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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.

Green Lean Six Sigma (GLSS) is a comprehensive approach that aims to reduce waste, emissions and non-value added activities, thus mark an effective impact on sustainability of firms. Despite the numerous benefits of GLSS implementation, it is evident that Micro Small and Medium Enterprises (MSMEs) are still struggling to understand, integrate and implement this strategy. This research work provides a comprehensive analysis of GLSS within MSMEs and proposes a conceptual integration framework of GLSS for improving economic and environmental dimensions of sustainability MSMEs.

A systematic literature review (SLR) methodology was planned to assess and analyse the research articles from 2007 to 2022. Different key elements of GLSS such as barriers, enablers and tool sets have been thoroughly reviewed and analysed for MSME organisations to understand their behaviour and effectively adopt the GLSS approach in their operations.

This study provides the analysis of different perspectives of GLSS and this will contribute to improve different metrics related to emissions and quality in MSMEs. It provides MSMEs industrial managers with a comprehensive knowledge base of GLSS elements, enabling effective deployment.

The present study provides a significant knowledge base and know-how of GLSS to researchers that will assist in deploying this sustainable approach in different industrial domains. The study also assists industrial managers by providing a systematic framework of GLSS for MSMEs. Further, the study also supports society by proving pathway to improve the environmental dynamics through the execution GLSS.

The study is of the first kind that review different facets of GLSS related to MSME. The study not only enhances theoretical know how of GLSS but also guides practitioners how to implement comprehensive GLSS program for improved environmental sustainability.

The purpose of this paper is to investigate the research landscape of LSS 4.0 papers published in two well-known repositories, Scopus and Web of Science (WoS), in terms of publication trends, article distribution by author, journal, affiliations and country, and article clustering based on keywords, authors and countries. In addition, a literature review was carried out to build a conceptual framework of integrated Lean Six Sigma and Industry 4.0 (LSS 4.0) that encompasses operational, sustainability and human factors or ergonomics aspects.

The literature review of integrated Lean Six Sigma and I4.0 publications published in Scopus and WoS databases in the current decade was conducted for the present study. This study categorizes LSS, I4.0 and related research articles based on publication patterns, journals, authors and affiliations, country and continental-wise distribution and clustering the articles based on keywords and authors from the Scopus and WoS databases from 2011 to 2022 using the search strings “Lean”, “Six Sigma”, “Lean Six Sigma” and “Industry 4.0” in the Title, Abstract and Keywords using Biblioshiny, VOS viewer and Microsoft Excel.

In the recent three years, from 2020 to 2022, LSS 4.0 has been substantially increasing and is seen as an emerging and trending area. This research identifies the most influential authors, most relevant affiliations, most prolific countries and most productive journals and clusters based on keywords, authors and countries. Further, a conceptual framework was developed that includes the impact of operational, sustainability and ergonomic or human factors in LSS 4.0.

This article assists in comprehending the trends and patterns of LSS 4.0. Further, the conceptual framework helps professionals and researchers understand the significance and impact of integrating LSS and Industry 4.0 in the aspects of human factors/ergonomic, sustainability and operations. Also, the research induce professionals to incorporate all these factors while designing and implementing LSS 4.0 in their organization.

This conceptual framework and bibliometric analysis would aid in identifying potential areas of research and providing future directions in the domain of LSS 4.0. It will be beneficial for academicians, professionals and researchers who are planning to apply and integrate techniques of LSS and technologies of I4.0 in their organizations and research.

The purpose of this study is to evaluate whether a diversification by grape varieties may help wine producers reduce uncertainty in quantity and quality variations due to increasingly erratic climate conditions.

This study hand-collects granular quantity and quality data from wine harvest reports for vintages 2003 to 2017 for the Valais region in Switzerland. The data allows us to obtain detailed data on harvested kilograms/liters and Oechsle/Brix degrees. It is then merged with precise meteorological data over the same sample period. The authors use this data set to capture weather conditions and their impact on harvested quantities and quality. Finally, they build portfolios including different grape varieties to evaluate whether this reduces variations in quality and quantity over vintages.

The findings highlight that the weather varies relatively strongly over the sample period and that climate hazards such as hail, frost or ensuing vine diseases effectively occur. These strongly impact the harvested quantities but less the quality of the wine. The authors further show that planting different grape varieties allows for a significant reduction in the variation of harvested quantities over time and thus acts as a good solution against climate risk.

The effect of climate change on viticulture is becoming increasingly important and felt and bears real economic and social consequences. This study transposes portfolio diversification which is central to reducing risk in the finance industry, into the wine industry and shows that the same principle holds. The authors thus propose a novel idea on how to mitigate climate risk.

This paper empirically investigates the demand for pastured eggs in the United States and evaluates the welfare consequences of Japan's egg import tariff reductions for the US consumers.

Using household-level Nielsen Homescan panel data, a fixed-effects Heckman two-stage sample selection model is estimated.

The estimation results ascertain the importance of a set of household socioeconomic characteristics, which are found to influence both the purchase probabilities and the consumption amounts associated with pastured eggs. In addition, demand for pastured eggs is estimated to be inelastic, and pastured eggs are found to be a normal good, more specifically a luxury.

The dataset used in this study reflect purchases only for at-home consumption, lacking information on away-from-home purchases.

Building upon previous research, this study makes the following distinct contributions to the current literature. To the best of our knowledge, it constitutes the first study to empirically examine the demand for pastured eggs, using household-level panel data and an estimation model that not only allows for left-censoring but also controls for regional and time fixed effects. Second, the present study reflects a unique effort in analyzing the adverse welfare consequences of the increased egg prices in the United States brought by a reduction of Japanese import tariffs on US-supplied eggs, focusing specifically on pastured eggs.

This study aims to examine the effect of structural transformation on poverty alleviation in Sub-Saharan Africa (SSA) countries with a higher share of services as a percentage of gross domestic product (GDP). The study specifically focuses on the value-added share as a percentage of GDP in the agricultural, manufacturing, industrial, and service sectors using time series data from 1988 to 2019.

The study utilizes the autoregressive distributive lag (ARDL) bound test framework for estimation, based on the conclusions drawn from the augmented Dickey-Fuller and Phillips–Perron unit root tests, which provide evidence of a mixed order of integration.

The result reveals that agriculture value-added (AVA), manufacturing value-added (MVA), industrial value-added (IVA), and services value-added (SVA) have a positive and significant impact on poverty alleviation in both the short and long run. However, the agriculture sector is found to be more effective in reducing poverty compared to the other sectors examined in this study. Additionally, this study challenges the notion that SSA countries have undergone an immature structural transformation. Instead, it reveals a pattern of stagnant structural transformation, as indicated by the lack of growth in the industrial and manufacturing value-added shares of GDP.

To enhance productivity and reduce poverty, SSA economies should adopt a development strategy that prioritizes heavy manufacturing and industrial sectors, leading to a transition from the agricultural to the secondary and tertiary sectors.

The study contributes to the emerging literature on structural transformation by investigating which sector is more efficient in reducing poverty in SSA countries, using the value-added share as a percentage of GDP for agricultural, manufacturing, industrial, and service sectors. The study also aims to determine if SSA countries have experienced immature structural transformation due to the growing share in the service sector.

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.

In light of the recent changes in the internal audit (IA) landscape, the role of auditors has undergone a significant transformation. This paper aims to investigate the effects of applying Lean Six Sigma (LSS) techniques on the effectiveness and efficiency of IA.

The study used a quantitative approach, surveying Iranian internal auditors with a sample size of 384 participants. Data analysis involved confirmatory factor analysis and structural equation modeling.

The analyses demonstrate a significant association between LSS application and IA effectiveness and efficiency. In addition, an exploratory analysis indicates that the application of LSS techniques by less experienced internal auditors had a reverse effect on IA function quality as a component of IA competency. However, IA motivation factors, including education and position, did not mediate the impact of LSS on IA effectiveness and efficiency.

This study was conducted with Iranian internal auditors, which may limit the generalizability of the findings to other countries. However, the primary academic implication of this research lies in its novel perspective on emphasizing the concept of continuous improvement in IA through the use of LSS techniques. By focusing on the need for internal auditors to add value to the business in new ways, this research contributes to the literature on IA quality.

This study has significant implications for the effective management of IA departments. By promoting the application of LSS techniques in IA, lean auditing is enhanced, and IA can create value by improving the quality of its functions. Moreover, IA regulators can benefit from this study as it emphasizes providing guidance and training on LSS techniques to enhance IA skills.

This research is pioneering in applying LSS methodology to enhance the effectiveness and efficiency of internal auditing. It also considers the integration of lean thinking into current audit practices, making it unique and valuable in internal auditing research.

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 article is to analyze the existing studies on the application of artificial intelligence (AI) in lean construction management (LCM). Further, this study offers a classification scheme that specifies different categories of AI tools, as applied to the field of LCM to support various principles of LCM.

This research adopts the systematic literature review (SLR) process, which consists of five consecutive steps: planning, searching, screening, extraction and synthesis and reporting. As a supplement to SLR, a bibliometric analysis is performed to examine the quantity and citation impact of the reviewed papers.

In this paper, seven key areas related to the principles of LCM for which AI tools have been used are identified. The findings of this research clarify how AI can assist in bolstering the practice of LCM. Further, this article presents directions for the future evolution of AI tools in LCM based on the current emerging trends.

This paper advances the LCM systems by offering a lens through which construction managers can better understand key concepts in the linkage of AI to LCM.

This research offers a new classification scheme that allows researchers to properly recall, identify and group various applications of AI categories in the construction industry based on various principles of LCM. In addition, this study provides a source of references for researchers in the LCM discipline, which advances knowledge and facilitates theory development in the field.