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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 explores the adoption of Industry 4.0 in developing countries' export industries, focusing on factors influencing this adoption, the moderating role of market pressure and prioritizing key factors for sustainable growth.

Based on the “TOE theory” this study has proposed a research framework to identify the factors influencing the adoption and sustainable implementation of Industry 4.0 in the export industry. This study has collected valid datasets from 387 export-oriented industries and applied SEM-ANN dual-stage hybrid model to capture linear and nonlinear interaction between variables.

Results revealed that Technical Capabilities, System Flexibility, Software Infrastructure, Human Resource Competency and Market pressure significantly influence the Adoption of Industry 4.0. Higher market pressure as a moderator also improves the Industry 4.0 adoption process. Results also pointed out that system flexibility is a gray area in Industry 4.0 adoption, which can be enhanced in the export industry to maintain a sustainable adoption and implementation of Industry 4.0.

Minute information is available on the factors influencing the adoption of Industry 4.0 in export-oriented industries. This study has empirically explored the role of influential factors in Industry 4.0 and ranked them based on their normalized importance.

Recently, the concept of the circular economy (CE) has witnessed significant momentum in academic and professional circles. However, there is a dearth of research that studies the enabling factors of the CE in the era of digital transformation. The existing research aimed to identify the impact of Industry 4.0 readiness on the CE in manufacturing firms operating in Jordan, as well as to identify the mediating role of the industrial Internet of things and big data analytics.

For this work objectives, 380 questionnaires were analyzed. Convergent validity and discriminant validity tests were performed through partial least squares-structural equation modelling (PLS-SEM) in the Smart-PLS programme. Data reliability was confirmed. A bootstrapping technique was used to analyze the data and then hypothesis testing was performed.

The results indicate that Industry 4.0 readiness, industrial Internet of things (IIoT) and big data analytics positively enable CE, also the IIoT and big data analytics positively mediate the nexus between Industry 4.0 readiness and CE.

This study promotes the idea of focusing on Industry 4.0 readiness to enhance CE in the Jordanian manufacturing sector and knowing the effect of IIoT and big data analytics in this relationship.

This research developed a theoretical model to understand how Industry 4.0 readiness might enhance the CE in manufacturing firms by invoking the IIoT and big data analytics as mediating constructs in the relationship between Industry 4.0 readiness and CE. This paper offers new theoretical and practical contributions that add value to industry 4.0 and CE literature by testing these constructs' mediation models in the manufacturing sector.

Industry 4.0 has received significant attention in today's competitive business market, necessitating a restructuring of functional domains in nearly every manufacturing organization. A comprehensive strategy to improve performance in preparation for Industry 4.0 implementation necessitates several steps, one of which is the establishment of performance outcomes (POs). The aim of this paper is to identify and rank the POs realized due to the adoption of Industry 4.0 enablers.

Based on an extensive literature review and inputs received from experts, a comprehensive list of enablers and the POs was prepared and finalized. This paper proposes a framework based on hybrid solution methodology, namely Neutrosophic Analytical Hierarchy Process (N-AHP) and Neutrosophic Combined Compromise Solution (N-CoCoSo), to rank the POs realized due to the adoption of Industry 4.0 enablers. The N-AHP methodology has been adopted to calculate the relative weights of the Industry 4.0 enablers. In comparison, the N-CoCoSo method has been adopted to rank the POs of Industry 4.0.

The proposed framework is applied to an Indian manufacturing organization to test the organization's practical applicability. Additionally, sensitivity analysis is also carried out to check the steadiness of the proposed framework. The findings of this study revealed that “Improved responsiveness to market conditions in today's competitive business environment” is the top-ranked PO of Industry 4.0, followed by “Enhanced competitiveness and better market share”, “Better product quality, through smart management of production process” and “Reduction in manufacturing waste and environmental sustainability” which could be realized due to adoption of its enablers.

This research would aid practitioners by enhancing the practitioners' capacity to understand and prioritize the various POs resulting from implementing Industry 4.0 enablers. Embracing a clear strategic plan will further assist practitioners in improving the efficiency of Industry 4.0 implementation.

Previous literature has only addressed the relationship between Industry 4.0 enablers and POs in a limited way. This paper attempts to compile a comprehensive list of Industry 4.0 enablers relevant to manufacturing organizations in order to fill this knowledge and research gap.

Industry 4.0 accelerates the performance of supply chains, in particular, the reduction in supply chain cost (SCC) and improvement in supply chain flexibility (SCF). The aim of this study is to examine the role of Industry 4.0 on SCC and SCF, using network theory to explain the interrelationships.

Data were collected from 182 manufacturing firms in Turkey. The partial least square structural equation modelling (PLS-SEM) was employed in testing the research hypotheses.

The results showed that Industry 4.0 positively affects SCC; however, no direct relationship was found between Industry 4.0 and SCF. Moreover, SCC was found to have a positive impact on SCF, while SCC was found to mediate the relationship between Industry 4.0 and SCF. An additional finding was that customer integration (CI) moderates the relationship between Industry 4.0 and SCC; however, CI does not moderate the relationship between Industry 4.0 and SCF.

The research validates the role of Industry 4.0 on supply chain processes and thus provides valuable insights into supply chain practitioners and decision-makers interested in Industry 4.0 for supply chain management.

In view of the limited number of studies, this study empirically contributes to the literature on the relationships among Industry 4.0, SCC, SCF and CI.

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 internationalization of business has grown the production value chains and created performance challenges for industrial production. Industry 4.0, the digital transformation of industrial processes, promises to deliver performance improvements through smart functionalities. This study investigates how digital transformation translates to performance gain by adopting a systems perspective to drive smartness.

This study uses qualitative research to collect data on the lived experiences of digital transformation practitioners for theory development. It uses semi-structured interviews with industry experts and applies the Gioia methodology for analysis.

The study determined that enterprise smartness is an organizational capability developed by digital transformation, it is a function of integration and the enabler of organizational performance gains in the Industry 4.0 context. The study determined that performance gains are experienced in productivity, sustainability, safety and customer experience, which represents performance metrics for Industry 4.0.

This study contributes a model that inserts smartness in the linkage between digital transformation and organizational outcomes to the digital transformation and production management literature.

The study indicates that digital transformation programs should focus on developing smartness rather than technology implementations, which must be considered an enabling activity.

Existing studies recognized the positive impact of technology on performance in industrial production. The study addresses a missing link in the Industry 4.0 value creation process. It adopts a systems perspective to establish the role of smartness in translating technology use to performance outcomes. Smart capabilities have been the critical missing link in the literature on harnessing digital transformation in organizations. The study advances theory development by contributing an Industry 4.0 value model that establishes a link between digital technologies, smartness and organizational performance.

This paper explores the relationships between Industry 4.0-driven technologies and the circular economy-driven business model (CEDBM) components of value creation, delivery and capture along manufacturing processes.

Based on the literature, a research model is developed in which the three CEBDM components are represented by five components: product service system (PSS), product design, industrial symbiosis (IS), consumer interaction and pay-per-use/rental. For each of these five components, enabling Industry 4.0 technologies are identified and vague interdependence relationships were assessed using a fuzzy decision-making trial and evaluation laboratory (DEMATEL) method.

This paper contributes to the literature by exploring the relationships of the CEDBM components of value creation, value delivery and value capture with Industry 4.0-driven technological enablers. In addition, causal relationships between Industry 4.0 technologies and their relevance for facilitating CE-enabled manufacturing processes are identified, and finally, Industry 4.0-driven technological enablers of CE are categorized as base and front-end technologies.

The findings suggest that value delivery-based differentiation provides new avenues for value creation and innovative forms of value capture in CEDBMs.

Practitioners can use the findings to develop a roadmap for Industry 4.0-driven technological solutions for CE.

CE-driven processes of manufacturing provide not only opportunities for value capture, creation and delivery but also avenues for customer-centric product and service development and effective resource utilization.

This paper is the first to identify value creation, delivery and capture processes along with Industry 4.0-enabled manufacturing processes.

The United Nation's Sustainable Development Goals (SDGs), introduced in 2015, connect several manufacturing strategies and promote sustainable practices in an organization. Manufacturing companies are struggling to meet changing market demands while also addressing social and biological issues. The current study aims to develop a framework that can assist practitioners and managers contribute to the attainment of the SDGs through the adoption of reconfigurable manufacturing system (RMS) practices and Industry 4.0 technologies.

An extensive literature review was carried out to identify RMS-Industry 4.0 practices and their interconnection, as well as their contribution to achieving the SDGs. The stepwise weight assessment ratio analysis (SWARA) method was then used to compute the weights of the selected RMS-Industry 4.0 practices, whereas the weighted aggregated sum product assessment (WASPAS) method was used to prioritize performance metrics. The developed framework's robustness was tested using a sensitivity analysis across five different organizations.

The findings show that advanced technologies practices have the most importance, followed by customization and rapid adjustment of capacity and functionality practices. The sensitivity analysis revealed the robustness of the developed framework as well as its adaptability among the chosen organizations.

This research will assist in the adoption of RMS and includes recent technologies that can help in the attainment of industrial SDGs. Managers will also be able to evaluate RMS in the context of industrial SDGs. Researchers and practitioners can now address the various RMS-Industry 4.0 practices while keeping the social and environmental aspects in mind.

No previous research has investigated the SDGs through the nexus effect of Industry 4.0 and RMS practices.

The purpose of this paper is to determine the factors that affect Industry 4.0 applications, the expected impacts of Industry 4.0 applications in companies and to analyze the importance of these factors and the importance of expected impacts correlatively.

This paper provides an empirical analysis of the factors affecting the adoption of Industry 4.0 transformation and its impacts on the companies. The paper is based on 103 valid answers to a questionnaire-survey distributed among companies in Turkey. The Pearson correlation analysis was conducted to determine the correlation between independent variables and dependent variables. Regression analyses were used to test the proposed hypotheses. A multiple regression analysis was used to investigate the causal relationship between independent and dependent variables. Linear regression method and stepwise regression method was employed for regression analyses. The factors that influence Industry 4.0 applications were determined as company size, technological level of products, budget allocation for R&D department, level of lean applications, level of agility/flexibility and level of automation; and the expected impacts of Industry 4.0 applications were determined as traceability of production processes, traceability of supply chain, flexibility of supply chains, communication between the partners of supply chain, productivity, real-time data analysis, integration between companies and integration in the company according to the literature review

The results of this research study revealed that, there is a stronger relationship between level of Industry 4.0 transformation and level of automation than there is between Industry 4.0 transformation and the other independent variables. From the analyses conducted, it can be stated that budget allocation for R&D and level of lean applications and level of automation had greater impacts on Industry 4.0 transformation than company size has. The independent variables included in the regression analysis had a positive effect on Industry 4.0 transformation of companies. However the effects of company size, technological level of products and level of agility/flexibility on Industry 4.0 transformation was weak. When the impacts of Industry 4.0 on companies were analyzed, it can be stated that there is a stronger relationship between Industry 4.0 transformation and real-time data analysis, traceability of production processes, integration in companies and productivity than there is between Industry 4.0 transformation and integration between companies, traceability of supply chains, flexibility of supply chains and communication between the partners of supply chain. It was determined that Industry 4.0 transformation generally impacts internal factors of company, while Industry 4.0 had limited impacts on the supply chains.

Although there are studies that separately investigated the factors affecting Industry 4.0 transformation and the impacts of Industry 4.0 transformation on companies, the present study provides important contributions to the literature in terms of considering the importance levels of the factors affecting Industry 4.0 transformation and the importance level of impacts of Industry 4.0 transformation on companies as a whole and in relation to each other.