Search results

This chapter reflects on the understanding of the phenomenon known as Smart Industry, Industry 4.0, fourth industrial revolution, and many other labels. It does so by reflecting on the issue of terminology, as well as the existing diversity regarding the description of the phenomenon. The issue of meaning is addressed by assessing the results from Culot, Nassimbeni, Orzes, and Sartor (2020) and Habraken and Bondarouk (2019) which are, subsequently, used to develop a workable description. Findings from the two assessed studies raise the question of whether a workable construction of the phenomenon is to be understood as the key technologies or the distinctive developments? A question without a definitive answer, but I will present my view by taking inspiration from the manner in which the prior industrial revolutions are commonly understood. This leads to a, still multifaceted though, more focused understanding of the phenomenon. The insights, formulated proposition and developed model stemming from the reflection of terminology and meaning of the phenomenon helps move the current technology-related phenomenon forward. They assist with the establishment of well-documented papers. A critical aspect if we aim to understand how management will look like in the era of this phenomenon.

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.

Despite the fact that labels such as “smart industry” and “industry 4.0” (terms used to denote the fourth industrial revolution) have become popular topics within academia and in practice, their meaning remains an issue of concern. It’s a concern that has drawn the attention of various authors. It is a struggle we engaged in as well – specifically regarding the Dutch “smart industry” label – to aid our aim of assessing whether our call to combine forces can be extended beyond industry 4.0 and industrie 4.0. We provide here initial indications of whether there is more unity in meaning and, thus, reasons to take steps toward combining labels. By means of 20 interviews with Dutch smart industry experts, a representation of smart industry was obtained as understood in the Netherlands. Based on this representation, we examined the extent of overlap between the Dutch “smart industry” label and the general term “fourth industrial revolution” as well as the “industry 4.0” label as defined by various scholars. Our findings showed that smart industry in the Netherlands does not match the denotation of an industrial revolution. Several signals were, however, detected indicating that the content observed under the Dutch smart industry label overlaps with what is being presented under the label industry 4.0. These results reveal that there is indeed more unity in meaning between the various labels that exist and, as such, strengthens our call to combine forces.

The aim of this study is to analyze workforce attributes related to Industry 4.0 using fuzzy decision-making trial and evaluation laboratory (DEMATEL) and fuzzy combinative distance-based assessment (CODAS).

Technological trends stipulate various revolution in industries. Industry 4.0 is a vital challenge for modern manufacturing industries. Workforce adoption to such challenge is gaining vital importance. Therefore, such workforce-related attributes need to be identified for enhancing their performance in Industry 4.0 environment. In this context, this article highlights the analysis of 20 workforce attributes for Industry 4.0. Relevant criteria are prioritized using fuzzy DEMATEL. Workforce attributes are prioritized using fuzzy CODAS.

The key attributes are “Skills/training in decision-making (WA2)”, “Competences in complex system modelling and simulation (WA1)” and “Coding skills (WA20)”.

In the present study, 20 workforce attributes are being considered. In future, additional workforce attributes could be considered.

The study has been conducted based on inputs from industry experts. Hence, the inferences have practical relevance.

The analysis of workforce attributes for Industry 4.0 using MCDM methods is the original contribution of the authors.

The paper aims to present a systematic literature review to analyze interrelated enablers of Industry 4.0 for implementation. Industry 4.0 is an integrated manufacturing strategy embedded with disruptive technologies. Adapting these technologies with the present industrial scenario is dependent on understanding the dynamics of various critical enablers in the existing literature. In this paper, an effort has been taken to validate and reinforce these enablers by experts in the field of Industry 4.0 for implementation.

A mixed-methodology is designed in this paper. A text mining approach with an expert’s linguistic assessment method is planned to discover the enablers from literature 2010 to 2019. The most critical enablers and their dependencies on other enablers are studied by using correlation analysis.

The research explores the power driving enablers in three groups: technology, features and requirements for implementing Industry 4.0 in the existing factory. In each group, a high degree of associated and dependent enablers is fragmented in detail.

This paper will benefit the research communities and practitioners to understand the significance of an integrated ecosystem of Industry 4.0 technologies, features and requirements for implementation.

The text mining approach integrated with expert’s linguistic assessment to explore the pairwise relationship among the enablers using word correlation is a novel approach in this paper. Moreover, to best of the authors’ knowledge, this is the first-ever attempt to conduct a structured literature review combined with text analysis and linguistic assessment to identify the enablers of Industry 4.0 for implementation.

The purpose of this paper is to introduce, summarize and combine the results of 11 articles in a special issue on the implementation of Industry 4.0. Industry 4.0 emerged as a phenomenon about a decade ago. That is why, it is interesting now to explore the implementation of the concept. In doing so, four research questions are addressed: (1) What is Industry 4.0? (2) How to implement Industry 4.0? (3) How to assess the implementation status of Industry 4.0? (4) What is the current implementation status of Industry 4.0?

Subgroups of articles are formed, around one or more research questions involving the implementation of Industry 4.0. The articles are carefully analyzed to provide comprehensive answers.

By comparing definitions systematically, the authors show important aspects for defining Industry 4.0. The articles in the special issue explore several cases of manufacturing companies that implemented Industry 4.0. In addition, systematic approaches to aid implementation are described: an approach to combine case-study results to solve new implementation problems, approaches to assess readiness or maturity of companies regarding Industry 4.0 and surveys showing the status of implementation in larger samples of companies as well as showing relationships between company characteristics and type of implementation. Small and large firms differ considerably in their process of implementing Industry 4.0, for example.

This special issue discusses implementation of Industry 4.0. The issue is limited to 11 articles, each of which with its own strengths and limitations.

The practical relevance of the issue is that it focuses on the implementation of Industry 4.0. Cases showing successful implementation, measurement instruments to assess degree of implementation and advice how to build a database with cases together with large-scale studies on the state of implementation do provide a wealth of information with a large managerial relevance.

The paper introduces an original take on Industry 4.0 by focusing on implementation. The special issue contains both literature reviews, articles describing case studies of implementation, articles developing systematic measurement instruments to assess degree of implementation and some articles reporting large-scale studies on the state of implementation of Industry 4.0 and thereby combine several perspectives on implementation of Industry 4.0.

Recent research has shown that the implementation of Industry 4.0 requires companies to (re)adjust their human resource (HR) policies. This article focuses on the relationship between Industry 4.0 and three HR practices: i.e. employee involvement, job design and skill development.

We use data of the European Company Survey (ECS) (2019). This nationally representative survey in the EU28 gathers data on workplace practices among managers from establishments employing at least 10 employees. We focus on 5,609 establishments in the manufacturing sector.

Firstly, employee involvement shows a strong positive correlation with Industry 4.0, irrespective of the digital technology used, country or firm size. Secondly, weak but significant correlations are found with increasing job complexity and skill development.

Research should engage in fine-grained analyses of the alignment between particular digital technologies and their respective HR practices.

Our results stress the importance of involving employees during the implementation of Industry 4.0.

Despite frequent acknowledgement across Industry 4.0 roadmaps and maturity models, the predictions for HR practices are empirically incomplete and theoretically inconclusive.

The following is an introductory profile of the fastest growing firms over the three-year period of the study listed by corporate reputation ranking order. The business activities in which the firms are engaged are outlined to provide background information for the reader.

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.

Quality 4.0 is concerned with managing quality in the Industry 4.0 era. Specifically, its focus is on which digital tools are used to enhance an organization’s ability to reliably give customers high-quality products. The purpose of this paper is to investigate the key ingredients for the effective implementation of Quality 4.0.

A narrative literature review was conducted on the extant works to collate and analyse previous studies in this relatively new field.

The study revealed eight key ingredients for the effective implementation of Quality 4.0 in organizations, namely: (1) handling big data, (2) improving prescriptive analytics, (3) using Quality 4.0 for effective vertical, horizontal and end-to-end integration, (4) using Quality 4.0 for strategic advantage, (5) leadership in Quality 4.0, (6) training in Quality 4.0, (7) organizational culture for Quality 4.0 and, lastly, (8) top management support for Quality 4.0. These findings have provided a steer for the future research agenda of Quality 4.0.

Organizations can use the eight ingredients to perform a self-assessment on the current state of each element within their own organization. When implementing Quality 4.0, each ingredient should be effectively analysed, and measures taken so that the implementation of Quality 4.0 is effective.

The paper makes the first attempt to present the key ingredients an organization should possess to effectively implement Quality 4.0.