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While traditional Industry 4.0 is studied in the context of smart factories, the authors study it as a metaphor that represents the spill-over effects of digitalisation, high-speed internet, cloud-based super-computing on industry, countries, human resource development and national competitiveness. This chapter analyses the Industry 4.0 steps taken by the United States, Germany, South Korea and India. It compares strategic actions taken by these countries using a strengths, weaknesses, opportunities, threats (SWOT) analysis to understand the position of each country. The authors use Max Weber’s ideal types as a positivist frame of analysis for the country-level data and from this draws policy recommendations. Based on the current status of India and other countries, the chapter concludes by suggesting short-term, mid-term and long-term strategies to transform India into a highly competitive industrialised economy in the context of the fourth industrial revolution.

The purpose of this study is to examine the key challenges currently prevalent in the Machinery and Equipment (M&E) sector of Malaysia and to offer an integrative Industry 4.0 strategic roadmap. The Environmental Scan 2016 and 2018 provides a basis for the identification of the challenges in the M&E sector of Malaysia. The study further investigates the challenges by analyzing the responses of four major stakeholders in a Focus Group Discussion. The findings reveal that the M&E sector suffers from very low automation adoption. This study is among the first few to analyze the challenges in the M&E sector and lay out a strategic roadmap encompassing the role of each stakeholder at every phase of the transition toward Industry 4.0. The proposed method of transitioning through targeted incentive schemes will help academics and practitioners in developing concrete and workable action plans to conduct the transition process.

This research aims to conduct, to the best of our knowledge, the first systematic review of the implementation of Industry 4.0 in BRICS. This review facilitates the identification of main factors that affect the readiness to adopt Industry 4.0 in BRICS and the role of different agents, such as multinationals, the public sector or educative institutions.

Key publications published from 2010 to 2019 have been analysed. A total of 61 papers have been selected from the systematic review.

Three factors of convergence of BRICS to developed economies in terms of Industry 4.0 are identified: (1) the public initiatives that can also result in the attraction of talent from developed countries to BRICS; (2) the role of multinationals and (3) the implication of educational institutions.

This review has some limitations. First, some grey literature, such as reports from non-governmental organisations and front-line practitioners' reflections, were not included. Second, only research studies in English were reviewed

The heterogeneity of BRICS amongst themselves affects the implementation of Industry 4.0 policies. Therefore, public policies should differ among countries to achieve the different readiness of companies within each country. Industry 4.0 cannot be understood as a manufacturing strategy against delocalisation, as emerging countries, such as BRICS, are also aware of the potential of automation.

Based on a systematic review, this article shows that the strategy created by Germany to increase industrial productivity has been also introduced in BRICS countries as a critical factor to improve their competitiveness.

Although the previous generations of the Industrial Revolution have brought many advantages to human life, scientists have been looking for a substantial breakthrough in creating technologies that can improve the quality of human life. Nowadays, we can make our factories smarter using new concepts and tools like real-time self-optimization. This study aims to take a step towards implementing key features of smart manufacturing including  preventive self-maintenance, self-scheduling and real-time decision-making.

A new bi-objective mathematical model based on Industry 4.0 to schedule received customer orders, which minimizes both the total earliness and tardiness of orders and the probability of machine failure in smart manufacturing, was presented. Moreover, four meta-heuristics, namely, the multi-objective Archimedes optimization algorithm (MOAOA), NSGA-III, multi-objective simulated annealing (MOSA) and hybrid multi-objective Archimedes optimization algorithm and non-dominated sorting genetic algorithm-III (HMOAOANSGA-III) were implemented to solve the problem. To compare the performance of meta-heuristics, some examples and metrics were presumed and solved by using the algorithms, and the performance and validation of meta-heuristics were analyzed.

The results of the procedure and a mathematical model based on Industry 4.0 policies showed that a machine performed the self-optimizing process of production scheduling and followed a preventive self-maintenance policy in real-time situations. The results of TOPSIS showed that the performances of the HMOAOANSGA-III were better in most problems. Moreover, the performance of the MOSA outweighed the performance of the MOAOA, NSGA-III and HMOAOANSGA-III if we only considered the computational times of algorithms. However, the convergence of solutions associated with the MOAOA and HMOAOANSGA-III was better than those of the NSGA-III and MOSA.

In this study, a scheduling model considering a kind of Industry 4.0 policy was defined, and a novel approach was presented, thereby performing the preventive self-maintenance and self-scheduling by every single machine. This new approach was introduced to integrate the order scheduling system using a real-time decision-making method. A new multi-objective meta-heuristic algorithm, namely, HMOAOANSGA-III, was proposed. Moreover, the crowding-distance-quality-based approach was presented to identify the best solution from the frontier, and in addition to improving the crowding-distance approach, the quality of the solutions was also considered.

This paper aims to investigate the adoption barriers of Industry 4.0 in the Indonesian manufacturing supply chains.

The mixed method was deployed to validate the findings. First, the qualitative study was conducted based on the interviews. Then, the companies were approached using filter questions on the involvement in adopting industry 4.0 and its impact on the supply chain.

Based on the qualitative study, nine main barriers were found in the thematic analysis. Thus, to get a consensus on the barriers in the industry, the barrier indicators were tested using a structural equation model retrieved from 173 small and medium Indonesian manufacturing firms. Results indicate that five main barriers (e.g. unclear Industry 4.0 policy, higher-risk investment, insecure data sharing, lack of expertise and lack of incentive) are confirmed as the adoption barriers.

The successful adoption of supply chain integration with Industry 4.0 technology can strengthen the manufacturing sector and competitiveness. Therefore, this study can be a complimentary assessment to evaluate the Indonesia Industry 4.0 Readiness Index (INDI 4.0) and the effectiveness of the government support program.

The results can be used as the framework to foresee the successful implementation of smart manufacturing supply chain management and its integration. Therefore, the authors proposed the framework to foresee the successful implementation of smart manufacturing, supply chain management and integration.

This paper aims to use text mining methods to explore the similarities and differences between countries’ national digital transformation (DT) and Industry 4.0 (I4.0) policies. The study examines the applicability of text mining as an alternative for comprehensive clustering of national I4.0 and DT strategies, encouraging policy researchers toward data science that can offer rapid policy analysis and benchmarking.

With an exploratory research approach, topic modeling, principal component analysis and unsupervised machine learning algorithms (k-means and hierarchical clustering) are used for clustering national I4.0 and DT strategies. This paper uses a corpus of policy documents and related scientific publications from several countries and integrate their science and technology performance. The paper also presents the positioning of Türkiye’s I4.0 and DT national policy as a case from a developing country context.

Text mining provides meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, aligned with their geographic, economic and political circumstances. Findings also shed light on the DT strategic landscape and the key themes spanning various policy dimensions. Drawing from the Turkish case, political options are discussed in the context of developing (follower) countries’ I4.0 and DT.

The paper reveals meaningful clustering results on similarities and differences between countries regarding their national I4.0 and DT policies, reflecting political proximities aligned with their geographic, economic and political circumstances. This can help policymakers to comparatively understand national DT and I4.0 policies and use this knowledge to reflect collaborative and competitive measures to their policies.

This paper provides a unique combined methodology for text mining-based policy analysis in the DT context, which has not been adopted. In an era where computational social science and machine learning have gained importance and adaptability to political and social science fields, and in the technology and innovation management discipline, clustering applications showed similar and different policy patterns in a timely and unbiased manner.

This chapter focuses on the junction of space and technology, place and context, on the one hand; and modern industrial systems, on the other hand; as well as the relevance of clusters and Industry 4.0. The authors will first briefly present the basics of cluster, as well as the fourth industrial revolution concepts. Then, the authors will speculate about the possible contribution of clusters to the development of Industry 4.0. This chapter demonstrates that the mechanisms and functionalities provided by clusters seem to be well aligned with the features of modern manufacturing, the industrial Internet and the integrated industry. Hence, it is reasonable to claim that clusters and Industry 4.0 are compatible, not contradictory, terms.

Food fraud as intentional deception for economic gain relies on a low-tech food value chain, that applies a ‘paper-and-pencil approach’, unable to provide reliable and trusted data about food products, accompanied processes/activities and actors involved. Such approach has created the information asymmetry that leads to erosion of stakeholders and consumers trust, which in turn discourages cooperation within the food chain by damaging its ability to decrease uncertainty and capability to provide authentic, nutritional, accessible and affordable food for all. Lack of holistic approach, focus on stand-alone measures, lack of proactive measures and undermined role of customers have been major factors behind weaknesses of current anti-fraud measures system. Thus, the process of strong and fast digitalisation enabled by the new emerging technology called Industry 4.0 is a way to provide a shift from food fraud detection to efficient prevention. Therefore, the objective of this chapter is to shed light on current challenges and opportunities associated with Industry 4.0 technology enablers' guardian role in food fraud prevention with the hope to inform future researchers, experts and decision-makers about opportunities opened up by transforming to new cyber-physical-social ecosystem, or better to say ‘self-thinking’ food value chain whose foundations are already under development. The systematic literature network analysis is applied to fulfil the stated objective. Digitalisation and Industry 4.0 can be used to develop a system that is cost effective and ensures data integrity and prevents tampering and single point failure through offering fault tolerance, immutability, trust, transparency and full traceability of the stored transaction records to all agri-food value chain partners. In addition, such approach lays a foundation for adopting new business models, strengthening food chain resilience, sustainability and innovation capacity.

The digital transformation under Industry 4.0 is complex and resource-intensive, making a strategic digitalization guideline vital to small and medium-sized enterprises' success in the Industry 4.0 transition. The present study aims to provide manufacturing small and medium-sized enterprises (SMEs) with a guideline for digital transformation success under Industry 4.0.

The study first performed a content-centric literature review to identify digital transformation success determinants. The study further implemented interpretive structural modeling to extract the order at which the success determinants should be present to facilitate the SMEs’ digital transformation success optimally. The interpretive model and interpretive logic knowledge base matrix were also used for developing the digital transformation guideline.

Eleven success determinants are vital to SMEs’ digital transformation efforts. For example, results revealed that external support for digitalization is the first step in ensuring digital transformation success among SMEs, while operations technology readiness is the most inaccessible success determinant.

The study highlights the degree of importance of the 11 success determinants identified, which magnifies each determinant's strategic priority based on its driving power and dependence power. Theorizing the dependent variable of “digital transformation success” and quantitatively measuring the extent to which each success determinant contributes to explaining “digital transformation success” offers an exciting opportunity for future research.

Digital transformation success phenomenon within the Industry 4.0 context is significantly different from the digitalization success concept within the traditional literature. The digital transformation under Industry 4.0 is immensely resource-intensive and complex. Smaller manufacturers must have specific capabilities such as change management and digitalization strategic planning capability to reach a certain degree of information, digital, operations and cyber maturity.

The digital transformation success guide developed in the study describes each success determinants' functionality in relation to other determinants and explains how they might contribute to the digital transformation success within the manufacturing sector. This guide enables smaller manufacturers to better understand the concept of manufacturing digital transformation under Industry 4.0 and devise robust strategies to steer their digital transformation process effectively.

The domains of Industry 4.0 and Smart Farming encompass the application of digitization, automation, and data-driven decision-making principles to revolutionize conventional sectors. The intersection of these two fields has numerous opportunities for industry, society, science, technology and research. Relatively, this intersection is new, and still, many grey areas need to be identified. This research is a step toward identifying research areas and current trends.

The present study examines prevailing research patterns and prospective research prospects within Industry 4.0 and Smart Farming. This is accomplished by utilizing the Latent Dirichlet Allocation (LDA) methodology applied to the data procured from the Scopus database.

By examining the available literature extensively, the researchers have successfully discovered and developed three separate research questions. The questions mentioned above were afterward examined with great attention to detail after using LDA on the dataset. The paper highlights a notable finding on the lack of existing scholarly research in the examined combined field. The existing database consists of a restricted collection of 51 scholarly papers. Nevertheless, the forthcoming terrain harbors immense possibilities for exploration and offers a plethora of prospects for additional investigation and cerebral evaluation.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

The paper proposes that the incorporation of Industry 4.0 technology into agricultural operations can enhance efficiency, production and sustainability. Furthermore, it highlights the significance of creating user-friendly solutions specifically tailored for farmers and companies. The study indicates that the implementation of supportive legislative frameworks, incentive programmes and resource conservation methods might encourage the adoption of smart agricultural technologies, resulting in the adoption of more sustainable practices.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

Based on a thorough examination of existing literature, it has been established that there is a lack of research specifically focusing on the convergence of Industry 4.0 and Smart Farming. However, notable progress has been achieved in the field of seclusion. To date, the provided dataset has not been subjected to analysis using the LDA technique by any researcher.