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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.

The purpose of this paper is to present a practical data-based framework for the prioritization of investment in manufacturing technologies, methods and tools, and to demonstrate its applicability and practical relevance through two case studies of manufacturing firms of different industrial segments.

The proposed framework is based on network theory applied on technology adoption. For this, the database of Industry 4.0 maturity assessments of SENAI was used to develop data visualization tools named “Technology Networks”. Thus, this study is descriptive research with correlational design. Besides, the framework was applied in two companies and semi-structured interviews were carried out with domain experts.

The technology networks highlight the technological adoption patterns of six industrial segments, by considering the answers of 863 Brazilian companies. In general, less sophisticated technologies were positioned in the center of the networks, which facilitates the visualization of adoption paths. Moreover, the networks presented a well-balanced adoption scenario of Industry 4.0 related technologies and lean manufacturing methods and tools.

Since the database was not built under an experimental design, it is not expected to make statistical inferences about the variables. Furthermore, the decision to use an available database prevented the editing or inclusion of technologies. Besides, it is estimated that the technology networks given have few years for obsolescence due to the fast pace of technological development.

The framework is a tool that may be used by practicing manufacturing managers and entrepreneurs for taking assertive decisions regarding the adoption of manufacturing technologies, methods and tools. The proposition of using network theory to support decision making on this topic may lead to further studies, developments and adaptations of the framework.

This paper addresses the topics of lean manufacturing and Industry 4.0 in an unprecedented way, by quantifying the adoption of its technologies, methods and tools and presenting it in network visualizations. The main value of this paper is the comprehensive framework that applies the technology networks for supporting decision making regarding technology adoption.

This study aims to investigate the motivations for the adoption of Industry 4.0 technologies among manufacturing firms in developing economies. Specifically, the effects of relative advantage of the technologies, competitive pressure, and government support on the adoption are explored. Moreover, the mediating role of top management support between environmental factors (government support and competitive pressure) and the adoption of Industry 4.0 technologies is examined.

A research model is developed based on the technology-organization-environment (TOE) framework strengthened by institutional theory. Structural equation modeling (SEM) approach is employed to evaluate the model using data obtained from 215 manufacturing firms through a cross-industry survey. Additionally, a post-hoc analysis is conducted using cluster analysis and ANOVA.

The results show that competitive pressure and government support significantly promote top management support, which in turn contributes to the adoption of Industry 4.0 technologies. Relative advantage of the technologies is not significantly related to the adoption.

This study does not explore the relationship between technology type and the specific needs of manufacturing firms. Future researchers can conduct a more comprehensive analysis by examining how different technology types align with the unique needs of individual companies.

The findings of this study have implications for both policymakers and managers. Policymakers can leverage these insights to understand the underlying motivations behind manufacturing firms' adoption of Industry 4.0 technologies and develop promoting policies. In turn, managers should keep an eye on government policies and utilize government support to facilitate technology adoption.

This study uncovers the underlying motivations—government support and competitive pressure—for the adoption of Industry 4.0 technologies among manufacturing firms in developing economies. Meanwhile, it complements previous research by showing the mediating role of top management support between environmental factors (government support and competitive pressure) and the adoption of Industry 4.0 technologies.

The purpose of this study is to identify the prominent research constituents in the domain of integration of Lean manufacturing and Industry 4.0 techniques and analyze the intellectual structure among them.

A bibliometric analysis of articles based on Donthu et al. (2021a) has been adopted to conduct a systematic review of the integration of Lean manufacturing and Industry 4.0 using the Scopus database.

The co-citation analysis and bibliographic coupling depicted three clusters and themes around which the research related to the integration of Lean manufacturing and Industry 4.0. Publications related to the topic have majorly focused on the development of conceptual models and frameworks for integrating Lean manufacturing and Industry 4.0, analyzing the compatibility between the two techniques for better implementation of one another and the techniques' combined impact on operational performance.

Most of the review studies related to the domain of integration of Lean manufacturing and Industry 4.0 have adopted a systematic literature review methodology. The present study has tried to infer the intellectual framework of the research being conducted in the said domain using the bibliometric analysis to identify the prominent research constituents in the field and examine the intellectual relationship between them.

Common availability of digital technologies encouraged companies in almost all industries to focus on exploring various ways of benefiting from their adoption and thus taking steps toward their digital transformation. This paper aims to describe the digital transformation of small and medium enterprises (SMEs) as a challenging opportunity and identify ways in which companies from the food industry address it.

The paper presents empirical evidence based on a case study of the Polish freeze-drying market and companies operating on it. This study adopted a single case study research method to describe the digital transformation journey of SMEs. The sample constitutes a single sectoral case study with more than one unit of analysis – sixteen companies. The undertaken approach follows an embedded case study design and allows for an extensive and multidimensional analysis of rich empirical data.

The results of this analysis allowed to identify four significant trends describing human resources involvement in the digital transformation of freeze-drying companies in Poland (i.e. visionary top-down, cooperative task-oriented, persuasive bottom-up, chaotic), a detailed catalog of outcomes of digital transformation from the perspective of food industry companies grouped in seven categories and a list of main barriers to digital transformation.

This paper contributes to expanding knowledge on the practices of food industry companies in addressing challenges posed by the development of information technology and the dynamically changing environment after the COVID-19 pandemic. It contributes further to the discussion related to context-, industry- and country-specific barriers to digital transformation, identifying time-related constraints as an essential barrier to digital transformation.