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The effect of the transition toward digital technologies on today’s businesses (i.e. Industry 4.0 transition) is becoming increasingly relevant, and the number of studies that have examined this phenomenon has grown rapidly. However, systematizing the existing findings is still a challenge, from both a theoretical and a managerial point of view. In such a setting, the knowledge management (KM) discipline can provide guidance to address such a gap. Indeed, the implementation of fundamental digital technologies is reshaping how firms manage knowledge. Thus, this study aims to critically review the existing literature on Industry 4.0 from a KM perspective.

First, the authors defined a structuring framework to highlight the role of Industry 4.0 transition along with absorptive capacity (ACAP) processes (acquisition, assimilation, transformation and exploitation), while specifying what is being managed, that is data, information and/or (actual) knowledge, according to the data-information-knowledge (DIK) hierarchy. The authors then followed the systematic literature review methodology, which involves the use of explicit criteria to select publications to review and outline the stages a process has to follow to provide a transparent and replicable review and to analyze the existing literature according to the theoretical framework. This procedure yielded a final list of 150 papers.

By providing a clear picture of what scholars have studied so far on Industry 4.0 transition, in terms of KM, this literature review highlights that among all the studied digital technologies, the big data analytics technology is the one that has been explored the most in each phase of the ACAP process. A constructive body of research has also emerged in recent years around the role played by the internet of things, especially to explain the acquisition of data. On the other hand, some digital technologies, such as cyber security and smart manufacturing, have largely remained unaddressed. An explanation of the role of these technologies has been provided, from a KM perspective, together with the business implications.

This study is one of the first attempts to revise the literature on Industry 4.0 transition from a KM perspective, and it proposes a novel framework to read existing studies and on which to base new ones. Furthermore, the synthesis makes two main contributions. First, it provides a clear picture of the different digital technologies that support the four ACAP phases in relation to the DIK hierarchy. Accordingly, these results can emphasize what the literature has looked at so far, as well as which digital technologies have gained the most attention and their impacts in terms of KM. Second, the synthesis provides prescriptive considerations on the development of future research avenues, according to the proposed research framework.

The benefits of artificial intelligence (AI) related technologies for manufacturing firms are well recognized, however, there is a lack of industrial AI (I-AI) maturity models to enable companies to understand where they are and plan where they should go. The purpose of this study is to propose a comprehensive maturity model in order to help manufacturing firms assess their performance in the I-AI journey, shed lights on future improvement, and eventually realize their smart manufacturing visions.

This study is based on (1) a systematic review of literature on assessing I-AI-related technologies to identify relevant measured indicators in the maturity model, and (2) semi-structured interviews with domain experts to determine maturity levels of the established model.

The I-AI maturity model developed in this study includes two main dimensions, namely “Industry” and “Artificial Intelligence”, together with 12 first-level indicators and 35 second-level indicators under these dimensions. The maturity levels are divided into five types: planning level, specification level, integration level, optimization level, and leading level.

The maturity model integrates indicators that can be used to assess AI-related technologies and extend the existing maturity models of smart manufacturing by adding specific technical and nontechnical capabilities of these technologies applied in the industrial context. The integration of the industry and artificial intelligence dimensions with the maturity levels shows a road map to improve the capability of applying AI-related technologies throughout the product lifecycle for achieving smart manufacturing.

The purpose of this research is to develop a generic framework of a digital twin (DT)-based automated construction progress monitoring through reality capture to extended reality (RC-to-XR).

IDEF0 data modeling method has been designed to establish an integration of reality capturing technologies by using BIM, DTs and XR for automated construction progress monitoring. Structural equation modeling (SEM) method has been used to test the proposed hypotheses and develop the skill model to examine the reliability, validity and contribution of the framework to understand the DRX model's effectiveness if implemented in real practice.

The research findings validate the positive impact and importance of utilizing technology integration in a logical framework such as DRX, which provides trustable, real-time, transparent and digital construction progress monitoring.

DRX system captures accurate, real-time and comprehensive data at construction stage, analyses data and information precisely and quickly, visualizes information and reports in a real scale environment, facilitates information flows and communication, learns from itself, historical data and accessible online data to predict future actions, provides semantic and digitalize construction information with analytical capabilities and optimizes decision-making process.

The research presents a framework of an automated construction progress monitoring system that integrates BIM, various reality capturing technologies, DT and XR technologies (VR, AR and MR), arraying the steps on how these technologies work collaboratively to create, capture, generate, analyze, manage and visualize construction progress data, information and reports.

This paper aims to describe how linked data technologies can change the digital library collections, what are the benefits of linked data applications in digital libraries and what are the challenges of digital libraries in linked data environment.

The present study is based on substantial literature review on the applications of linked data technologies in digital libraries. The search engines such as Google, Yahoo and Google Scholar were used to find the relevant literature for the study. Online databases such as Pro Quest, Science Direct, Emerald and JSTORE were also used to find the relevant literature of the study. Databases of Library Sciences Library and Information Science and Technology Abstracts and Library Information Science Abstracts were also used to find the relevant literature of the study. Library, linked data technologies, Semantic Web, digital library and digital collections were the main keywords which were used to find the relevant literature for the study.

The evolution of linked data technologies and Semantic Web has changed the traditional role of the libraries. Traditional libraries are converting into digital libraries and digital libraries are in a struggle to publish their resources on the Web using XML-based metadata standards. It has made capable the digital collections to be viewed by machines on the Web just like human. On the emergence of linked data applications in digital libraries, Web visibility of the libraries has been enhanced to provide the opportunities for the users to find their required quality information of libraries round-the-clock on the Web. National Library of France, National Library of Spain, Europeana, Digital Public Library of Americana, Library of Congress and The British Library have taken the initiatives to publish their resources on the Web using linked data technologies.

This study present several key issues for policy makers, software developers, decision makers and library administrators about linked data technologies and its implementations in digital libraries. The present study may play its role to facilitate the users of the Web who are enthusiastically interested to exploit the quality and authentic library resources on the Web round-the-clock. Search engines will also achieve their longstanding goal to exploit the quality resources of the libraries for their Web users to make their Web appearance more credible and trustworthy.

Road freight transportation companies need to take advantage of information and communication technologies to develop capabilities. This study proposes a framework to guide road freight transportation companies to achieve data visibility in their operations by developing such capabilities. By proposing this framework, this research contributes to literature and practice, highlighting the capabilities and the respective supporting technologies for improved data visibility in road freight transportation.

A mixed-method approach is used to develop the framework, considering three methodological steps. In phase 1, the capabilities are identified in the literature and validated by experts. In phase 2, an empirical assessment of cause–effect relationships between capabilities is performed using a multiple case study and DEMATEL. Lastly, in phase 3, an analysis of the cause model and significant associations is conducted to enable the development of the framework. In addition, the proposed framework was validated by the experts interviewed.

The results provide a framework that explains the link between the technology-enabled data visibility capabilities in road freight transportation operations. In addition, a pathway was established that road freight transportation companies could follow to achieve data visibility in their operations by developing such capabilities.

This work develops the first framework that provides a path for data visibility in road freight transportation operations from adopting certain technologies. The insights are compelling for researchers and practitioners to optimize the decision-making process for adopting technologies and developing capabilities related to data visibility.

The current era of the fourth industrial revolution has attracted significant research on the use of digital technologies in improving construction project delivery. However, less emphasis has been placed on how these digital tools will influence the management of the construction workforce. To this end, using a review of existing works, this chapter explores the fourth industrial revolution and its associated technologies that can positively impact the management of the construction workforce when implemented. Also, the possible challenges that might truncate the successful deployment of digital technologies for effective workforce management were explored. The chapter submitted that implementing workforce management-specific digital platforms and other digital technologies designed for project delivery can aid effective workforce management within construction organisations. Technologies such as cloud computing, the Internet of Things, big data analytics, robotics and automation, and artificial intelligence, among others, offer significant benefits to the effective workforce management of construction organisations. However, several challenges, such as resistance to change due to fear of job loss, cost of investment in digital tools, organisational structure and culture, must be carefully considered as they might affect the successful use of digital tools and by extension, impact the success of workforce management in the organisations.

Digital twin (DT) is an emerging technology that enables sophisticated interaction between physical objects and their virtual replicas. Although DT has recently gained significant attraction in both industry and academia, there is no systematic understanding of DT from its development history to its different concepts and applications in disparate disciplines. The majority of DT literature focuses on the conceptual development of DT frameworks for a specific implementation area. Hence, this paper provides a state-of-the-art review of DT history, different definitions and models, and six types of key enabling technologies. The review also provides a comprehensive survey of DT applications from two perspectives: (1) applications in four product-lifecycle phases, i.e. product design, manufacturing, operation and maintenance, and recycling and (2) applications in four categorized engineering fields, including aerospace engineering, tunneling and underground engineering, wind engineering and Internet of things (IoT) applications. DT frameworks, characteristic components, key technologies and specific applications are extracted for each DT category in this paper. A comprehensive survey of the DT references reveals the following findings: (1) The majority of existing DT models only involve one-way data transfer from physical entities to virtual models and (2) There is a lack of consideration of the environmental coupling, which results in the inaccurate representation of the virtual components in existing DT models. Thus, this paper highlights the role of environmental factor in DT enabling technologies and in categorized engineering applications. In addition, the review discusses the key challenges and provides future work for constructing DTs of complex engineering systems.

With the rapid change that has taken place with digitalization and data-driven approaches in supply chains, business environment become more competitive and reaching sustainability in supply chains become even more challenging. In order to manage supply chains properly, in terms of considering environmental, social and economic impacts, organizations need to deal with huge amount of data and improve organizations' data management skills. From this view, increased number of stakeholders and dynamic environment reveal the importance of data-driven technologies in sustainable supply chains. This complex structure results in new kind of risks caused by data-driven technologies. Therefore, the aim of the study to analyze potential risks related to data privacy, trust, data availability, information sharing and traceability, i.e. in sustainable supply chains.

A hybrid multi-criteria decision-making (MCDM) model, which is the integration of step-wise weight assessment ratio analysis (SWARA) and TOmada de Decisao Interativa Multicriterio (TODIM) methods, is going to be used to prioritize potential risks and reveal the most critical sustainability dimension that is affected from these risks.

Results showed that economic dimension of the sustainable supply chain management (SSCM) is the most critical concept while evaluating risks caused by data-driven technologies. On the other hand, risk of data security, risk of data privacy and weakness of information technology systems and infrastructure are revealed as the most important risks that organizations should consider.

The contribution of the study is expected to guide policymakers and practitioners in terms of defining potential risks causes by data-driven technologies in sustainable supply chains. In future studies, solutions can be suggested based on these risks for achieving sustainability in all stages of the supply chain causes by data-driven technologies.

The purpose of this paper is to identify current technologies related to Industry 4.0 and to develop a rationale to enhance the understanding of their functions within a data-driven paradigm.

A systematic literature review of 119 papers published in journals included in the Journal Citation Report (JCR) was conducted to identify Industry 4.0 technologies. A descriptive analysis characterizes the corpus, and a content analysis identifies the technologies.

The content analysis identified 111 technologies. These technologies perform four functions related to data: data generation and capture, data transmission, data conditioning, storage and processing and data application. The first three groups consist of enabling technologies and the fourth group of value-creating technologies. Results show that Industry 4.0 publications focus on enabling technologies that transmit and process data. Value-creating technologies, which apply data in order to develop new solutions, are still rare in the literature.

The proposed framework serves as a structure for analysing the focus of publications over time, and enables the classification of new technologies as the paradigm evolves.

Because the technical side of the new production paradigm is complex and represents an evolving field, managers benefit from a simplified and data-driven approach. The proposed framework suggests that Industry 4.0 should be approached by looking at how data can create value and at what role each technology plays in this task.

The study makes a direct link between Industry 4.0 technologies and the key resource of this revolution, i.e. data. It provides a rationale that not only establishes relationships between technologies and data, but also highlights their roles as enablers or creators of value. Beyond showing the current focus of Industry 4.0 publications, this paper proposes a framework that is useful for tracking the evolution of the paradigm.