the clusters featured in the research, including aspects of planning, control and systems for OKP process. This cluster addresses information regarding next-generation manufacturing systems, scheduling and design science, computer simulation and project approach. On the other hand, the authors point out six topics for future research regarding contemporary issues associated with PPC in the context of OKP.

This paper fills an important gap regarding OKP production planning and control practices. The results provide a theoretical overview of different PPC practices suitable for the OKP environment. Furthermore, it can provide insights for scientific developments in order to manage the complexity inherent in the OKP process.

This study aims to analyze the performance of quality indices to continuously validate a predictive model focused on the control chart classification.

The research method used analytical statistical methods to propose a classification model. The project science research concepts were integrated with the statistical process monitoring (SPM) concepts using the modeling methods applied in the data science (DS) area. For the integration development, SPM Phases I and II were associated, generating models with a structured data analysis process, creating a continuous validation approach.

Validation was performed by simulation and analytical techniques applied to the Cohen’s Kappa index, supported by voluntary comparisons in the Matthews correlation coefficient (MCC) and the Youden index, generating prescriptive criteria for the classification. Kappa-based control charts performed well for m = 5 sample amounts and n = 500 sizes when Pe is less than 0.8. The simulations also showed that Kappa control requires fewer samples than the other indices studied.

The main contributions of this study to both theory and practitioners is summarized as follows: (1) it proposes DS and SPM integration; (2) it develops a tool for continuous predictive classification models validation; (3) it compares different indices for model quality, indicating their advantages and disadvantages; (4) it defines sampling criteria and procedure for SPM application considering the technique’s Phases I and II and (5) the validated approach serves as a basis for various analyses, enabling an objective comparison among all alternative designs.

The lean manufacturing (LM) approach is a highly effective method that can be implemented in any industry to streamline production processes, meet customer demand and eliminate any unnecessary waste. This paper aims to propose and evaluate a generic project-based framework grounded on the LM approach for reducing lead time in foundry processes.

Using design science research (DSR), we developed a generic LM project-based framework for reducing lead time in foundry processes.

The developed framework provides an alternative method to implement LM projects to reduce lead time and nonvalue activities in foundry factories.

The findings of this research can guide better lean practitioners for lead time reduction in foundry processes.

This paper contributes to the operational excellence literature when discussing the impact of the LM approach on foundry processes. In addition, the paper provides a roadmap for reducing lead time in a foundry company.

The present paper aims to demonstrate the potential of integration between industrial robotics and Lean Manufacturing (LM) approach to increase the efficiency of an assembly line.

Based on a case study performed in an Italian company, this paper reports a comparative analysis of the results produced on a line balancing study involving a semi-automated production line, aided by an industrial robot.

The results suggest the possibility of implementing industrial robotics in line balancing studies highlighting efficiency gains and idle reduction. Further, it also addresses some concepts directly related to industry 4.0, such as collaborative robotics, artificial intelligence, and lean automation.

Line balancing studies may include advanced robotics in order to extend traditional lean practices toward Digital LM.

This study adds contributions to the operational excellence literature, demonstrating the symbiosis between industrial robotics and LM practices.

This paper aims to provide empirical evidence regarding Lean Six Sigma (LSS) practices supported by Industry 4.0 (I4.0) technologies in heavy vehicle manufacturing processes.

A two-case study was performed involving LSS specialists, leaders and managers of two heavy vehicle manufacturers in Brazil. The data analysis procedure combined content analysis techniques, conceptual maps and network analysis.

The results provide consistent evidence of synergies between LSS and I4.0, including digital mistake-proofing, digital andon, e-kanban, statistical monitoring as well as process mapping aided by cyber-physical systems (CPS) and big data analytics (BDA). To enable such interactions, companies need to invest in automation architectures, system integration, human–machine interfaces and analytical skills.

This study relies on data from a two-case study carried out in two companies from a single manufacturing sector in Brazil. For this reason, the findings cannot be generalized to the entire automotive industry.

There is still a lack of comprehensive research on the application of digital technologies in LSS practices. This is the first study which provides empirical evidence regarding the LSS practices supported by I4.0 technologies used by heavy vehicle manufacturers.

The purpose of this paper is to develop a bibliometric study about Lean Six Sigma (LSS) in the manufacturing process and to conduct an analysis of sources of publication, authorship, citations and other bibliometric indicators. This paper also identifies the research agenda for future research related to the LSS approach in manufacturing processes.

A total of 508 articles published during the period 2002 to 2017 were collected through an automated process from the Scopus and Web of Science databases and later analyzed using techniques such as data mining, bibliometric indicators analysis, cluster analysis, network analysis and word cloud. The boundaries of the study cover studies directed to the manufacturing processes.

The research identified 1,110 authors from 54 countries and 15 most prolific journals among the 162 journals investigated. The study unveils relevant articles, authors and journals that have discussed LSS initiatives in the manufacturing process.

The study findings can make practitioners aware of the state of the art and the specificities of the most prolific studies. Furthermore, this paper also intends to clarify the project themes and tools most used in these works.

The geographical locations of influential articles and authors are revealed. Additionally, frequently used words are listed and helped to develop a research agenda that highlights relevant themes, methods and industries.

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 characterize a sample of Lean Six Sigma (LSS) projects in healthcare settings and discuss some specificities of operational excellence (OPEX) initiatives in hospitals and healthcare organizations in the USA.

A content analysis involving a sample of 23 documents shared by US hospitals was performed in order to achieve the research objectives. Such analysis was based on a conceptual framework developed from the literature review. It was also applied to a quantitative approach, including descriptive statistics, hypothesis testing and correspondence analysis that supported the research.

Most LSSH projects were focused on business transformation and strategic improvements. Simple techniques and tools were predominant such as descriptive statistics, process mapping, 5S and spaghetti charts, usually implemented by Green Belts and Black Belts through the define, measure, analyze, improve and control (DMAIC) method. In addition to the expressive findings reported, these projects' results have been aligned with lead time and operational cost reduction, quality improvement and capacity increase.

The study adds knowledge to the OPEX literature by analyzing the Lean Six Sigma healthcare (LSSH) in hospitals and healthcare institutions in the USA. It also demonstrates that different approaches, such as the kaizen event and DMAIC project show different results according to some techniques and tools applied in the hospital environment.

The empirical evidence presented in this study provides scenery of the LSS practices in the healthcare settings, highlighting the implementation areas, outcomes, tools and techniques mostly used in the North American healthcare institutions.