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Industry 4.0 has an inimitable potential to create competitive advantages for the apparel industry by enhancing productivity, production, profitability, efficiency and effectiveness. This study, thus, aims to assess the digitalisation level of the Tanzanian apparel industry through the Industry 4.0 perspectives.

A mixed-methods-based approach was deployed. This study deployed semi-structured interviews, document review and observation methods for the qualitative approach. For the quantitative approach, closed-ended questionnaires were used to ascertain the digitalisation levels and maturity level of the textiles and apparel (T&A) factories and small and medium-sized textile enterprises in Tanzania. The sample size was 110, with participants engaged through the purposive sampling technique.

Industry 4.0 frameworks evolved into practices mainly since 2011 in several service and manufacturing industries globally. For Tanzania, the findings indicate that the overall maturity level of the T&A industries is 2.5 out of 5.0, demonstrating a medium level of adoption. Thus, the apparel industries are not operating under the industry 4.0 framework; they are operating within the third industrial revolution – Industry 3.0 – framework. For such industries to operate within the fourth industrial revolution – Industry 4.0 – that is only possible if there is significantly well-developed industrial infrastructure, availability of engineering talent, stable commercial partnerships, demand from the marketplace and transactional relationship with customers.

This study’s limitations include: firstly, Industry 4.0 is an emerging area; this resulted in limited theoretical underpinnings in the Tanzanian perspectives. Secondly, the studied industries may not suffice the need to generalise the findings for the entire country, thus needing another study.

Although Industry 4.0 conceptual frameworks have been on trial in several industries since 2011, this is amongst the first empirical research on Industry 4.0 in the Tanzanian apparel industry that assesses the digitalisation levels.

Maintenance represents an indispensable role in the productive sector of the steel industry. The increasing use of operating with a high level of precision makes hydraulic systems one of the issues that require a high level of attention. This study aims to explore an empirical investigation for decreasing the occurrences of corrective maintenance of hydraulic systems in the context of Lean 4.0.

The maintenance model is developed based on action-research methodology through an empirical investigation, with nine stages. This approach aims to build a scenario to analyze and interpret the occurrences, seeking to implement and evaluate the actions to be performed. The undertaken initiatives demonstrate that this approach can be applied to optimize the maintenance of an organization.

The main contribution of this paper is to demonstrate that the applied method allows the overviewing results, with a qualitative approach concerning the maintenance actions and management processes to be considered, allowing a holistic understanding and contributing to the current literature. The results also indicated that Lean 4.0 has direct and mediating effects on maintenance performance.

This research intends to propose an evaluation framework with an interdimensional linkage between action research methodology and Lean 4.0, to explore an empirical investigation and contributing to understanding the actions to reduce the occurrences of hydraulic systems corrective maintenance in a production line in the steel industry.

This research provides the essential aspects of the transition from traditional quality methods like total quality management, quality assurance and quality control to a new quality approach linked with the Industry 4.0 era. The purpose of this paper is to address this issue.

The study used a survey method to obtain the practitioners' perceptions of the Quality 4.0 (Q4.0) concepts. Both closed-ended and open-ended structured questionnaires assessed the perceptions of respondents regarding manufacturers' readiness and Q4.0 awareness to transition to Q4.0. Non-probability and purposive sampling tactics selected 15 Tanzanian manufacturing industries (TMIs). Garnered data were scrutinised quantitatively and qualitatively utilising Minitab® 20, SmartPLS 3.3.7 and MAXQADA 2020 software packages.

The results indicate that TMIs are equipped to deploy the Q4.0 approach because industrialists are familiar with the concept's characteristics and dimensions and the benefits of implementing Q4.0. Most TMIs utilise a Q3.0 method for managing quality, while some manufacturing industries have begun to apply Q4.0 leveraging technologies. The study revealed several factors influencing Q4.0 readiness in TMIs, including leveraged technology adoption, training, Q4.0 skills, infrastructures, the government set-up, top management support, Q4.0 strategy and vision, collaboration, awareness, knowledge of Q4.0, customer and supplier centeredness and organisational culture.

The implication of this study is on Q4.0 awareness creation so that industries can grab the advantages of Q4.0 leveraged technologies. Another implication is that organisational readiness factors identified in this study are critical for the effective adoption of Q4.0. The highlighted influences may be utilised as indications to determine an organisation's readiness to transition to the Q4.0 approach. This research was limited to TMIs, excluding service firms, mining, and the building and construction industry due to differences in their mode of operation.

Determining readiness factors and awareness for the Q4.0 study is probably the first amongst the seven East African countries, including Tanzania. This study thus bridges a huge gap in fulfilling the need of this research type.

In recent times, reverse logistics (RL) is gaining significant traction in various automobile industries to recapture returned vehicles’ value. A good RL program can lower manufacturing costs, establish a green supply chain, enhance customer satisfaction and provide a competitive advantage. However, reducing disruptions and increasing operational efficiency in the automobile RL requires implementing innovative technology to improve information flow and security. Thus, this manuscript aims to examine the hurdles in automobile RL activities and how they can be effectively tackled by blockchain technology (BCT). Merging BCT and RL provides the entire automobile industry a chance to generate value for its consumers through effective vehicle return policies, manufacturing cost reduction, maintenance records tracking, administration of vehicle information and a clear payment record of insurance contracts.

This research is presented in three stages to accomplish the task. First, previous literature and experts' opinions are examined to highlight certain factors that are an aggravation to BCT implementation. Next, this study proposed an interval-valued intuitionistic fuzzy set (IVIFS) – decision-making trial and evaluation laboratory (DEMATEL) with Choquet integral framework for computing and analyzing the comparative results of factor interrelationships. Finally, the causal outline diagrams are plotted to determine the influence of factors on one another for BCT implementation in automobile RL.

This study has categorized the barriers to BCT implementation into five major factors – operational and strategical, technical, knowledge and behavioral, financial and infrastructural, and government rules and regulations. The results revealed that disreputable technology, low-bearing capacity of IT systems and operational inefficiency are the most significant factors to be dealt with by automobile industry professionals for finer and enhanced RL processes utilizing BCT. The most noticeable advantage of BCT is its enormous amount of data, permitting automobile RL to develop client experience through real-time data insights.

This study reveals several factors that are hindering the implementation of BCT in RL activities of the automobile industry. The results can assist experts and policymakers improve their existing decision-making systems while making an effort to implement BCT into the automobile industry's RL activities.

Although there are several studies on the benefits of BCT in RL and the adoption of BCT in the automobile industry, individually, none have explicated the use of BCT in automobile RL. This is also the first kind of study that has used IVIFS-DEMATEL with the Choquet integral framework for computing and analyzing the comparative results of factor interrelationships hindering BCT implementation in automobile RL activities.

This study aims to evaluate the potential of using the components of the quadruple helix and quintuple helix models, which are extensions of the triple helix university-private sector-public sector cooperation model. Thus, the triple helix model shaped by university-private-public sector cooperation has transformed into a quadruple helix innovation model with the inclusion of the media and culture-oriented public helix. In this context, while the triple helix emphasizes tripartite networks and hybrid organizations, the quadruple helix system focuses on intertwined collaborations, coevolution, and specialization within the framework of firms, institutions, and stakeholders. In the quadruple helix innovation system, the coevolution of art and innovation has assumed a central role in knowledge generation and innovation. In the quintuple helix innovation model, the natural environment of society is added to the quadruple helix. This study consists of three parts. In the first part, the literature on triple helix, quadruple helix, and quintuple helix models is reviewed. In the second part, digital transformation and technological innovations from Industrial Revolution 1.0 to Industry 5.0 are analyzed. In the third section, the contribution of the quintuple helix model to Industry 5.0 and Society 5.0 is explained.

Quality 4.0 represents the integration of quality management principles with digital technologies to drive continuous improvement and innovation in organizations. The purpose of this paper is to explore the essential organizational variables (OVs) for the successful implementation of Quality 4.0 in the Indian furniture industry.

Through a broad literature review, data from the Indian furniture industry and experts’ judgments a list of nineteen OVs have been recognized and classified into four major categories of digitalization, design, continuous improvement and employee training and up-skilling. The analytic hierarchy process (AHP) has been used to give comparative importance and prioritize the identified nineteen OVs of Quality 4.0 in the context of the Indian furniture industry.

The results of this study reveal that the identified variables are very important for successful Quality 4.0 implementation and have been supported by empirical evidence from the Indian furniture industry. The variable “automation” under the digitalization-related category is a significant variable having a maximum weightage of 26.8% followed by Cloud computing (DI4) having a global weight of 12.8%.

In addition to offering valuable insights and practical recommendations, the study recognizes a few limitations, such as industry-specific and the limited sample size. To diminish these limitations, future research should believe in conducting similar studies in different industries and extend the scope of the study.

Quality 4.0 is a term that refers to the integration of advanced digital technologies and smart data analytics into quality management systems to implement it considering OVs.

This chapter investigates a new human-centric perspective oriented to consider society as a whole, by following a systemic vision, where social well-being is important: Industry 5.0. The analysis is based on the great advantages for the ecosystem, going beyond the custom of affirming technological development and managerial views, favoring the dialog between the actors of the system, the adaptation of technologies to the context in which one operates, choosing the most appropriate business strategies, sustaining smart, sustainable and inclusive solutions in the innovation process. In this sense, big data, internet of things, augmented realities, or robotics are all technologies to increase productivity and improve working conditions but, at the same time, they have to improve social life. To overcome the main related challenges and support the transition, new insights to design innovative strategies urge. Our research aims to explore the scientific literature about Industry 5.0 and its development in the European Union. We carried out a systematic review and a bibliometric analysis of the scientific production through the Web of Science (WoS) and SCOPUS databases. In addition, our study analyzes how the proposed topic has evolved over time by collecting contributions from different countries and authors. We adopted an innovative tool that goes beyond the traditional statistical methods generally used in bibliometric research, and it allowed us to obtain more advanced results. Our software permitted us to join the findings of two databases simultaneously, such as WoS and SCOPUS, to generate a bibliometric analysis and identify new paradigm trends.

The purpose of this study is to explore the relationship between Lean manufacturing and Industry 4.0 for small and medium size of enterprise in Japan and Brazil.

The authors conducted a quantitative survey (20 companies in Japan and 30 companies in Brazil) combined with a qualitative interview (2 companies in Japan and 15 companies in Brazil).

According to the quantitative study, 90% of them practice Lean manufacturing and 40% of them practice Industry 4.0. In the qualitative study in Brazil, four managers responded that the Lean manufacturing is a prerequisite for Industry 4.0 since any production process with waste cannot be productive, even with sophisticated digitalization technology.

The authors explored further the relationship between “defensive Digital Transformation (DX),” which is based mainly on Lean manufacturing, and “offensive DX,” which relates to customer value creation through Industry 4.0. This study clarifies the relationship and plays as a roadmap to develop better the manufacturing from current status to the vision of Industry 4.0.

This paper aims at identifying the key factors influencing the adoption intention of data analytics for predictive maintenance (PdM) from the lens of the Technology–Organization–Environment (TOE) framework in the Singapore Process Industries context. The research model aids practitioners and researchers in developing a holistic maintenance strategy for large-scale asset-heavy process industries.

The TOE framework has been used in this study to consider a wide set of TOE factors and develop a research model with the support of literature. A survey is undertaken and the structural equation modelling (SEM) technique is adopted to test the hypotheses of the proposed model.

This research highlights the significant roles of digital infrastructure readiness, security and privacy, top management support, organizational competence, partnership with external consultants and government support in influencing adoption intention of data analytics for PdM. Perceived challenges related to organizational restructuring and process automation are not found significant in influencing the adoption intention.

This paper reports valuable insights on adoption intention of data analytics for PdM with relevant implications for the various stakeholders such as the leaders and senior managers of process manufacturing industry companies, government agencies, technology consultants and service providers.

This research uniquely validates the model for the adoption of data analytics for PdM in the process industries using the TOE framework. It reveals the significant technology, organizational and environmental factors influencing the adoption intention and highlights the relevant insights and implications for stakeholders.

The set of 2,509 documents related to the human-centric aspect of manufacturing were retrieved from Scopus database and systmatically analyzed. Using an unsupervised machine learning approach based on Latent Dirichlet Allocation we were able to identify latent topics related to human-centric aspect of Industry 5.0.

This study aims to create a scientific map of the human-centric aspect of manufacturing and thus provide a systematic framework for further research development of Industry 5.0.

In this study a 140 unique research topics were identified, 19 of which had sufficient research impact and research interest so that we could mark them as the most significant. In addition to the most significant topics, this study contains a detailed analysis of their development and points out their connections.

Industry 5.0 has three pillars – human-centric, sustainable, and resilient. The sustainable and resilient aspect of manufacturing has been the subject of many studies in the past. The human-centric aspect of such a systematic description and deep analysis of latent topics is currently just passing through.