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The purpose of this study is to explore and model the strategies to overcome the barriers of Lean Six Sigma (LSS) implementation in Indian small manufacturing enterprises (SMEs).

In this research, 31 strategies of LSS implementation in SMEs have been identified through detailed literature review and out of them, 13 are finalized using statistical tools like CIMTC and Importance-Index analysis. Moreover, the consistency of finalized strategies was examined through reliability test using SPSS software version 22. The finalized strategies are modelled through interpretive structural modelling (ISM) and classified them using MICMAC based on their driving and dependency power.

The key findings of this techno-managerial study are identification and modelling of 13 strategies to overcome adoption challenges of LSS in context of Indian SMEs. The usage of ISM-MICMAC approach provides the guidance to industrialist consider the mutual interaction of strategies during planning and scheduling for LSS projects.

Due to human involvement and judgements, there may be chance of biasness and subjectivity during construction of self-interaction matrix. Also, the number of identified strategies to overcomes barriers of LSS adoption may vary by altering nature, scope and region of research.

Literature is full of studies regarding LSS barriers and its rankings. Also, few studies explored the solutions of LSS barriers and prioritized them. To the best of the authors’ knowledge, our study is very rare to witness which expose the strategies to overcome the barriers and frame the mutual interaction are per the driving and dependence power of strategies. The application of ISM-MICMAC approach suggests a roadmap for implementing LSS approach efficiently through considering developed ISM model of strategies in context of SMEs.

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.

This research aims to conduct a literature review of Lean Six Sigma (LSS) in the Indian context and related research publications and apply bibliometric analysis and the author's visualization to map research trends in this area.

This article conducts a bibliometric analysis of LSS-related research in the Indian context using data gathered from Scopus and Web of Science databases from 2011 to 2022. The review provides information on LSS-related research in the Indian context and evaluates performance based on primary sources, authors, keywords, countries, affiliations, and documents. The analysis employs the Biblioshiny app and Bibliometrix R-tool for data analysis and scientific mapping.

The results of the bibliometric analysis indicate that the LSS culture has widely spread in India. The International Journal of Lean Six Sigma and Production Planning and Control were found to be the most productive sources for publishing LSS-related research articles. Antony J. was identified as the most active author in this field, contributing the most over the years. Among all organizations, NITs have conducted the most comprehensive research on LSS, indicating their significant investment of resources and efforts in studying this methodology and its applications in India. Additionally, the study examined the intellectual, social, and conceptual structures to identify implicit gaps and future research opportunities.

The findings of this study can inform academicians, researchers, practitioners, and policymakers about the state-of-the-art and the specifics of the most prolific studies. This study will facilitate their exploration of emerging research areas in LSS.

To the best of the authors knowledge, this is the first bibliometric analysis of LSS in the Indian context, providing an overview of relevant publications published between 2011 and 2022. This study analyzed 194 articles on LSS in India, which can help researchers and academics identify emerging research areas, suitable collaborators, and relevant journals for future publications.

To redesign the spare parts (MRO) inventory management at Company XYZ's warehouse, considering the conditions after the COVID-19 pandemic.

To address this research project, the authors integrated three methodologies: action research, Lean Six Sigma (DMAIC) and Cross Industry Standard Process for Data Mining. These methodologies integrated the Lean Six Sigma (LSS) 4.0 framework applied in this project.

The spare parts inventory value was reduced by 15%, and inventory turnover increased by 120% without negatively impacting the internal service level.

Practitioners leading or participating in continuous improvement projects (CIPs) should consider data quality (data available and data trustworthiness), problem-solving approach and target area involvement to achieve CIP goals. Otherwise, the LSS 4.0 could fail or extend its duration by several weeks or months.

This project shows the importance of controlling a target area before deciding to conduct a LSS 4.0 project. To address this problem, the LSS 4.0 team implemented 5S during the measure phase of the DMAIC. Also, this project offers significant practitioner and theoretical contributions to the body of knowledge about LSS 4.0.

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.

Many organizations currently transition towards digitalized process design, execution, control, assurance and improvement, and the purpose of this research is to empirically demonstrate how data-based operational excellence techniques are useful in digitalized environments by means of the optimization of a robotic process automation deployment.

An interpretive mixed-method case study approach comprising both secondary Lean Six Sigma (LSS) project data together with participant-as-observer archival observations is applied. A case report, comprising per DMAIC phase (1) the objectives, (2) the main deliverables, (3) the results and (4) the key actions leading to achieving the presented results is presented.

Key findings comprise (1) the importance of understanding how to acquire and prepare large system generated data and (2) the need for better large system-generated database validation mechanisms. Finally (3) the importance of process contextual understanding of the LSS project lead is emphasized, together with (4) the need for LSS foundational curriculum developments in order to be effective in digitalized environments.

This study provides a rich prescriptive demonstration of LSS methodology implementation for RPA deployment improvement, and is one of the few empirical demonstrations of LSS based problem solving methodology in industry 4.0 contexts.

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.

The objective of this study is to systematically identify, categorize and assess the driving factors and interdependencies associated with various types of healthcare waste. The study specifically focuses on waste that has been managed or is recommended for treatment through the application of Lean Six Sigma (LSS) methodologies.

To accomplish the study’s objectives, interpretive structural modeling (ISM) was utilized. This analytical tool aided in quantifying the driving power and dependencies of each form of healthcare waste, referred to as “enablers,” as well as their related variables. As a result, these enablers were classified into four distinct categories: autonomous, dependent, linkage and drivers or independents.

In the healthcare sector, the “high cost” (HC) emerges as an autonomous variable, operating with substantial independence. Conversely, variables such as skill wastage, poor service quality and low patient satisfaction are identified as dependent variables. These are distinguished by their low driving power and high dependency. On the flip side, variables related to transportation, production, processing and defect waste manifest strong driving forces and minimal dependencies, categorizing them as independent factors. Notably, inventory waste (IW) is highlighted as a salient issue within the healthcare domain, given its propensity to engender additional forms of waste.

Employing the ISM model, along with comprehensive case study analyses, provides a detailed framework for examining the complex hierarchies of waste existing within the healthcare sector. This methodological approach equips healthcare leaders with the tools to accurately pinpoint and eliminate unnecessary expenditures, thereby optimizing operational efficiency and enhancing patient satisfaction. Of particular significance, the study calls attention to the key role of IW, which often acts as a trigger for other forms of waste in the sector, thus identifying a crucial area requiring focused intervention and improvement.

This research reveals new insights into how waste variables are structured in healthcare, offering a useful guide for managers looking to make their waste-reduction strategies more efficient. These insights are highly relevant not just for healthcare providers but also for the administrators and researchers who are helping to shape the industry. Using the classification and ranking model developed in this study, healthcare organizations can more easily spot and address common types of waste. In addition, the model serves as a useful tool for practitioners, helping them gain a deeper, more detailed understanding of how different factors are connected in efforts to reduce waste.

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.