PurposeThis purpose of this study is to provide an overview of the current state of research on Lean Six Sigma (LSS) and Industry 4.0 and the key aspects of the relationships between them. The research analyses LSS's evolution and discusses the future role of LSS 4.0 in an increasingly digitalized world. We present the benefits and motivations of integrating LSS and Industry 4.0 as well as the critical success factors and challenges within this emerging area of research.Design/methodology/approachA systematic literature review methodology was established to identify, select and evaluate published research.FindingsThere is a synergistic nature between LSS and Industry 4.0. Companies having a strong LSS culture can ease the transition to Industry 4.0 while Industry 4.0 technologies can provide superior performance for companies who are using LSS methodology.Research limitations/implicationsOne limitation of this research was that as this area is a nascent area, the researchers were limited in their literature review and research. A more comprehensive longitudinal study would yield more data. There is an opportunity for further study and analysis.Practical implicationsThis study reviews the evolution of LSS and its integration with Industry 4.0. Organisations can use this study to understand the benefits and motivating factors for integrating LSS and Industry 4.0, the Critical Success Factors and challenges to such integration.Originality/valueThis is the first systematic literature review on LSS 4.0 and can provide insight for practitioners, organisations and future research directions.

The evolution and future of lean Six Sigma 4.0


Purpose
This paper aims to deal with the selection of the sustainability-oriented Green Lean Six Sigma (GLS) project for the manufacturing industry in the complex decision-making environment. Moreover, the study also proposes a GLS implementation framework for improved organizational performance.


Design/methodology/approach
GLS project selection has been done based on the six sustainability-oriented criteria formed from 17 sub-criteria (found from the literature and developed by authors). The weights of the criteria have been determined through the entropy method. The projects have been ranked based on the criteria through the advanced decision-making approach: grey relation analysis (GRA). The results of the study were validated using best worst method and sensitivity analysis.


Findings
It has been found that the productivity-related criterion is the most significant among other criteria with entropy weight of 0.2721. GRA has been used in this research work to rank the potential GLS projects in a manufacturing industry based on six sustainability criteria, to select a project that exhibits the maximum potential for sustainable improvement. The machine shop has been found as the most significant GLS project with grey relation grade of 0.4742.


Practical implications
The present study facilitates practitioners and industrial managers to implement an inclusive GLS approach for improved sustainability dynamics through effective GLS project selection and implementation framework.


Originality/value
With increased globalized competition in recent times, new projects are being considered as the foundation stone for organizational success. The decision-making becomes quite complex to select an effective project due to the intriguing nature of various criteria, sub-criteria and different aspects of sustainability. To the best of the authors’ knowledge, the present study is the first of its kind that provides ways for the selection of sustainability-oriented GLS projects.


Green lean six sigma sustainability – oriented project selection and implementation framework for manufacturing industry

PurposeThis research aims to propose a framework to integrate Green Lean Six Sigma (GLSS) and Industry 4.0 to improve organizational sustainability.Design/methodology/approachThe integration of GLSS and Industry 4.0 is proposed based on theoretical facets of the individual approaches. A generic, conceptual framework of an integrated GLSS-Industry 4.0 approach is then proposed using the application of different tools and techniques of GLSS and Industry 4.0 at different stages of the realization of a project.FindingsBoth approaches have common facets related to enablers and barriers, and the integrated application of tools and techniques of each approach supplements the common focus of both related to sustainability enhancement. The proposed, conceptual framework provides systematic guidelines from the project selection stage to the sustainment of the solution, with the enumerated application of different techniques and tools at each step of the framework.Originality/valueThis research is the first of its kind to propose the integration of GLSS and Industry 4.0 under the umbrella of a unified approach, including a conceptual framework of this integrated GLSS-Industry 4.0 approach.

Integrating Green Lean Six Sigma and industry 4.0: a conceptual framework

PurposeThis study investigates the impact of big data analytics capabilities on green supply chain performance. Moreover, it assesses the mediating effect of the green innovation and moderating effect of technological intensity.Design/methodology/approachThis study is based on primary data that were collected from the food and beverages manufacturing sector operating in Jordan. A total of 420 samples were used for the final data analysis. Data analysis was performed via structural equation modeling (SEM) using SmartPLS 3.3.9.FindingsThe results of the data analysis supported a positive relationship between big data analytics capabilities and the green supply chain performance as well as a mediating effect of green innovation. It was confirmed that technological intensity moderated the relationship of green innovation on green supply chain performance.Research limitations/implicationsThe study faced many limitations such as the method of collecting primary data, which relied on a questionnaire only and the use of cross-sectional data, as well as studying one context and in one country.Practical implicationsThe findings can guide managers and policymakers in the Jordanian food and beverage manufacturing sector on how to manage organizational capabilities related to big data analytics to enhance green supply chain performance and improve green innovation in these firms.Originality/valueThis study developed a theoretical and empirical model to investigate the relationship between big data analytics capabilities, green innovation, technological intensity and green supply chain performance. This study offers new theoretical and managerial contributions that add value to the supply chain management and innovation literature by testing the moderated mediation model of these constructs in the food and beverages manufacturing sector in Jordan.

Big data analytics capabilities and green supply chain performance: investigating the moderated mediation model for green innovation and technological intensity

In light of global environmental concerns growing, environmental awareness within firms has become more important than before, and many scholars and researchers have argued the importance of environmental management in promoting sustainable organizational performance, especially in the context of supply chains. Thus, the current study aimed at identifying the impact of the components of green intellectual capital (green human capital, green structural capital, green relational capital) on green supply chain performance in the manufacturing sector in Jordan, as well as identifying the moderating role of big data analytics capabilities. To achieve this aim, we developed a conceptual model of Structural Equation Modelling-Partial Least squares and tested through the Smart-PLS software on a sample of 438 respondents. Empirical results showed that each of the components of green intellectual capital and big data analytics explains 71.1% of the variance in green supply chain performance and that all components of green intellectual capital have a statistically significant impact on green supply chain performance. The results also revealed that the relationship between green relational capital and green supply chain performance is moderated through big data analytics capabilities. Finally, this study made a theoretical and managerial implications to the supply chain literature and industry.

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Green Intellectual Capital and Green Supply Chain Performance: Does Big Data Analytics Capabilities Matter?

The advent of new technologies alongside the generation of the vast amount of data in the manufacturing processes makes Green Lean Six Sigma (GLSS) approaches very challenging. This paper presents ...

A Big Data Analytics-driven Lean Six Sigma framework for enhanced green performance: a case study of chemical company

COVID-19 pandemic has heavily affected worldwide. One of the major industrial sectors impacted by COVID-19 virus is the travel and hospitality, and it has resulted in extreme level of unemployment in travel and tourism industries especially hotels, restaurant chains, street food caterers and bars. Cancellations of events, conferences, conventions and sports leagues are instantaneously causing severe impact on the travel and tourism and hospitality industry. The unemployed workforce is facing numerous mental health challenges in the form of depression, anxiety, hopelessness, lack of confidence and loss of interest in life which are leading them to maladaptive coping mechanisms like suicide, alcohol and substance abuse. This chapter discusses the following topics:

M. Dileep Kumar

Papers

A Big Data Analytics-driven Lean Six Sigma framework for enhanced green performance: a case study of chemical company

COVID-19 Impact on Leisure and Travel Industry: Psychological, Safety and Governance Imperatives