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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.

The coronavirus (COVID-19) pandemic has led to a surge in demand for health-care facilities, medicines, vaccines and other health-care items. The purpose of this study is to investigate different facets of integrated Green Lean Six Sigma and Industry 4.0 approach in the context of COVID-19 for better healthcare management. Integrating Green Lean Six Sigma (GLSS) and Industry 4.0 (I4.0) has the potential to meet the modern demand of health-care units and also leads to improving the quality of inpatient care with better safety, hygiene and real-time diagnoses. A systematic review has been conducted to determine the tools/techniques, challenges, application areas and potential benefits for the adoption of an integrated GLSS-I4.0 approach within health-care facilities from the perspective of COVID management. Further, a conceptual framework of integrated GLSS-I4.0 has been proposed for better COVID management.

To conduct the literature review, the authors used the preferred reporting items for systematic reviews and meta-analysis and covers relevant papers from the arrival of COVID-19. Based on the systematic understanding of the different facets of the integrated GLSS-I4.0 approach and through insights of experts (academicians and health-care personnel), a conceptual framework is proposed to combat COVID-19 for better detection, prevention and cure.

The systematic review presented here provides different avenues to comprehend the different facets of the integrated GLSS-I4.0 approach in different areas of COVID health-care management. In this study, the proposed framework reveals that the Internet of Things, big data and artificial intelligence are the major constituents of I4.0 technologies that lead to better COVID management. Moreover, integration of I4.0 with GLSS aids during different stages of the COVID management, right from diagnosis, manufacture of items and inpatient and outpatient care of the affected person.

This study provides a significant knowledge database to the practitioners by understanding different tools and techniques of an integrated approach for better COVID management. Moreover, the proposed framework aids to grab day-to-day information from the affected people and ensures reduced hospital stay with better space utilization and the creation of a healthy environment around the patient. This inclusive implementation of the proposed framework will enhance knowledge base in medical areas and provides different novel prospects to combat other medical urgencies.

To the best of the authors’ knowledge, this study is the first of its kind to review different facets of the integrated GLSS-I4.0 approach with a view of the COVID health-care perspective and provides a conceptual framework.

The present study aims to explore the barriers to Lean Six Sigma (LSS) implementation in the healthcare sector and develop the ranking of finalized barriers using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach under a fuzzy environment.

The LSS barriers are identified through the literature review and validated by the expert's opinion and statistical analysis. A total of 124 experts were identified through the purposive sampling method for conducting this study. A questionnaire survey method is used to collect the data related to identified LSS barriers in the healthcare sector. The screened barriers are ranked through the Fuzzy DEMATEL approach.

In this study, a total of 21 barriers were identified with the help of a systematic literature review and screened 13 significant barriers by the expert opinions of healthcare personnel. The result reveals that “Lack of top management commitment and support, lack of awareness about LSS”, “resistance to culture change and inadequate resources emerges as the most critical barriers”. The prioritization of barriers facilitates the managers to make effective policies and guidelines for LSS implementation in healthcare organizations.

To avoid LSS implementation failure, the practitioners and researchers need to focus on LSS barriers as per suggested ranking more conventionally and make plans and adoption policies accordingly.

This study is unique in terms of investigation and empirical analysis of LSS implementation barriers in the healthcare sector in the Indian context. The outcomes of the present study will help the managers of healthcare organizations to make the strategies and policies for LSS implementation as per the recommended LSS barriers.

This paper aims to uncover the significance of capacity, capacity utilization (CU) and its role in the quality and productivity improvement in an industrial environment. Besides, the current study is also aiming to explore the various ways to estimate CU and its status across the world.

In the present study, a comprehensive literature review on capacity and CU is carried out to expose the research direction in the field of CU. This work is primarily focused on capacity, CU and their estimation methods based on the research in various industries of different countries and current status in present scenario across the world.

The literature reveals that CU estimation is carried out by some government/central agencies at the national or sector level rather than the industry level in most of the productive nations. As far as industrial growth is concerned, capacity management should be carried out at a particular industry level so that engineering managers can be able to find out loopholes for huge capacity waste within the plant. It is observed that CU in the industrial sectors mainly computed by time series method, survey method, economic approach and engineering approach worldwide.

This paper tries to cover almost all research work in the field of CU in various industrial sectors. However, the organizations which are producing the product with limited demand may get benefit inadequately.

This paper provides a vision to management toward productivity improvement through optimal utilization of available resources. As in most organizations, CU issues are much neglected areas.

This paper provides valuable insights on capacity and CU in the industrial sector across the world. Besides, it focused on comprehensive literature of capacity and various methods to estimate CU in industrial sectors.

Lean six sigma (LSS) has attained a prominent position in mature organizations but small- and medium-sized enterprises (SMEs) are struggling in the proper implementation of LSS in their core business. This study aims to make a comprehensive analysis of LSS implementation barriers in SMEs so that LSS execution can be much fluent in SMEs.

This research work is carried out based on investigation of LSS barriers through extensive literature review. For validating the identified barriers, a questionnaire survey was conducted, and out of 400 samples, 260 responses received back. The collected responses are analyzed statistically and found 16 significant barriers. The finalized barriers are modeled using interpretive structural modeling (ISM) and clustered them through matrice d’impacts croisés-multiplication appliquée a un classement (MICMAC) analysis. Furthermore, to check the consistency of results, ISM-MICMAC outcomes are validated through structural equation modeling (SEM).

The result reveals that 16 LSS implementation barriers are finalized through expert’s opinion and validated through statistical reliability test with Cronbach’s alpha value of 0.820. The ISM model reveals that the management relevant barriers are exhibiting the leading role to influence the implementation of LSS in SMEs. Moreover, the obtained results validated through SEM are found in good agreement.

During pairwise comparisons, there may be some prejudice and subjectivity as human judgments are engaged.

This study provides impetus to practitioners and consultant for the initiation of LSS in the business organization through tackling the LSS barriers as per their driving and dependence power.

In the past, limited studies had explored the LSS barriers, but a few studies analyzed the mutual relationship between barriers. No such study is reported in literature that validates the mutual interaction model of LSS barriers. Hence, this paper presents the original research work of identification and modeling of barriers associated with LSS implementation in SMEs through hybrid ISM-SEM approach.

The analyze phase of Lean Six Sigma (LSS) project is an important phase where the project heads and organizational directors need to select the critical issues for further improvements. The present work is primarily focused on analyze phase of LSS project to prioritize the critical to quality (CTQ) in a particular case industry.

The CTQ prioritization is being done based on the five evaluation criteria found from the literature. The weights of the criteria are determined through the modified digital logic (MDL) method. The identified CTQs in assembly section of case industry have been ranked through the gray relational analysis (GRA) under fuzzy environment. The results of the study have been validated using fuzzy VlseKriterijumska Optimisacija I Kompromisno Resenje (VIKOR).

It is found that the “cost” criterion is the most significant among other criteria with MDL weight of 0.3. Through fuzzy-GRA, out of ten identified CTQs, non-availability of rack system is found to be the most critical issue in assembly section of case industry. The perceptions of industrial manager and production head of case industry are strongly in favor of the obtained results and have implemented the suggested solutions.

To sustain in the competitive environment and produce quality product at right time, organizations need to control their CTQs as per their criticality. For this, the decision-making becomes quite complex to select the most critical factors because of the fascinating nature of various criteria and sub-criteria. The present study is the first attempt that has implemented the multi-criteria decision-making approach in analyze phase of LSS project.

At the present time, Industry 4.0 has proven its effectiveness and significance in automation and data exchange within industries across different sectors worldwide. In the current literature, there is still a lack of research on adopting Industry 4.0 in the manufacturing setting in developing economies. The main purpose of the present study is to explore the fundamental pillars and framework for ease of adoption of Industry 4.0 in manufacturing environments, along with highlighting the benefits and challenges.

In this study, a systematic literature review has been conducted through protocol, search, appraisal, synthesis, analysis, report (PSALSAR) model. In the literature, the articles are included within time span of 2008–2022, consisting keywords like Industry 4.0, blockchain, machine learning, artificial intelligence, Internet of Things, 3D printing, big data analytics, etc. Based on available literature, conceptual implementation framework of Industry 4.0 is proposed.

This study explored the key ingredients that play an essential role to bridge the gap and construct a strong relationship among physical and cyber world. The results reveals that the emerging technologies such as IoT, blockchain, artificial intelligence, augmented reality, 3D printing, big-data analytics, cloud-computing join hands to accomplish success in Industry 4.0 by reducing human interference for effective and efficient systems. In addition, the study also explored the possible benefits of emerging technologies with challenges faced by manufacturing setting during adaptation of Industry 4.0.

As per the authors' best knowledge, no research articles are found in literature which explore various emerging technologies in Industry 4.0 with its implementation framework in the manufacturing setting in developing economies. The main focus of the present study is to discover the literature review in defined area and find the research gap among current scenario and future trend for execution of Industry 4.0 in manufacturing environment.

The increased health-care costs, improved service quality and sustainability-oriented customer demand have forced the health-care sector to relook their current process. The present work deals with the identification, analysis and prioritization of just in time (JIT) enablers in the health-care sector.

JIT leads to waste reduction, improves productivity and provides high-quality patient care. The practical implementation of JIT depends on vital factors known as enablers. The enablers have been found through the comprehensive literature review and prioritized using responses from different health-care facilities of the national capital region of India. Grey relational analysis (GRA) has been used in the present study to rank enablers and ranks were further validated using the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) and sensitivity analysis.

It has been found that top management support, teamwork and real-time information sharing are the most significant enablers of JIT in health care with grey relational grades 0.956, 0.832 and 0.718, respectively. The corresponding closeness coefficients of the fuzzy TOPSIS for the enablers were found as 0.875, 0.802 and 0.688, respectively. The findings of the present research work will facilitate the health-care organizations to implement a comprehensive JIT approach that further leads to improved patient care at a low cost.

The present study is unique in terms of the exploration of the readiness measures or enablers of JIT using GRA and fuzzy TOPSIS. The findings of the present research work will facilitate the health-care organizations to optimize their resources for better patient care.

The purpose of this paper is to identify and prioritize prime just-in-time (JIT) elements in Indian healthcare sector based on the degree of importance and difficulty. This facilitates the implementation of JIT philosophy in healthcare services without any failure as well as provides quality services to patients at low cost.

JIT elements related to health care services have been sorted out from comprehensive literature survey. Moreover, important and difficult JIT elements have been ranked on the basis of score obtained by the quantitative method using statistical tools. Furthermore, validation and prioritization of important JIT elements based on the degree of importance have been computed using the analytical hierarchy process (AHP) and best worst method (BWM).

The present work provides important, difficult and easy to implement JIT elements in healthcare services. Besides, this work justifies the application of decision-making tool (AHP, BWM) for the prioritization of JIT elements in the health care sector.

The present study develops a deep understanding of JIT concepts in health care services. Furthermore, it motivates professionals for implementing JIT in healthcare services with healthy situation for both hospital and supplier.

The present work is beneficial for all the dimensions of sustainable development (social, economic and environmental). The implementation of JIT with a proper understanding of its elements results in an improved patient care, lesser cost of healthcare delivery, time and better management of associated medical items. This work also facilitates the proper management of inventory items together with the reduction in various Lean wastes with the proper implementation of JIT in healthcare. The reduction in various associated wastes leads to cleaner surrounding and lesser environmental degradation.

This paper outlines the need of a robust JIT approach in the healthcare sector for quality services. The efficacy of JIT, AHP and BWM has been explored to find out critical elements for successful JIT implementation in health care.

The present environmental concern has enforced manufacturing firms to re-evaluate their business practices to reduce the adverse impact on the environment with optimum production. Therefore, the present study aims to deal with the implementation of the environmental lean six sigma (ELSS) framework in an Indian medical equipment manufacturing industry to obtain operational and environmental sustainability.

The proposed ELSS framework is based on the DMAIC (Define–Measure–Analyze–Improve–Control) methodology, sustainability and lean tools. The efficacy of the developed framework is tested through a case study in an Indian medical equipment manufacturing unit.

In the case company, four major issues were reported, i.e. improper material handling, non-productive movement of men and material, poor indoor air quality and rework. The ELSS project facilitates the case organization to enhance their capacity utilization from 59.25% to 74.3%, defects per million opportunities reduce from 309,523 to 48,951.44 and indoor air quality levels decrease from 156.87 to 86.85 µgm3.

The efficacy of the developed ELSS framework is evaluated through a case study conducted only in one Indian manufacturing environment.

The present study facilitates industrial managers and practitioners to achieve sustainability and process excellence through ELSS implementation. Organizations that intend to simultaneously improve operational and environmental performance can benefit from this paper as it can be used as a guide to conduct similar projects with both operational and environmental benefits.

This paper presents a successful implementation of the ELSS framework in the healthcare industry to achieve operational and environmental sustainability.