Industry 4.0: Emerging themes and future research avenues using a text mining approach

Social media research: We are publishing more but with weak influence

The purpose of this paper is to address the chasm between academic research on social media as an expanding academic discipline and at the same time a growing marketing function. A bibliometric analysis indicated the evolution of academic research on social media. The results of a survey of 280 social media practitioners shed the light on the gap between academic social media research and the practice of professionals. A qualitative study also offered novel insights and recommendations for future developments in academic research on social media. The findings of this paper showed that academic research on social media is growing in terms of the number of publications but is struggling in three areas: visibility, relevance, and influence on practitioners. This study contributes to the body of knowledge on social media. The implications of our study are derived from the importance of our findings on the directions to publish more relevant and timely academic research on social media. While extensive studies exist on social media, their influence on practitioners is still limited.

A bibliometric analysis of the first 20 years of the Journal of Corporate Real Estate

Purpose

Journal of Corporate Real Estate (JCRE) is an international journal of repute. JCRE is an interdisciplinary journal focusing on corporate real estate management and caters to the needs of real estate owners and end-users. The journal's scope includes private and public sector corporate real estate owned and used by businesses to run their operations. This paper aims to analyze the journal's impact using bibliometric analysis from 1998 to 2017 on 380 publications.

Design/methodology/approach

The network analysis is performed using VOSviewer software and Biblioshiny R studio. The mapping presents citation structures, coauthorship patterns, bibliographic coupling and other network analyses.

Findings

The results show that the journal is growing in terms of citations and impact. The findings document various mapping criteria and their summaries. Seven major clusters are identified to delineate the underlying themes of publications in JCRE.

Originality/value

To the best of the authors’ knowledge, this paper offers the original and first bibliometric analysis of the journal’s impact over the first two decades. The findings are relevant for researchers and real estate stakeholders.

Dyadic, network and ecosystem alliances: systematic literature review and research agenda

Purpose

Literature on strategic alliances (SAs) is large, diverse and growing. It needs synthesis and analysis for application and further research. Authors have also underlined the need for studies covering differences in structuring, situational application and management of alliances. This systematic literature review aims to cover dyadic, network and ecosystem (DNE) alliances with theory, context, characteristics and methodology (TCCM) methodology covering the life cycle stages of an alliance (pre-formation, formation and management) to uncover insights which inform practice and guide further research on this important subject.

Design/methodology/approach

This paper follows systematic literature review (SLR) methodology for research design and article selection and TCCM methodology for analysis. It also analyses the literature on DNE alliances using a nine-box matrix with DNE alliance forming one axis and three alliance lifecycle stages of pre-formation (alliance objectives), formation (alliance design and operationalisation) and post formation (alliance management) stages along the other axis.

Findings

Analysis indicates focus on individual firms and their own customer value proposition (CVP) in the dyadic and network alliances. Industries with fast-paced technological change benefit from loosely coupled alliances. Social context and social exchanges leading to collaboration and collective strengths mark network alliances, with a focus on knowledge creation and dissemination. Ecosystems focus on a collective CVP, which guides alliance behaviour. Ecosystem leadership guides this purpose through governance for sustained competitiveness. Negative consequences (dark side) of alliances can be mitigated by careful design, formation and management of DNE alliances.

Research limitations/implications

While literature has focussed on static view of alliances in their industry or social context, this literature review analyses alliances along the dyadic, network and ecosystem typology, thus providing a new lens to study alliances. The review also recognises that alliances evolve over their life-cycle stages and observes their progression through their lifecycle stages aids for fuller comprehension of their behaviour. Areas for future research in structuring, leadership, value co-creation and technological change set an agenda for future research.

Practical implications

Dynamic managerial capabilities are drivers of alliance performance. The analysis using the nine-box matrix allows managers to better appreciate the options that exist and the consequences of their actions. Situating their alliance in the appropriate box can allow managers better plan, operationalise and manage their own alliances.

Originality/value

This systematic review compares and contrasts DNE alliances in extant literature and through their lifecycle stages. Insights from TCCM analysis and the nine-box matrix provide unique contribution to both theory and practice in this large and growing domain.

AR-AI Tools as a Response to High Employee Turnover and Shortages in Manufacturing during Regular, Pandemic, and War Times

The world faces the continuously increasing issue of a lack of skilled employees, staff migration, and turnover. It is strengthened by unexpected situations such as wars, pandemics, and other civilization crises. Solutions are sought and researched in various branches of industry and academia, including engineering, social sciences, management, and political and computer sciences. From the viewpoint of this paper, this is a side topic of Industry 4.0 and, more specifically, sustainability in working environments, and the issue is related to production employees who perform manual operations. Some of the tasks cannot be carried out under robotization or automation; therefore, novel human-work support tools are expected. This paper presents such highly demanded support tools related to augmented reality (AR) and artificial intelligence (AI). First, a panoramic literature review is given. Secondly, the authors explain the main objective of the presented contribution. Then the authors’ achievements are described—the R&D focus on such solutions and the introduction of the developed tools that are based on AR and AI. Benefits connected to the AR-AI technology applications are presented in terms of both time savings with the tool usage and job simplification, enabling inexperienced, unskilled, or less skilled employees to perform the work in the selected manual production processes.

Identifying the Directions of Technology-Driven Government Innovation

The world is now strengthening its Information and Communication Technology (ICT) capabilities to secure economic growth and national competitiveness. The role of ICT is important for problems like COVID-19. ICT based innovation is effective in responding to problems for industry, economy, and society. However, we need to understand, not from the perspective of performance or investment, that the use and performance of ICT technology are promoted when each country’s ICT related environment, policies, governance, and regulations are effective. We need to share sustainable ICT experiences, successes, and challenges to solve complex problems and reorganize policies. This study proposes a Text Mining methodology from a future-oriented perspective to extract semantic system patterns from International Telecommunication Union (ITU) professional reports. In the text extracted from the report, we found a new relationship pattern and a potential topic. The research results provide insights into a diverse perspective for policymakers to search for successful ICT strategies.

Threats and Opportunities in Digital Transformation in SMEs from the Perspective of Sustainability: A Case Study in the Czech Republic

Digital technologies represent an important factor in the achievement of the sustainability goals of the European Green Deal. Whether the digitalisation trend will contribute to the sustainable development of societies in the long term depends on how we shape and understand it. The aim of this paper is to create a concept for the management of the threats and opportunities in the digital transformation of SMEs. As part of a broader research project, a complex tool was developed to assess the digital maturity of SMEs, and our paper focuses on one aspect of this tool, which is related to the identification and management of the opportunities and threats that are associated with digital transformation trends. The methodology that was used to develop this paper is based on a pilot survey that involved 154 SMEs in the Czech Republic, 74 of which were used for the follow-up survey on the opportunities and threats. Sixteen key trends were identified, which were then reduced to five latent factors, with their names forming five dimensions. In the cluster analysis, the companies were divided into three clusters, which served as the basis for determining the final cluster centres. The result is a concept for the management of the threats and opportunities in the digital transformation of SMEs. Digital transformation is the broad topic. Therefore, the theoretical overlap of this study can be seen in the narrower focus on the identification and management of the opportunities and threats in digital transformation. In addition, this study focuses on SMEs because there are more than one million small and medium-sized enterprises in the Czech Republic, and they create 54% of the innovative solutions. The practical impact of this study can be understood in the fact that the proposed concept may enable SMEs to more easily manage threats and opportunities in their digital transformation.

Analyzing global inequality in access to energy: Developing policy framework by inequality decomposition

Energy poverty is a critical policymaking problem in the world, while the outlined solutions in academic and policy literature talks about the solutions, without addressing the possible cause of the problem. The interaction between labor and energy market might pave a way to address the issue. Within the context of energy poverty, this interaction might turn out to be a major roadblock in the way to attain the objectives of Sustainable Development Goals (SDGs). From this perspective, this study aims at analyzing the constituents of inequality in access to energy, and in that pursuit, it has employed Kaya-Theil Decomposition method. The study is carried out at the global level over the period of 1990-2019. The study outcomes demonstrate all the inequality components to be rising during the study period. Presence of a possible feedback loop in the association might create the Vicious Circle of Energy Poverty around the globe. This study contributes to the literature by addressing the demand-side dimension of the energy poverty issue, while using the Kaya-Theil Decomposition method as an estimator of demand-side factors. Based on the study outcomes, a policy framework has been recommended, and it is aimed at helping the nations to achieve the objectives of SDG 7, SDG 8, and SDG 10.

Factors Influencing Digital Innovation Strategy in Organizations

Prior research has shown that digitalization is found to deliver a source of investigation for presenting organizations to redesign their business model in order to align their strategy towards digital transformation. This study examines the factors influencing digital innovation strategy in organizations and a model for digital innovation strategy in organizations is also developed . Specifically, drawing on data from 450 respondents, the researchers propose that the following variables: organizational IT application portfolio, organizational culture, organizational structure, organizational dynamic capabilities, leadership and ethics predict innovation and strategy in organizations. The researchers found that organizational culture variable contribution was the highest by collectively predicting 78.1% for digital innovation & strategy in organizations. Overall, this study contributes to the literature by providing a model for developing digital innovation strategy in organizations.

Disentangling the link between ICT and Industry 4.0: impacts on knowledge-related performance

Purpose

Industry 4.0 technologies are promising to increase manufacturing companies' performance through the new knowledge that such digital technologies allow to create and manage within the firm boundaries and through customer interactions. Despite the great attention on the Industry 4.0 adoption paths, little is known about the relationships with previous waves of digital technologies, namely, information and communication technologies (ICTs), and how different groups of both types of technologies link to knowledge and its related performances.

Design/methodology/approach

The study employed a quantitative research design using a survey method. Submitting the questionnaire to entrepreneurs, chief operation officers or managers in charge of the operational and technological processes of Italian manufacturing firms, 206 respondents stated that their firm has adopted at least one of the seven Industry 4.0 technologies investigated.

Findings

The findings of the study highlight the positive relationship between ICT and Industry 4.0 technologies in terms of both intensity and groups of technologies (Web-based, Management and Manufacturing ICT; Operation, Customization and Data-processing 4.0), and how technologies affect knowledge-related performances in terms of products and processes, job-learning, product-related services and customer involvement.

Originality/value

This study is one of the first attempts to link groups of ICT to groups of Industry 4.0 technologies and to explore the effects in terms of knowledge-related performances as a measure of technology use. The study shows strong path dependency among ICT, Industry 4.0 and knowledge performance, enriching the literature on technological innovation and knowledge management.

The road towards industry 4.0: a comparative study of the state-of-the-art in the Italian manufacturing industry

Purpose

This paper has two objectives: first, to investigate the state-of-the-art of Industry 4.0 (I4.0) adoption in Italian manufacturing firms and, second, to understand variations in technologies implemented and business functions involved, benefits perceived, and obstacles encountered in I4.0 implementation over a three-year period.

Design/methodology/approach

The approach adopted in this research is descriptive, nesting longitudinal features. The paper presents a descriptive survey of 102 Italian manufacturing companies. The authors also evaluated non-response biases. The longitudinal approach was achieved by comparing the responses of the 40 sub-samples in common with a second similar survey launched three years prior, which aimed to identify patterns of evolution in the adoption of the I4.0 paradigm.

Findings

Survey findings demonstrate that Italian manufacturing companies still have limited awareness of I4.0 technologies, and the adoption of I4.0 technologies differs per technology. Company size and information system coverage level are the two factors that impact the company's technology adoption level. The comparative study shows that knowledge and adoption increase in a three-year interval with an unbalanced involvement of business functions regarding the I4.0 transformation. Indeed, companies are still seeking I4.0 solutions to reduce costs and lead times primarily, and the benefits perceived by companies are shown to be related to the number of I4.0 technologies in use. Finally, when companies put the I4.0 technologies into practice, competence is constantly considered the most significant barrier.

Research limitations/implications

This paper aims at conducting a thorough investigation into the development of I4.0 adoption in manufacturing companies. The main limitation of this study concerns the limited number of subjects involved in the longitudinal study (40) and the focus on a limited geographical area (Italy). In addition, more I4.0 technologies could also be incorporated into the survey protocol to gain further insight into I4.0 development.

Originality/value

The authors provide one of the first attempts to assess the variations of I4.0 implementation concerning technology adoption, business function involvement, and the alteration of benefits and obstacles. Several studies presented in the literature highlight the lack of longitudinal studies investigating the development of the I4.0 paradigm in a specific manufacturing context: this paper is the attempt at filling this gap.

May the Fourth (Industrial) Revolution be with You: Value Convergence within Uber’s Sharing Economy

This research provides empirical insights to reveal how value convergence occurs within Uber’s sharing economy. Uber’s business model is built on a digital platform that links private vehicle owners and their under-utilized assets to be economic revenue generating units for on-demand transport requests from potential clients. By analyzing adoption values for drivers and users of Uber, we identify antecedent operational and behavioral values for Uber adoption. We add to extant Industry 4.0 research and show value convergence emanating from economic and convenience motives. On the basis of our findings, we develop propositions for value convergence leveraging on cyber-physical systems embedded in Industry 4.0, and its application for theory and practice within the broader domains of the sharing economy.

Impact of IoT on Manufacturing Industry 4.0: A New Triangular Systematic Review

The Internet of Things (IoT) has realised the fourth industrial revolution concept; however, its applications in the manufacturing industry are relatively sparse and primarily investigated without contextual peculiarities. Our research undertakes an intricate critical review to investigate significant aspects of IoT applications in the manufacturing Industry 4.0 perspective to address this gap. We adopt a systematic literature review approach by Denyer and Tranfield (2009) to carry out critical analyses that help develop future research domains based on empirical studies. We describe key knowledge gaps in the existing literature and empirical studies by exploring the main contribution categories and finding six critical differences between traditional and manufacturing Industry 4.0 and 10 enablers and 11 challenges of IoT applications. Finally, an agenda for future research is proposed with 11 research domains to focus on the recognised gaps.

Make-in-India and Industry 4.0: technology readiness of select firms, barriers and socio-technical implications

Purpose

In this research, the emphasis is multifold. First objective is to study differences amongst India's Make-in-India, Germany's Industry 4.0 and China's Made-in-China 2025 on a macro level. Second objective is to identify where does individual industry segment out of the five broad segments (prioritized by Make-in-India initiative) represented by ten firms in India stand in terms of adoption of Industry 4.0 technologies. Third objective is to identify key barriers for each of these five industry segments. Finally, socio-technical interventions are also proposed aimed at faster adoption of Industry 4.0 technologies.

Design/methodology/approach

A mixed methodological approach is followed to achieve the research objectives. First, for the macro-level comparison of three pertinent countries, extant research and industry literature have been relied upon. Thereafter, at a micro level, inputs from experts belonging to focal sectors are included in this study to ascertain the current level of readiness of adoption of Industry 4.0 technologies and the barriers to adoption. Finally, the authors argue for and propose some socio-technical interventions that are aimed at mitigation of barriers for adoption of Industry 4.0 technologies.

Findings

It has been ascertained that amongst the ten firms (two each from given focal sectors) considered in the study, the automotive and the software firm are perhaps best placed to adopt the Industry 4.0 technology, while the infrastructure project management firm is least ready for Industry 4.0 technologies. The common barriers to adoption of Industry 4.0 technologies, as elaborated by experts belonging to each of the ten firms, are also identified. These three commons barriers are resistance to change, unclear economic benefits and problems related to coordination and collaboration.

Research limitations/implications

The study is one of first attempts to understand the nuances related to technology readiness across focal industries pertaining to the Make-in-India initiative and Industry 4.0. The study furthers the extant understanding of common and distinct barriers across industries. Employing the soft-systems methodology, the study advocates for a number of socio-technical interventions pertaining to establishment of e-skill ecosystem, community learning clusters and sector-focussed skill acquisition and augmentation. Since the study considers only two firms corresponding to each of the five focal sectors, including more firms across industries could have resulted in further validation of study as well.

Practical implications

Contrasting the initiatives of the three countries results in identification of different thematic focus of the respective initiatives. While India's Make-in-India initiative has a strong social dimension, Germany's Industry 4.0 and Made-in-China 2025 have key objective related to integration of cyber-physical systems and to graduate to innovation-driven country, respectively. Further, analysis on the technology readiness for adoption of Industry 4.0 technologies based on the respective experts' assessment results in understanding of the underlying barriers.

Social implications

Adopting the soft-systems perspective linking nuances of stakeholders, socio-technical systems and socio-economic characteristics results in several propositions to further the social objectives of India's Make-in-India initiative. These propositions advocate for pathways in which extant strengths in terms of technology, people and existing socio-technical structures can be brought together to cater to the requirements related to employability and skill augmentation of new as well as existing workforce.

Originality/value

Extant research literature is primarily focussed on certain specific topics within Industry 4.0 implementation and is mainly based on conceptual or theoretical basis. From a practitioners' perspective, only a few empirical papers could be found that too are typically focussed on single case studies resulting from pilot applications of Industry 4.0. However, such papers have not examined the broad implications of Industry 4.0 in terms of differences between key countries' manufacturing initiatives, readiness of key sectors, sectoral barriers and accompanying policy-level implications associated with implementation of Industry 4.0. Thus, the objective of this research is to abridge these research gaps.

Industry 4.0 Technologies and Their Impact in Contemporary Logistics: A Systematic Literature Review

Even though the topic of Industry 4.0 in the last decade has attracted significant and multifarious attention from academics and practitioners, a structured and systematic review of Industry 4.0 in the context of contemporary logistics is currently lacking. This study attempted to address this shortcoming by performing a systematic review of the available literature of Industry 4.0 in the logistics context. To that end, and after a systematic inclusion/exclusion process, 65 carefully selected papers were addressed in the study. The results obtained from this study were illustrated and discussed in order to provide answers to two research questions pre-defined by the authors. In essence, this study identified emerging aspects and present trends in the area, addressed the main technological developments and evolution of Industry 4.0 and their impact for contemporary logistics, and finally pinpointed literature shortcomings and currently under-explored areas with a high potential for impactful future research. Findings of this review can hopefully be used as the basis for future research in the emerging Logistics 4.0 concept and related topics.

The Green Blockchains of Circular Economy

Eco-friendly systems are necessitated nowadays, as the global consumption is increasing. A data-driven aspect is prominent, involving the Internet of Things (IoT) as the main enabler of a Circular Economy (CE). Henceforth, IoT equipment records the system’s functionality, with machine learning (ML) optimizing green computing operations. Entities exchange and reuse CE assets. Transparency is vital as the beneficiaries must track the assets’ history. This article proposes a framework where blockchaining administrates the cooperative vision of CE-IoT. For the core operation, the blockchain ledger records the changes in the assets’ states via smart contracts that implement the CE business logic and are lightweight, complying with the IoT requirements. Moreover, a federated learning approach is proposed, where computationally intensive ML tasks are distributed via a second contract type. Thus, “green-miners” devote their resources not only for making money, but also for optimizing operations of real-systems, which results in actual resource savings.

Digital transformation of legionella-safe cooling towers: an ecosystem design approach

Purpose

Legionnaires’ disease is a major threat to public health. Solutions to deal with this problem are usually siloed and not entirely effective. This paper aims to model the information requirements of legionella-safe cooling towers in the era of Industry 4.0.

Design/methodology/approach

A year-long design science research was conducted in a cooling tower producer for heavy industries. The project started with a bibliometric analysis and literature review of legionella in cooling towers. Goal modeling techniques are then used to identify the requirements for digital transformation.

Findings

The improvement of legionella prevention, detection and outbreak response in digitally enabled cooling tower should involve different stakeholders. Digital twins and blockchain are disruptive technologies that can transform the cooling tower industry.

Originality/value

For theory, this study revises the most recent advances in legionella protection. Legionella-safe systems must be prepared to anticipate, monitor and immediate alert in case of an outbreak. For practice, this paper presents a distributed and digital architecture for cooling tower safety. However, technology is only a part of outbreak management solutions, requiring trustworthy conditions and real-time communication among stakeholders.

Analyzing interrelated enablers of industry 4.0 for implementation in present industrial scenario

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Practical implications

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.

Originality/value

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.

Quality 4.0: The EFQM 2020 Model and Industry 4.0 Relationships and Implications

The European Foundation for Quality Management (EFQM) 2020 model is a comprehensive and updated business model that encompasses sustainability and shares features with Industry 4.0, emphasizing transformation and improved organizational performance, yet with different theoretical and practical foundations. This research highlights the EFQM 2020 model’s novelties and its relationships/implications with the Industry 4.0 paradigm, contributing to the Quality 4.0 body of knowledge. Several linkages between the EFQM 2020 model and Industry 4.0 have been identified, namely, at the criteria level and guidance points, which can support successful digital transformation by combining quality and excellence with Industry 4.0. However, given the model’s generic and non-prescriptive nature, there is no specific reference to the nine Industry 4.0 pillars. Additionally, the links between direction and organizational culture and leadership criteria and driving performance and transformation are not evident, which might be a concern for business and technology transformation strategies. Managing knowledge, skills, and capabilities is critical for the successful adoption of Industry 4.0. The EFQM model adds a strategic and technologically unbiased perspective to Industry 4.0, providing an integrated business excellence framework for Quality 4.0. With empirical support of the model application, future research is recommended to develop this subject further.

Evaluating Industry 4.0 Technology Application in SMEs: Using a Hybrid MCDM Approach

Evaluating Industry 4.0 technology application in small and medium-sized enterprises (SMEs) is an issue that requires a multi-criteria strategy comprising quantitative and qualitative elements. The purpose of this study is to integrate performance estimation of Industry 4.0 technology application using the technology–organization–environment (TOE) framework. Relating TOE to Industry 4.0 technology application evaluation is more multifaceted than other methods and it requires comprehensive analysis. In this study, we applied a multiple-criteria decision-making (MCDM) approach to develop a model which integrates MCDM to perform an assessment that prioritizes the influence weights of Industry 4.0 technology application to SMEs’ factors. Firstly, we carried out a review of the literature and the TOE framework was selected to generate nine elements, along with three aspects used to measure Industry 4.0 technology application in SMEs. Secondly, the approach of the decision-making trial and evaluation laboratory (DEMATEL) was set up using an influence network relations digraph (INRD). The DEMATEL-based analytic network process (DANP) was used to indicate the influence weights linking the above aspects and elements. Lastly, the modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique applied influence weights to assess the aspects/elements in the gaps identified and to investigate how to reduce the gaps so as to estimate the application of Industry 4.0 technology by SMEs. The results show that the technology aspect is the most influential factor.

A Review of Data Mining Applications in Semiconductor Manufacturing

For decades, industrial companies have been collecting and storing high amounts of data with the aim of better controlling and managing their processes. However, this vast amount of information and hidden knowledge implicit in all of this data could be utilized more efficiently. With the help of data mining techniques unknown relationships can be systematically discovered. The production of semiconductors is a highly complex process, which entails several subprocesses that employ a diverse array of equipment. The size of the semiconductors signifies a high number of units can be produced, which require huge amounts of data in order to be able to control and improve the semiconductor manufacturing process. Therefore, in this paper a structured review is made through a sample of 137 papers of the published articles in the scientific community regarding data mining applications in semiconductor manufacturing. A detailed bibliometric analysis is also made. All data mining applications are classified in function of the application area. The results are then analyzed and conclusions are drawn.

Fine-Grained Mechanical Chinese Named Entity Recognition Based on ALBERT-AttBiLSTM-CRF and Transfer Learning

Manufacturing text often exists as unlabeled data; the entity is fine-grained and the extraction is difficult. The above problems mean that the manufacturing industry knowledge utilization rate is low. This paper proposes a novel Chinese fine-grained NER (named entity recognition) method based on symmetry lightweight deep multinetwork collaboration (ALBERT-AttBiLSTM-CRF) and model transfer considering active learning (MTAL) to research fine-grained named entity recognition of a few labeled Chinese textual data types. The method is divided into two stages. In the first stage, the ALBERT-AttBiLSTM-CRF was applied for verification in the CLUENER2020 dataset (Public dataset) to get a pretrained model; the experiments show that the model obtains an F1 score of 0.8962, which is better than the best baseline algorithm, an improvement of 9.2%. In the second stage, the pretrained model was transferred into the Manufacturing-NER dataset (our dataset), and we used the active learning strategy to optimize the model effect. The final F1 result of Manufacturing-NER was 0.8931 after the model transfer (it was higher than 0.8576 before the model transfer); so, this method represents an improvement of 3.55%. Our method effectively transfers the existing knowledge from public source data to scientific target data, solving the problem of named entity recognition with scarce labeled domain data, and proves its effectiveness.

Sustainable Industry 4.0 in Production and Operations Management: A Systematic Literature Review

This study draws attention to the upcoming changes within sustainable value chains and manufacturing environments caused by the digital transformation. With a special focus on Industry 4.0 (I4.0), the presented study explores the scientific progress within this research field. A systematic literature review approach using a set of predefined keywords and with several exclusion criteria was adopted in order to identify the literature that is related to sustainability in I4.0 and its impact in the area of production and operations management (P&OM). A total of 89 papers from the period 2010–2020 were identified, which were then examined along the lines of the most influential journals, key topics of the selected literature, geographical distribution, and sustainability dimensions. The analysis was executed via bibliometric and text mapping tools, namely NVivo and BibExcel. Furthermore, a focus group discussion with experts from European semiconductor manufacturing companies and researchers from several academic institutions was conducted to derive practical insights. The results of this study will contribute to the evaluation of sustainable I4.0 innovations from the past 10 years. The key issues and research gaps identified in this article will provide a reference point to encourage and guide interested researchers for future studies, thus supporting both theoretical and practical progress in this research area.

The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0

Industry 4.0 is expected to impart profound changes to the configuration of manufacturing companies with regards to what their value proposition will be and how their production network, supplier base and customer interfaces will develop. The literature on the topic is still fragmented; the features of the emerging paradigm appear to be a contested territory among different academic disciplines. This study assumes a value chain perspective to analyze the evolutionary trajectories of manufacturing companies. We developed a Delphi-based scenario analysis involving 76 experts from academia and practice. The results highlight the most common expectations as well as controversial issues in terms of emerging business models, size, barriers to entry, vertical integration, rent distribution, and geographical location of activities. Eight scenarios provide a concise outlook on the range of possible futures. These scenarios are based on four main drivers which stem from the experts' comments: demand characteristics, transparency of data among value chain participants, maturity of additive manufacturing and advanced robotics, and penetration of smart products. Researchers can derive from our study a series of hypotheses and opportunities for future research on Industry 4.0. Managers and policymakers can leverage the scenarios in long-term strategic planning.

Classification of Small- and Medium-Sized Enterprises Based on the Level of Industry 4.0 Implementation

Due to Industry 4.0 technologies, small- and medium-sized enterprises have a great opportunity to increase their competitiveness. However, the question remains as to whether they are truly able to implement such modern technologies faster and carry out digital transformation. The main aim of the paper is to classify small- and medium-sized enterprises into various groups, according to the level of implementation of Industry 4.0, using the Index of Industry 4.0. Based on the results of the cluster analysis, the small and medium enterprises are categorized into four different groups, according to the level of implementation of Industry 4.0. There are top Industry 4.0 technological enterprises, I4 start enterprises, noobs enterprises, and I4 advances enterprises. So far, the largest group consists of the small- and medium-sized enterprises that are just starting out with the introduction of Industry 4.0 technologies, such as IT infrastructure, digitalization (data, cloud, data analysis, and information systems), and sensors. On the other hand, the top I4 technological enterprises group is the least numerous. The analysis carried out comparing the small- and medium-sized enterprises with the large enterprises shows that the SMEs still have a lower level of Industry 4.0 implementation. This confirms the assumption that the large enterprises have greater opportunities to use new technologies and transform them into smart factories. However, this situation may change in the future if new technologies become more accessible, and SMEs are worth investing in Industry 4.0 in terms of the return on investment.

Text Mining in Big Data Analytics

Text mining in big data analytics is emerging as a powerful tool for harnessing the power of unstructured textual data by analyzing it to extract new knowledge and to identify significant patterns and correlations hidden in the data. This study seeks to determine the state of text mining research by examining the developments within published literature over past years and provide valuable insights for practitioners and researchers on the predominant trends, methods, and applications of text mining research. In accordance with this, more than 200 academic journal articles on the subject are included and discussed in this review; the state-of-the-art text mining approaches and techniques used for analyzing transcripts and speeches, meeting transcripts, and academic journal articles, as well as websites, emails, blogs, and social media platforms, across a broad range of application areas are also investigated. Additionally, the benefits and challenges related to text mining are also briefly outlined.

Sustainable Product Lifecycle: The Role of ICT

In this paper, we introduce the themes addressed and the approaches used in the Special Issue entitled “Sustainable Product Lifecycle: The Role of ICT”. Specifically, by offering multiple perspectives of analysis, this work increases our comprehension and understanding of the role of information and communications technologies (ICTs) in enhancing sustainable product lifecycle.

Generating Transparency in the Worldwide Use of the Terminology Industry 4.0

In 2011, the concept of Industry 4.0 was introduced and later adopted by the German government, paving the way for a new industrial revolution in Germany. The high significance of this topic is reflected by the large number of corresponding publications. Additionally, the regional focus of research is widespread on a global level and often differs even at a national level. This paper generates transparency regarding the adoption of the concept of Industry 4.0 by analyzing the locations of main contributors within the research field on an international, European, and German-national level. Further, it examines the regionally different foci concerning the concept of Industry 4.0. Having identified four main aspects linked to Industry 4.0 within a pre-study, a quantitative literature research was conducted based on over 800 published papers. The results were further visualized with QGIS. Looking at the results, it can be concluded that the German research community is virtually the only user of the term Industry 4.0, while other institutions seem to link their research to other related concepts. On a German level, the majority of the analyzed studies originate from Southern and Western Germany. North Rhine-Westphalia and the Aachen/Jülich region, in particular, represent main contributors.

Evaluating the Degree of Uncertainty of Research Activities in Industry 4.0

Research and development (R&D) are always oriented towards new discoveries, based on original terms or hypotheses, and their concluding outcomes are often uncertain. The present work focused on the degree of uncertainty for R&D activities. In fact, uncertainty makes it difficult to quantify the time and resources needed to achieve a final outcome, create a work plan and budget, and finalize the resulting “innovative” products or services that could be transferred or exchanged in a specific market. The present work attempts to indicate the degree of uncertainty of the research activities developed by a set of firms. The method used aimed to quantify the five criteria defined by the Manual of Frascati. Through the creation of an uncertainty cloud, a cone of uncertainty was defined following an approach based on project management. The evaluation grid was characterized by the decomposition of the different variables divided into quartiles, which allowed for the detection of the evolution of the project and each of its component. The ancillary objective aim was to also observe the development degree of these industries towards a framework of Industry 4.0.