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An integrative review exploring the development of sustainable product design in the technological context of Industry 4.0
This study explores the relationships among Industry 4.0 technologies, their application areas, and the involvement of Russian manufacturing firms in global and domestic value chains (GVCs and DVCs). Using an original survey dataset of approximately 1,700 Russian manufacturing firms, we apply logit and multinomial logit regressions. We make a novel contribution to the literature by uncovering an asymmetry in the adoption of Industry 4.0 technologies among Russian industrial firms in Domestic Value Chains (DVCs) and Global Value Chains (GVCs). This asymmetry has the potential to impede GVC localization and DVC internationalization. Based on our results, software automation solutions are the only ones demonstrating statistical significance for firms participating in GVCs, DVCs, and both GVCs and DVCs simultaneously. Companies in DVCs demonstrate a broader utilization of Industry 4.0 technologies across various application areas. We also identify evidence of reshoring in DVCs, indicating that Industry 4.0 adoption encourages firms to establish enduring relationships with domestic suppliers. Highlighting that differences in technology adoption are influenced by external factors, including adherence to international standards and regulatory principles, we propose policy implications for developing countries. Recommendations encompass reducing entry barriers to DVCs, improving procurement transparency, and promoting competition in the digital solutions market to empower firms for seamless GVC integration.
In many application domains data from different sources are increasingly available to thoroughly monitor and describe a system or device. Especially within the industrial automation domain, heterogeneous data and its analysis gain a lot of attention from research and industry, since it has the potential to improve or enable tasks like diagnostics, predictive maintenance, and condition monitoring. For data analysis, machine learning based approaches are mostly used in recent literature, as these algorithms allow us to learn complex correlations within the data. To analyze even heterogeneous data and gain benefits from it in an application, data from different sources need to be integrated, stored, and managed to apply machine learning algorithms. In a setting with heterogeneous data sources, the analysis algorithms should also be able to handle data source failures or newly added data sources. In addition, existing knowledge should be used to improve the machine learning based analysis or its training process. To find existing approaches for the machine learning based analysis of heterogeneous data in the industrial automation domain, this paper presents the result of a systematic literature review. The publications were reviewed, evaluated, and discussed concerning five requirements that are derived in this paper. We identified promising solutions and approaches and outlined open research challenges, which are not yet covered sufficiently in the literature.
Given that the previous industrial revolution brought about significant and occasionally unanticipated changes in the “economy,” “the environment,” and “society,” industry 4.0’s sustainability effects deserve all of academia's attention. The study of the start-up operations 4.0 sustainability effects is in its infancy, and more research is needed to fully understand the sustainability implication of start-up operation 4.0 in terms of the influence of digitization on the economy, the environment, and society. Though research on sustainability in industry 4.0 has been performed, a study on the factors influencing start-up operations 4.0 to achieve sustainability has not received the necessary attention. To address this issue and gap, the current study models the factors influencing start-up operations 4.0 to achieve sustainability. Through review of literatures and experts’ opinion, ten factors have been identified. To identify how the factors interact, the “Modified-Total Interpretive Structural Modelling (M-TISM)” technique is employed, and the “MICMAC method” is used to “rank and categorize” the factors. The findings shows that the key importance should be given to the “management support for sustainability adoption,” “decentralized system,” “green design,” and “machine learning system.” The developed hierarchical link between variables provides a comprehensive understanding of how sustainability helps start-ups competitiveness and what elements are responsible for this impact. The management of the start-ups can utilize this framework to enhance start-up operations 4.0 since our study uses factors often studied separately but not combined. This study will help academics, and key stakeholders understand the aspects that lead to sustainability in start-up operations 4.0.
Artificial Intelligence (AI) and Machine learning (ML) represents an important evolution in computer science and data processing systems which can be used in order to enhance almost every technology-enabled service, products, and industrial applications. A subfield of artificial intelligence and computer science is named machine learning which focuses on using data and algorithms to simulate learning process of machines and enhance the accuracy of the systems. Machine learning systems can be applied to the cutting forces and cutting tool wear prediction in CNC machine tools in order to increase cutting tool life during machining operations. Optimized machining parameters of CNC machining operations can be obtained by using the advanced machine learning systems in order to increase efficiency during part manufacturing processes. Moreover, surface quality of machined components can be predicted and improved using advanced machine learning systems to improve the quality of machined parts. In order to analyze and minimize power usage during CNC machining operations, machine learning is applied to prediction techniques of energy consumption of CNC machine tools. In this paper, applications of machine learning and artificial intelligence systems in CNC machine tools is reviewed and future research works are also recommended to present an overview of current research on machine learning and artificial intelligence approaches in CNC machining processes. As a result, the research filed can be moved forward by reviewing and analysing recent achievements in published papers to offer innovative concepts and approaches in applications of artificial Intelligence and machine learning in CNC machine tools.
Context
Cyber-physical systems present a challenge to testers, bringing complexity and scale to safety-critical and collaborative environments. Digital twins enhance these systems through data-driven and simulation based models coupled to physical systems to provide visualisation, predict future states and communication. Due to the coupling between digital and physical worlds, digital twins provide a new perspective into cyber–physical system testing.
Objective
The objectives of this study are to summarise the existing literature on digital-twin-based testing. We aim to uncover emerging areas of adoptions, the testing techniques used in these areas and identify future research areas.
Method
We conducted a systematic literature review which answered the following research questions: What cyber–physical systems are digital twins currently being used to test? How are test oracles defined for cyber–physical systems? What is the distribution of white-box, black-box and grey-box modelling techniques used for digital twins in the context of testing? How are test cases defined and how does this affect test inputs?
Results
We uncovered 26 relevant studies from 480 produced by searching with a curated search query. These studies showed an adoption of digital-twin-based testing following the introduction of digital twins in industry as well as the increasing accessibility of the technology. The oracles used in testing are the digital twin themselves and therefore rely on both system specification and data derivation. Cyber-physical systems are tested through passive testing techniques, as opposed to either active testing through test cases or predictive testing using digital twin prediction.
Conclusions
This review uncovers the existing areas in which digital twins are used to test cyber–physical systems as well as outlining future research areas in the field. We outline how the infancy of digital twins has affected their wide variety of definitions, emerging specialised testing and modelling techniques as well as the current lack of predictive ability.
Additive manufacturing (AM), originally used for prototyping, is increasingly adopted for custom final part production across different industries. However, printing speed and production volume are two barriers for the adoption of AM for product customization at large scale. Nevertheless, manufacturers could aim to combine the benefits of AM for product customization with traditional mass customization (MC) technologies over the product life cycle (PLC). This approach is showcased in our paper as a manufacturing opportunity and is addressed via a non convex-concave optimization model that considers a monopolist manufacturer producing horizontally differentiated products at scale. To satisfy individual customer preferences under capacity considerations, the firm jointly decides on the inventory, production quantity, product variety, optimal technology-switching times (between AM and MC) and pricing strategy. Our approach can be implemented by decision-makers to leverage customer-centricity and benefit from this novel hybrid manufacturing practice. By deriving a closed-form solution for the production quantity based on an adaptive inventory policy, the resulting optimization problem is solved using the Sample Average Approximation framework grounded by analytical results. Our results demonstrate that the new usage of AM with MC can benefit a manufacturer for customer-centric driven strategies. Significant profit improvements can be achieved with an AM–MC–AM technology-switching scenario under certain capacity conditions and with an increasing-decreasing pricing strategy. Our results also indicate that the benefits of pricing flexibility are highest when capacity is unlimited or when the firm does not hold inventory. Under capacity constraints, a simple decreasing pricing policy combined with inventory performs very well.
IoT edge computing is a new computing paradigm “in the IoT domain” for performing calculations and processing at the edge of the network, closer to the user and the source of the data. This paradigm is relatively recent, and, together with cloud and fog computing, there may be some confusion about its meaning and implications. This paper aims to help practitioners and researchers better understand what the industry thinks about what IoT edge computing is, and the expected benefits and challenges associated with this paradigm. We conducted a survey using a semi-structured in-depth questionnaire to collect qualitative data from relevant stakeholders from 29 multinational companies and qualitatively analyzed these data using the Constructivist Grounded Theory (Charmaz) method. Several researchers participated in the coding process (collaborative coding). To ensure consensus on the constructs that support the theory and thus improve the rigor of qualitative research, we conducted an intercoder agreement analysis. From the analysis, we have derived a substantive and analytic theory of what companies perceive about IoT edge computing, its benefits and challenges. The theory is substantive in that the scope of validity refers to the 29 surveys processed and analytic in that it analyzes “what is” rather than explaining causality or attempting predictive generalizations. A public repository with all the data related to the information capture process and the products resulting from the analysis of this information is publicly available. This study aims to strengthen the evidence and support practitioners in making better informed decisions about why companies are adopting edge computing and the current challenges they face. Additionally, the testing theory phase shows that the results are aligned with the ISO/IEC TR 30164 standard.
Sustainability 4.0 is being enabled through the effective adoption of modern technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML), Machine Vision (MV), Data Analytics (DA), Additive Manufacturing (AM) and other modern technologies. These technologies enable services at significantly lower prices due to the effective use of energy and resources with lesser wastage. Manufacturers are constantly looking for methods to lower the operating expenses associated with production processes. Manufacturers might optimise their value chain's production and associated processes by adopting sustainability 4.0 technologies. Such technologies will help manufacturers select their optimal facilities and employees, lower operational costs, enhance productivity and resource utilisation and provide a picture of process gaps that can be addressed. Sustainability is based on the effective use and reuse of resources across the product life cycle, from materials and processes to equipment and skills. Sustainable manufacturing produces manufactured goods using economically viable procedures that reduce negative environmental consequences while preserving energy and natural resources. This paper briefs Sustainability 4.0 and its significant needs. Various fundamental technologies and futuristic research aspects for Sustainability 4.0 are discussed diagrammatically. Finally, we identified and discussed significant applications of Sustainability 4.0 in manufacturing. Sustainability 4.0 refers to a long-term vision for enterprises that allows them to continue perpetually without depleting resources faster than they can be replaced. Sustainability 4.0 entails empowering prosumers to co-create to reshape the economy and society toward social inclusion and environmental sustainability. The use of sustainability and digitization to solve environmental, social, and economic problems appears hopeful and exhausting.
Over the years, there has been a heavy reliance on cloud computing as IT has innovated through time. In recent times cloud computing has grown monumentally. Many organizations rely on this technology to perform their business as usual and use it as a backbone of their companies' IT infrastructure. This paper investigates the organizational adaptation for cloud computing technology - reviewing case studies from various institutions and companies worldwide to provide a detailed analysis of innovative techniques with cloud computing. We investigate the features and delivery approaches cloud computing offers and the potential challenges and constraints we face when adopting cloud computing into the business setting. We also explore the cybersecurity elements associated with cloud computing, focusing on intrusion detection and prevention and understanding how that can be applied in the cloud. Finally, we investigate the future research directions for cloud computing and expand this paper into further articles with experiments and results.
This paper introduces a novel scientometrics method and applies it to estimate the subject coverages of many of the popular English-focused bibliographic databases in academia. The method uses query results as a common denominator to compare a wide variety of search engines, repositories, digital libraries, and other bibliographic databases. The method extends existing sampling-based approaches that analyze smaller sets of database coverages. The findings show the relative and absolute subject coverages of 56 databases—information that has often not been available before. Knowing the databases’ absolute subject coverage allows the selection of the most comprehensive databases for searches requiring high recall/sensitivity, particularly relevant in lookup or exploratory searches. Knowing the databases’ relative subject coverage allows the selection of specialized databases for searches requiring high precision/specificity, particularly relevant in systematic searches. The findings illustrate not only differences in the disciplinary coverage of Google Scholar, Scopus, or Web of Science, but also of less frequently analyzed databases. For example, researchers might be surprised how Meta (discontinued), Embase, or Europe PMC are found to cover more records than PubMed in Medicine and other health subjects. These findings should encourage researchers to re-evaluate their go-to databases, also against newly introduced options. Searching with more comprehensive databases can improve finding, particularly when selecting the most fitting databases needs particular thought, such as in systematic reviews and meta-analyses. This comparison can also help librarians and other information experts re-evaluate expensive database procurement strategies. Researchers without institutional access learn which open databases are likely most comprehensive in their disciplines.
Industry 4.0 has been associated with the rise of disruptive intelligence and information technologies. These cutting-edge technologies have the potential to increase productivity while simultaneously having a significant impact on social and environmental sustainability. As a result, manufacturers must evaluate the role of these innovative technologies in sustainable development, as these technologies have the potential to address prevalent sustainability issues. A content-centric study of the implementation of these Industry 4.0 cutting-edge technologies in sustainable manufacturing is currently absent. A systematic literature study was conducted to explain the potential contribution of these novel technologies to the economic, social, and environmental dimensions of manufacturing industries. This study describes how these cutting-edge technologies are used in sustainable manufacturing. The findings of this study are particularly beneficial to practitioners who seek to apply one or more digital technologies to sustainable development.
Cyber-physical systems (CPS) offer great potential for the digital transformation of industrial value creation in the context of Industry 4.0. They unify and integrate several technological approaches, including big data analysis and artificial intelligence, enhancing real-time monitoring and control of manufacturing processes. An extensive knowledge base formed by various disciplines, including information systems, engineering, and computer science, already exists for CPS. However, this knowledge has not been holistically captured and structured to date. To address this research gap, this study conducts a large-scale literature review of 2365 papers representing the current state of the research and then develops a novel categorization on industrial CPS with 10 sections, 32 areas, and 246 fields. The categorization is presented in hierarchical graphical form and can also be utilized as a web tool. To conclude, a perspective on future research needs and potentials to enhance Industry 4.0 in both research and practice are offered.
This article presents the results of research with the main goal of identifying possible applications of edge computing (EC) in industry. This study used the methodology of systematic literature review and text mining analysis. The main findings showed that the primary goal of EC is to reduce the time required to transfer large amounts of data. With the ability to analyze data at the edge, it is possible to obtain immediate feedback and use it in the decision-making process. However, the implementation of EC requires investments not only in infrastructure, but also in the development of employee knowledge related to modern computing methods based on artificial intelligence. As the results of the analyses showed, great importance is also attached to energy consumption, both in ongoing production processes and for the purposes of data transmission and analysis. This paper also highlights problems related to quality management. Based on the analyses, we indicate further research directions for the application of edge computing and associated technologies that are required in the area of intelligent resource scheduling (for flexible production systems and autonomous systems), anomaly detection and resulting decision making, data analysis and transfer, knowledge management (for smart designing), and simulations (for autonomous systems). View Full-Text
Due to rapid changes in consumer demand and electronic technology advancements, electronics products in general, and specifically home automation segment products, are becoming obsolete at a very high rate. Therefore, strategies for End-of-Life (EOL) management need to be adapted. However, EOL management of these products is not globally approached due to the lack of autonomous, integrated, modular, and flexible systems to cope with the wide variety, and heterogeneous complicate structure of products. This paper reviews current practices and issues related to EOL management of printed circuit boards (PCBs) and mechatronics products in the home automation segment, focusing on their different material compositions as well as on the uses of these raw materials in the circular economy perspective. In addition, this paper provides possible Cyber-Physical System (CPS) implementation scenarios using physical tools available at the STIIMA-CNR de-manufacturing pilot plant. Eventually, this paper highlights challenges and guidelines to develop a new generation of advanced EOL management through the Cyber-Physical System technique.
Sustainable manufacturing aims to deal with the challenges such as climate change, biodiversity loss and resource scarcity by minimising the environmental impacts due to product, processes, and systems through optimal use of energy and resources. Life cycle assessment (LCA) has become an important tool to identify, evaluate and assess the environmental impact of a product, process, or system, along with all the stages of the life cycle of a product. However, several drawbacks such as complexity, uncertainty and impreciseness are also associated with this methodology. Therefore, live LCA as a plausible solution, is gaining popularity with the advancements in Industry 4.o tools and techniques, enhancing ability to collect and analyse live data from various processes, interpret results, identify hotspots, trade-offs, and present better ideas about the environmental impacts in-line with the process. This paper proposes a cyber-physical production system framework for live LCA to estimate the environmental impact of 3D printed products with different combinations of design and process parameters. Also, optimum settings of these parameters are determined, and validated to minimize the environmental impacts. This enables real-time monitoring of the environmental impacts, driving prompt decision support for useful insights to the operator, project manager, business manager, or customer thereby improving the visibility and transparency.
This chapter aims to analyze the Industry 4.0 framework, identify the definition and drivers of the Industry 4.0 paradigm, discuss its potential effect, and determine obstacles of the Industry 4.0. For the research methodology, a critical literature review is performed, we relied on the recent studies related to industry 4.0. Findings-This study concluded that Industry 4.0 describes a future production system's vision; it is an inevitable revolution and radical change, covering a wide range of innovative technologies, and all sectors. Industry 4.0 brings significant advantages to organizations , including real-time data analysis, increased visibility, autonomous monitoring, enhanced productivity, and competitiveness. The key features of Industry 4.0 are collaboration and integration of schemes, both horizontal and vertical. Innovation performs an essential role in organizations, sectors, countries. Industry 4.0 has enormous potential effect in many areas, and its application will have an impact across transforming the work environment. Industry 4.0 leads to potentials in three dimensions of sustainability. The KUKA corporation is an application for industry 4.0, for instance, smart factories, M-2-M, intelligent robots, etc., these technologies help industry 4.0 to separate rapidly. In contrast, there are some barriers, to implementing Industry 4.0 for example financial constraints, technical competency, organizational restraints. Keywords: industrial revolutions, components of industry 4.0, impacts of industry 4.0, industry 4.0 drivers, barriers of industry 4.0
Industry 4.0 (I4.0) is the fourth industrial revolution and a synonym for intelligent manufacturing. It drives the convergence of several cutting-edge technologies to provoke autonomous, fully integrated, collaborated, highly automated, and customized industries. Edge Computing (EC), a highly distributed framework, emerged a couple of years ago and embraced the industry to leverage the benefit of low latency and near real-time performance. It brings computation and storage in the close proximity of end devices and reduces the cloud overhead. In addition to improved operational efficiency, storage, and latency, EC further reduces the cost, improves productivity with higher quality maintenance and customer satisfaction. At the digital-to-digital stage of the Physical-Digital-Physical (PDP) loop, adapting EC can furnish tremendous benefits and further accelerate the next stages of the loop. This survey identifies the past and present works oriented towards Intelligent Manufacturing integrated with the EC platform and categorizes the research based on architecture, intelligence platform, edge objectives, and application. Herein, the authors have incorporated; (1) The progress in I4.0 following the PDP loop; (2) The discussion on EC in I4.0 and their Research Trend; (3) Methods to bring intelligence to the edge. To the best of our knowledge, it is the first review article that focuses on the applications and objectives of EC in Intelligent Manufacturing. It also outlines the optimum solutions to bring intelligence to the edge by overcoming the resource and complexity-bound with accuracy and latency constraints for the decision-making processes. Future directions include the less explored research areas, challenges in edge deployment in industries, and the integration of trending technologies such as Blockchain, Software Defined Networking, and 5 G with EC to excite the EC researchers. A few collaborative edge scenarios are discussed for the promotion and application of EC in I4.0. Nevertheless, efficient edge deployments face many challenges since studies are still limited to conceptual levels or design steps, and future orientation to application strategies for Smart Manufacturing is required.
Industry 4.0 technologies provide critical perspectives for future innovation and business growth. Technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big data, Machine Learning, and other advanced upcoming technologies are being used to implement Industry 4.0. This paper explores how Industry 4.0 technologies help create a sustainable environment in manufacturing and other industries. Industry 4.0 technologies and the crucial interrelationships through advanced technologies should impact the environment positively. In the age of Industry 4.0, manufacturing is tightly interlinked with information and communication systems, making it more scalable, competitive, and knowledgeable. Industry 4.0 provides a range of principles, instructions, and technology for constructing new and existing factories, enabling consumers to choose different models at production rates with scalable robotics, information, and communications technology. This paper aims to study the significant benefits of Industry 4.0 for sustainable manufacturing and identifies tools and elements of Industry 4.0 for developing environmental sustainability. This literature review-based research is undertaken to identify how Industry 4.0 technologies can help to improve environmental sustainability. It also details the capabilities of Industry 4.0 in dealing with environmental aspects. Twenty major applications of Industry 4.0 to create a sustainable environment are identified and discussed. Thus, it gives a better understanding of the production environment, the supply chains, the delivery chains, and market results. Overall, Industry 4.0 technology seems environmentally sustainable while manufacturing goods with better efficiency and reducing resource consumption.
Industry 4.0 (I4.0) is a fast-evolving area of research, bringing together knowledge from multiple academic fields into creative solutions for manufacturing innovation. Despite the growing amount of published work covering a wide range of I4.0 areas, there has been relatively little research devoted to the organisational side of implementing I4.0. To address this gap, this systematic review paper used quantitative analysis by text-mining 97 articles from 2015 to 2021. The analysis identified eleven research streams, which were grouped into five levels, namely industry and firm, smart factory, data, human resources and supply chain. The research streams were then comprehensively reviewed and presented. For each stream, the paper presents a number of sub-themes and highlights important findings and areas that may require further development. We discuss three important research avenues in the organisational management literature: (1) the impact of the pandemic on the implementation of I4.0, (2) the tension between value creation and value protection during the implementation of 4.0 and (3) the relevance of a contingency approach during the implementation of I4.0.
Sustainability is one of the key requirements for any business, and continuously changing business requirements and the environment are the main drivers of sustainable business practices. However, there exist gaps in recognizing and aligning these sustainable practices with everyday operational activities. This study’s aim is to explore the current awareness level in business leaders and stakeholders about corporate responsibility toward sustainability and reflects on the obstacles encountered by them in diverse built environments, laying the groundwork for addressing these hurdles and contributing toward the overall sustainable development. The study has two major findings: the analysis revealed that there is a paradigm shift in the understanding of sustainability; in contrast to earlier publications, newer publications focus on technological advancements such as the use of “Green Building Information Management” and “Green Internet of Things” illustrating the real-time implications of sustainable practices. Further analysis found a knowledge gap that exists among business leaders in understanding the concept of sustainable development. This, in essence, poses a burden for corporate leaders to keep up with ongoing technological developments. The study concludes that to achieve sustainable growth, leadership in the corporate environment needs a 360-degree view of sustainability, allowing them to assign equal value to the environment, social, and economic pillars of sustainability to address the current issues leveraging from the state-of-the-art technological innovations.
With intensified market competition, product development procedure is accelerated, requiring rapid product innovation and efficient collaboration between design and manufacturing. However, there still exist information islands, prohibiting integration of product life cycle processes. To address this issue, bionics and digital twin (DT) are combined as a potential solution. The concepts, framework, and features of digital twin bionics (DTB) is originally proposed, with co-evolution mechanism of product-twins (including virtual and physical products) and production-twins (including virtual and physical production) elaborated. A symbiotic co-evolution mechanism is presented to integrate the processes of product development and manufacturing. In detail, the supporting technologies, such as industrial internet of things, cloud edge computing, big data, and artificial intelligence, as well as the potential key technologies, are illustrated. A case study for rapid development of automotive body-in-white in an industrial robotics welding production line is investigated to verify the applicability of the proposed framework. The results suggest that the integration of bionics and DTs can accelerate the innovations and developments of new products and management the process of production construction.
Housing practices are forcing accelerated demand for natural resources and flow-on effects contribute to severe environmental consequences. Transition to a Circular Economy (CE) offers some respite from this mounting pressure. To date, however, efforts to implement a CE have primarily focused on addressing the technical challenges associated with the long lifespan and complexity of buildings, overlooking the domain of housing. Research suggests that new business models should be developed as a precursor to embracing circularity. The Product-Service System (PSS) is one such promising business model. Yet, it is not well understood from the perspective of circular housing, with the existing literature scattered across several research streams. Using an integrative review approach, this paper investigates the concept of PSS and CE within the context of housing to describe their interrelationships and inform an integrated understanding of PSS as it applies to housing in a CE. A conceptual framework is proposed that discusses the PSS as a potentially viable life cycle approach to promote efficiency, longevity, and sufficiency for creating a CE in housing in three levels of energy, material, and space use. This paper further discusses renewed economic and social value proposition of PSS and how a transition to PSS practices may impact the housing industry's organisational arrangement. Finally, this paper critiques the state of the literature on this phenomenon, reveals gaps, and points to directions for future research, practice, and policy development.
In machine learning, two approaches outperform traditional algorithms: ensemble learning and deep learning. The former refers to methods that integrate multiple base models in the same framework to obtain a stronger model that outperforms them. The success of an ensemble method depends on several factors, including how the baseline models are trained and how they are combined. In the literature, there are common approaches to building an ensemble model successfully applied in several domains. On the other hand, deep learning-based models have improved the predictive accuracy of machine learning across a wide range of domains. Despite the diversity of deep learning architectures and their ability to deal with complex problems and the ability to extract features automatically, the main challenge in deep learning is that it requires a lot of expertise and experience to tune the optimal hyper-parameters, which makes it a tedious and time-consuming task. Numerous recent research efforts have been made to approach ensemble learning to deep learning to overcome this challenge. Most of these efforts focus on simple ensemble methods that have some limitations. Hence, this review paper provides comprehensive reviews of the various strategies for ensemble learning, especially in the case of deep learning. Also, it explains in detail the various features or factors that influence the success of ensemble methods. In addition, it presents and accurately categorized several research efforts that used ensemble learning in a wide range of domains.
Networked enterprises (NEs) in the current business are constantly under pressure from stakeholders and government restrictions to encourage ethical and transparent behavior in using natural resources, and their impacts on nearby and global ecosystems, people, and communities. In addition, NEs face vulnerable economical challenges including, market changes, personalized consumer trends, as well as, environmental and social restrictions. In this context, this paper addresses the problem of sustainable NEs vulnerabilities. To do so, a big data analytics-based approach is developed to drive sustainable NEs flexibility and robustness. More specifically, flexibility refers to the network’s ability to respond quickly to changes and risks. While robustness concerns the development of optimum and long-term strategies enabling the network to cope with severe environmental risks and economical costs. Moreover, even if the literature is rich with Big Data models and frameworks developed for sustainable enterprises, there is a real need to scale and extend existing models to cover all sustainability pillars (i.e., social, environmental, and economical) and sustainable value creation (SVC). Accordingly, flexibility and robustness coupling with big data analytics (BDA) levels (i.e. descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics) will enable NEs to grow sustainability in order to create sustainable value. Finally, to demonstrate the applicability of the developed approach, the corporate environmental impact (CEI) database is used to evaluate the sustainable development goals (SDGs) of NEs. The obtained numerical results show the efficiency of our approach.
How can creativity foster sustainability? This is a recently emerging question that sees current approaches to sustainability solutions as lacking the use of creativity. Creativity is thus seen as an aspiration to re-create and discover new paths to accelerate the transitions towards sustainability. We apply an integrative review of the creativity for sustainability literature with an in-depth analysis of 86 journal articles. In addition, we draw on insights from innovation and business studies and from the social sciences on sustainability transitions. The findings indicate four levels of creativity for sustainability including the individual, community, organizational, and institutional levels. On each of these levels, a set of indicators has been identified.
Integrating artificial intelligence (AI) systems into decision-making tasks attempts to assist people by augmenting or complementing their abilities and ultimately improve task performance. However, when considering recommendations from modern “black box” intelligent systems, users are confronted with the decision of accepting or overriding AI’s recommendations. These decisions are even more challenging to make when there exists a significant knowledge imbalance between the users and the AI system—namely, when people lack necessary task knowledge and are therefore unable to accurately complete the task on their own. In this work, we aim to understand people’s behavior in AI-assisted decision-making tasks when faced with the challenge of knowledge imbalance and explore whether involving users in an AI’s prediction generation process makes them more willing to follow the AI’s recommendations and enhances their perception of collaboration. Our empirical study reveals that the involvement of users in generating AI recommendations during a task with notable knowledge imbalance causes them to be more willing to agree with the AI’s suggestions and to perceive the AI agent and their collaboration as a team more positively.
Firms are using blockchain technology to prevent fraudulent activities through consensus mechanisms and help identify data tampering through its underlying characteristics, such as immutability, encryption transparency, and operational resilience. However, a holistic review of blockchain application in building a cyber-secure supply chain remains elusive, which can impede the advancement of the field when competing arguments are made, and pure replications are initiated. Therefore, the present study carries a systematic literature review to assess the state of literature review on the significance of blockchain in developing a cyber-secure and resilient supply chain. A systematic review of 122 peer-reviewed papers published between 2012-2022 has been conducted using a structured methodological approach. Further, the Theory, Methodology and Context (TCM) framework has been applied to suggest avenues for future research. Our analysis reveals six dominant themes related to blockchain and its adoption, blockchain application for resilience, blockchain application for cyber-security, blockchain and its role in intermediation, disintermediation and reintermediation, challenges of blockchain adoption and benefits of blockchain applications in the supply chain. Although the role of blockchain application in the cyber secure and resilient supply chain has been recognised, very little attention has been given to contextual factors affecting its adoption and consequences on key performance parameters. The present study contributes to the literature by synthesising the prime findings of reviewed papers and the managerial implications, thereby giving a holistic view of the existing research and presenting an array of future opportunities for firms to adopt blockchain technology in the supply chain.
Article Classification: Literature Review
Industry 4.0 has become one of the most discussed subjects in academic and professional fields. The number of articles published is large and continues to increase, introducing new issues, concepts, methods, and technologies. Many review articles deal with specific issues and not always with the necessary rigor, making a more general understanding of the subject difficult. Motivated by the large volume of literature, this study makes a tertiary review of Industry 4.0 (i4.0), identifying the main concepts, methods, and technologies. The study is guided by three research questions: What are the literature reviews on i4.0? What are the research questions of these reviews? What are the main results of i4.0 reviews? The reviewed articles are systematic review articles indexed in the Scopus and Web of Science databases. This study presents a descriptive analysis of the review articles (46 articles) and a second descriptive analysis of the references cited in them (1542 articles). In content analysis, we grouped the articles into three classes: conceptual articles, articles on enabling technologies, and articles that address operations management in i4.0. The reviewed articles show the multidisciplinary nature of the topic and the still relative scarcity of studies on its application in companies.
Understanding how consumers categorize a consumer good as eco-friendly is key to facilitating consumers' purchasing of products with lower environmental footprints. Scholarship has increasingly addressed this question. However, most research has examined a single cue that prevents the building of a holistic explanation. An integrative review of studies may provide not only a synthesis of the state of the art but also an overarching integrative theoretical framework that explains what cues consumers use to categorize products as green and the mechanisms guiding the interpretation of these cues. This review of 29 studies examining consumers' assessment of eco-friendliness in consumer goods unearths five cues used as surrogate indicators of eco-friendliness. Nevertheless, these cues are not entirely related to the actual environmental footprint of a product based on the life cycle assessment. Drawing from schema categorization theory, an integrative theoretical framework is presented whereby categorization processes are said to be guided by consumers' lay theories. A research agenda is outlined to stimulate new lines of inquiry around lay theories and product attributes.
The industrial world is undergoing a digitalization process, in which information and communication technologies bring new opportunities for sustainable development. The Digital Twin (DT) technology is mentioned in the literature as one of the main tools to support production and maintenance activities and can effectively contribute to the achievement of sustainability goals. The aim of this study is to investigate, through the results of a systematic literature review, the state-of-the-art and the opportunities that the adoption of DT technology can bring for the realization of sustainable industrial maintenance and production activities. The review results show a growing interest in this field of research, in which, the DT has been applied in different industrial sectors, and considers a variety of maintenance and production activities. Furthermore, this paper investigates how the sustainability issue is addressed by the current DT literature and a list of the sustainability criteria considered with the respective frequency of use was provided. This study reveals that the economic dimension of sustainability is the most considered, followed by the environmental dimension, and lastly by the social dimension. Moreover, the majority of the analysed studies explore few sustainability issues: energy cost and efficiency are the most frequently used criteria in sustainable maintenance and production.
Purpose
Deep learning (DL) technologies assist manufacturers to manage their business operations. This research aims to present state-of-the-art insights on the trends and ways forward for DL applications in manufacturing operations.
Design/methodology/approach
Using bibliometric analysis and the SPAR-4-SLR protocol, this research conducts a systematic literature review to present a scientific mapping of top-tier research on DL applications in manufacturing operations.
Findings
This research discovers and delivers key insights on six knowledge clusters pertaining to DL applications in manufacturing operations: automated system modelling, intelligent fault diagnosis, forecasting, sustainable manufacturing, environmental management, and intelligent scheduling.
Research limitations/implications
This research establishes the important roles of DL in manufacturing operations. However, these insights were derived from top-tier journals only. Therefore, this research does not discount the possibility of the availability of additional insights in alternative outlets, such as conference proceedings, where teasers into emerging and developing concepts may be published.
Originality/value
This research contributes seminal insights into DL applications in manufacturing operations. In this regard, this research is valuable to readers (academic scholars and industry practitioners) interested to gain an understanding of the important roles of DL in manufacturing operations as well as the future of its applications for Industry 4.0, such as Maintenance 4.0, Quality 4.0, Logistics 4.0, Manufacturing 4.0, Sustainability 4.0, and Supply Chain 4.0.
The exponentially growing literature on Industry 4.0 technologies and their implications for supply chains exhibits valuable insights alongside considerable fragmentation. While prior systematic literature reviews (SLRs) started to consolidate the literature, an SLR that simultaneously (a) covers several core technologies of the Industry 4.0, (b) synthesizes their positive and negative implications for supply chain performance in a broad sense, and (c) accounts for the critical success factors that foster or impede these implications is still missing. We contribute to establishing a cumulative body of knowledge by conducting such an SLR. We synthesize 221 articles published on 11 Industry 4.0 technologies between 2005 and 2021. Rather than aggregate implications, our SLR presents the benefits, challenges, and critical success factors of each core technology vis-à-vis supply chain performance individually. We integrate our findings into a framework of Industry 4.0 supply chain performance and derive promising avenues for future research. Specifically, we call for more research on (a) the challenges and critical success factors of Industry 4.0 technologies; (b) hitherto underexplored core technologies of the Industry 4.0; (c) the interaction of multiple core technologies (are they complements or substitutes?); as well as for (d) further consolidation and interdisciplinary dissemination efforts.
This paper takes a historical approach to analyse the Fourth Industrial Revolution, denominated Industry 4.0. Although its technologies are more evolutive than disruptive, their combination and gradual improvement promise significant impacts on the economy and society, thus characterising a veritable revolution. However, the pace of diffusion depends on profit expectation, competition intensity, the regulatory system, financial availability, demand, the labour market, and attitudes towards the new technologies. To enhance understanding of the phenomenon, we describe a framework with three contextual elements of the history of each revolution: technological complementarities, economic institutions, and social structure.
With the ever-increasing demand for personalized product functions, product structure becomes more and more complex. To design a complex engineering product, it involves mechanical, electrical, automation and other relevant fields, which requires a closer multidisciplinary collaborative design (MCD) and integration. However, the traditional design method lacks multidisciplinary coordination, which leads to interaction barriers between design stages and disconnection between product design and prototype manufacturing. To bridge the gap, a novel digital twin-enabled MCD approach is proposed. Firstly, the paper explores how to converge the MCD into the digital design process of complex engineering products in a cyber-physical system manner. The multidisciplinary collaborative design is divided into three parts: multidisciplinary knowledge collaboration, multidisciplinary collaborative modeling and multidisciplinary collaborative simulation, and the realization methods are proposed for each part. To be able to describe the complex product in a virtual environment, a systematic MCD framework based on the digital twin is further constructed. Integrate multidisciplinary collaboration into three stages: conceptual design, detailed design and virtual verification. The ability to verify and revise problems arising from multidisciplinary fusions in real-time minimizes the number of iterations and costs in the design process. Meanwhile, it provides a reference value for complex product design. Finally, a design case of an automatic cutting machine is conducted to reveal the feasibility and effectiveness of the proposed approach.
The fourth industrial revolution has turned into a reality during the past few years, and, as a result, the related literature has grown at an unprecedented rate, offering valuable insights into the possible impacts of Industry 4.0 at various analysis levels. Investigating the economic effects of Industry 4.0, especially at the corporate level, has been a cutting-edge research topic across various disciplines. Similarly, several studies have addressed the opportunities that Industry 4.0 might offer to environmental sustainability. On the contrary, the social sustainability implications of Industry 4.0 are less explored in the literature. Unlike the overoptimism around the economic benefits, academia remains quite inconsistent while interpreting the social aspects linked to Industry 4.0. Trying to shed some light on this issue, this research conducts a state-of-the-art systematic review of academic papers and a Machine Learning-based analysis of grey literature on the social implications of Industry 4.0. Contributing to this very relevant and fresh topic, the study summarizes the ongoing trends on social sustainability consequences of Industry 4.0, highlights the existing gaps, and proposes exciting avenues for future research.
Despite the increasing academic interest in Industry 4.0, only a minority of studies has examined the antecedents and outcomes of sustainability-oriented Industry 4.0 initiatives. In this paper, the authors investigate how sustainability drivers might be taken into account in Industry 4.0 technologies' implementation. Industry 4.0 sustainability drivers and externalities are identified based on a comprehensive review of the literature and categorized based on experts’ surveys. The authors employ the Best-Worst Method to prioritize the sustainability drivers and externalities. The results highlight the role of management support and commitment in sustainability-oriented Industry 4.0 initiatives and underline their economic and socio-environmental externalities. Based on the findings, the authors develop a self-assessment framework of Industry 4.0 sustainability drivers and externalities with a readiness index for organizations willing to adopt such initiatives. The authors suggest approaching sustainability-oriented Industry 4.0 implementation through three main stages: approach, deployment, and results (externalities). In doing so, this study contributes to knowledge in the Industry 4.0 and sustainability fields by defining the factors influencing both disciplines and by proposing an integrative readiness model.
This study expands the technical approach that dominates the academic literature on Industry 4.0, identifying relevant benefits and challenges for its implementation process, assessing the relevance of sustainability in Industry 4.0 and analyzing its potential social impact in a developing country. Using a survey-based methodology, specialists on Industry 4.0 from multinationals and national companies in the manufacturing sector in Brazil were consulted. The results showed that the increase in the global competitiveness of the companies and the improvement in the quality of the production lines were the most expected benefits; and the difficulty in changing the organizational culture, the high investments and the difficulty in hiring/training people in digital technology were the most cited challenges. Sustainability was considered strategically secondary, with its social dimension little considered. The implication of this study is to draw attention to the problem of unemployment generated by the implementation of Industry 4.0 and the potential social impacts on local society. The originality and theoretical contribution are to encourage researchers and civil society to contribute with theory and practice to the social dimension of sustainability in Industry 4.0, seeking to prevent the aggravation of social inequalities.
Industry 4.0 is transforming the manufacturing industry and the economics of value creation. A great deal of positive hype has built up around the sustainable development implications of Industry 4.0 technologies during the past few years. Expectations regarding the opportunities that Industry 4.0 offers for sustainable manufacturing are significantly high, but the lack of accurate understanding of the process through which Industry 4.0 technologies enable sustainable manufacturing is a fundamental barrier for businesses pursuing digitalization and sustainable thinking. The present study addresses this knowledge gap by developing a roadmap that explains how Industry 4.0 and the underlying digital technologies can be leveraged to support and facilitate the triple bottom line of sustainable manufacturing. To this purpose, the study conducted a systematic literature review and identified 15 sustainability functions through which Industry 4.0 contributes to sustainable manufacturing. Interpretive structural modeling was further applied to identify the relationships that may exist within the sustainability functions. The resulting sustainable manufacturing roadmap explains how, and in which order, various Industry 4.0 sustainability functions contribute to developing the economic, environmental, and social dimensions of sustainability. The resulting implications are expected to serve manufacturers, industrialists, and academia as a strategic guide for leveraging Industry 4.0 digital transformation to support sustainable development.
The Fourth Industrial Revolution (Industry 4.0) leads to mass personalisation as an emerging manufacturing
paradigm. Mass personalisation focuses on uniquely made products to individuals at scale. Global challenges
encourage mass personalisation manufacturing with efficiency competitive to mass production. Driven by
individualisation as a trend and enabled by increasing digitalisation, mass personalisation can go beyond today’s
mass customisation. This paper aims to introduce Mass Personalisation as a Service (MPaaS) to address unique
and complex requirements at scale by harnessing Industry 4.0 technologies, including Internet of Things, Additive
Manufacturing, Big Data, Cloud Manufacturing, Digital Twin, and Blockchain. A case study for the
implementation of MPaaS in personalised face masks is presented. The workforce with constant exposure to
contaminants requires personal protective equipment (PPE), such as facemasks, for longer hours resulting in
pressure-related ulcers. This prolonged use of PPE highlights the importance of personalisation to avoid ulcers
and other related health concerns. Most studies have used Additive Manufacturing for individualisation and
cloud capabilities for large-scale manufacturing. This study develops a framework and mathematical model to
demonstrate the capability of the proposed solution to address one of the most critical challenges by making
personalised face masks as an essential PPE in the critical industrial environment.
In order to support advanced collaborations among smart products, services, users and service providers in a smart product and service ecosystem (S-PSS), this paper proposed a service-oriented hybrid digital twin (DT) and digital thread platform-based approach with embedded crowd-/service-sourcing mechanism for enabling advanced manufacturing services. This approach is well supported by the ecosystem interaction intelligence of digitally connected products, services, users, and service providers via Internet of Beings (IoB) (Things, Users and Service providers). First, driven by industrial application needs in heating industry, a conceptual model of the service-oriented hybrid platform integrated with crowdsourcing mechanism is developed, which supports the concepts of product DT, service DT and human user DT. Second, the key system realization techniques are developed to integrate service crowdsourcing and service recommendation for realizing smart services. Finally, a case study is carried out for evaluating and confirming its feasibility.
As recent ICT-enabled innovations are becoming increasingly popular, there will inevitably be a need for enterprises to create innovative products and services to help improve people’s daily lives. Although current product-service systems (PSS) envision a future in coupling products and services to co-create more satisfying value for both customers and enterprises, most recent studies focus on a product’s service system design in the context of its use phase rather than a methodical support for implementing entire PSS design. This research aims to address the shifting interest of research and practices in Smart PSS from the viewpoint of product design and development (PDD). It presents a TRIZ-based product-service design approach to assist designers/engineers in developing innovative cyber-physical products (CPP) for use by customers. In the context of COVID-19 pandemic, an empirical study concerning a novel face mask design was conducted to verify the applicability and validity of the proposed design approach. The theoretical frameworks and practical implications of the proposed approach are also discussed.